diff --git a/.gitignore b/.gitignore
index 71283b03bcdf9fdf8dce0f393ebc1c299ef5e45c..6ea650685bf06878a3d192de47e5a11bc2c1617d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,7 @@
-.Rproj.user
-.Rhistory
-.RData
-.Ruserdata
-dce_simulation_tool.Rproj
-modeloutput/
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+dce_simulation_tool.Rproj
+modeloutput/
*.html
\ No newline at end of file
diff --git a/Designs/bayefficient.ngd b/Designs/bayefficient.ngd
index 59b463266d7a8b602c94a8f44cb57f0f4dd9ee83..c239636496659d8bd7b792e1791b415a44656db5 100644
--- a/Designs/bayefficient.ngd
+++ b/Designs/bayefficient.ngd
@@ -1,41 +1,41 @@
-Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.zugang alt1.gestaltung alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.zugang alt2.gestaltung alt2.beitrag Block
-1 1 2 2 1 1 0 -2 0 0.6 0.05 0.6 0 0 1 -2 1 0.12 2
-1 2 0.5 0.3 0 0 1 0 0 0.12 0.2 3 1 1 0 -5 1 0.6 3
-1 3 1 1 1 0 1 -5 0 0.6 0.1 1 0 1 0 0 1 0.9 4
-1 4 2 0.3 1 1 0 -5 1 0.12 0.05 3 0 0 1 0 0 1.2 2
-1 5 0.05 0.3 1 0 1 -2 1 0.12 2 3 0 1 0 -2 0 1.2 3
-1 6 0.1 0.6 1 0 0 0 0 0.06 1 3 0 1 1 -5 1 0.06 1
-1 7 0.05 3 1 1 1 0 1 0.6 2 0.3 0 0 0 -5 0 0.9 3
-1 8 0.1 2 0 1 1 0 1 0.12 1 0.6 1 0 0 -5 0 0.6 4
-1 9 2 0.3 1 1 1 -2 0 0.36 0.05 3 0 0 0 -2 1 0.9 4
-1 10 0.5 3 1 1 0 -2 0 0.06 0.1 0.3 0 0 1 -2 1 0.06 4
-1 11 0.05 2 0 1 0 -5 0 0.9 2 0.6 1 0 1 0 1 0.12 4
-1 12 0.5 0.6 1 0 1 -2 0 1.2 0.2 2 0 1 0 -2 1 0.06 1
-1 13 0.2 1 0 1 1 -5 1 1.2 0.5 1 1 0 0 0 0 0.36 3
-1 14 1 2 1 1 1 -2 1 0.06 0.1 0.6 0 0 0 -2 0 1.2 2
-1 15 1 3 0 0 1 -5 1 0.9 0.1 0.3 1 1 0 0 0 0.6 1
-1 16 2 2 1 0 0 0 1 0.6 0.05 0.3 0 1 1 -5 0 0.9 4
-1 17 2 1 0 1 0 0 1 0.36 0.05 1 1 0 1 -5 0 0.6 2
-1 18 0.2 0.6 1 0 1 -2 1 0.9 0.5 2 0 1 0 -2 0 0.36 4
-1 19 0.1 3 1 0 0 0 0 0.9 1 0.3 0 1 1 -5 1 0.36 3
-1 20 0.05 0.3 1 1 0 -5 1 0.36 2 2 0 0 1 0 0 0.9 1
-1 21 0.2 1 0 0 1 -5 1 0.12 0.5 1 1 1 0 0 0 0.9 2
-1 22 0.1 3 0 1 1 -2 0 0.6 1 0.3 1 0 0 -2 1 0.06 3
-1 23 1 3 1 0 1 -5 0 1.2 0.2 0.3 0 1 0 0 1 0.12 1
-1 24 2 0.6 0 0 0 -5 1 0.9 0.05 1 1 1 1 0 0 0.6 4
-1 25 0.5 1 0 1 1 0 0 0.36 0.2 1 1 0 0 -5 1 1.2 2
-1 26 0.2 1 1 1 0 -2 1 1.2 0.5 1 0 0 1 -2 0 0.36 1
-1 27 0.2 0.6 0 1 1 -2 1 0.12 0.5 2 1 0 0 -2 0 0.12 1
-1 28 1 2 0 0 1 0 0 1.2 0.1 0.6 1 1 0 -5 1 0.12 1
-1 29 0.1 0.6 0 1 0 0 0 0.6 1 2 1 0 1 -5 1 0.36 3
-1 30 0.5 0.3 0 0 0 0 0 0.36 0.2 3 1 1 1 -5 1 1.2 2
-1 31 0.05 0.3 0 0 0 -5 0 0.9 2 2 1 1 1 0 1 0.6 3
-1 32 0.1 0.3 1 1 1 -5 0 0.06 1 3 0 0 0 0 1 0.36 3
-1 33 0.5 1 0 0 0 0 1 0.06 0.2 0.6 1 1 1 -5 0 1.2 2
-1 34 1 2 0 1 0 -5 0 0.36 0.1 0.6 1 0 1 0 1 0.06 1
-1 35 0.05 3 1 0 0 -2 1 0.06 2 0.3 0 1 1 -2 0 0.06 2
-1 36 0.2 0.6 0 0 0 -2 1 1.2 0.5 2 1 1 1 -2 0 0.12 4
-||||||||||
+Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.zugang alt1.gestaltung alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.zugang alt2.gestaltung alt2.beitrag Block
+1 1 2 2 1 1 0 -2 0 0.6 0.05 0.6 0 0 1 -2 1 0.12 2
+1 2 0.5 0.3 0 0 1 0 0 0.12 0.2 3 1 1 0 -5 1 0.6 3
+1 3 1 1 1 0 1 -5 0 0.6 0.1 1 0 1 0 0 1 0.9 4
+1 4 2 0.3 1 1 0 -5 1 0.12 0.05 3 0 0 1 0 0 1.2 2
+1 5 0.05 0.3 1 0 1 -2 1 0.12 2 3 0 1 0 -2 0 1.2 3
+1 6 0.1 0.6 1 0 0 0 0 0.06 1 3 0 1 1 -5 1 0.06 1
+1 7 0.05 3 1 1 1 0 1 0.6 2 0.3 0 0 0 -5 0 0.9 3
+1 8 0.1 2 0 1 1 0 1 0.12 1 0.6 1 0 0 -5 0 0.6 4
+1 9 2 0.3 1 1 1 -2 0 0.36 0.05 3 0 0 0 -2 1 0.9 4
+1 10 0.5 3 1 1 0 -2 0 0.06 0.1 0.3 0 0 1 -2 1 0.06 4
+1 11 0.05 2 0 1 0 -5 0 0.9 2 0.6 1 0 1 0 1 0.12 4
+1 12 0.5 0.6 1 0 1 -2 0 1.2 0.2 2 0 1 0 -2 1 0.06 1
+1 13 0.2 1 0 1 1 -5 1 1.2 0.5 1 1 0 0 0 0 0.36 3
+1 14 1 2 1 1 1 -2 1 0.06 0.1 0.6 0 0 0 -2 0 1.2 2
+1 15 1 3 0 0 1 -5 1 0.9 0.1 0.3 1 1 0 0 0 0.6 1
+1 16 2 2 1 0 0 0 1 0.6 0.05 0.3 0 1 1 -5 0 0.9 4
+1 17 2 1 0 1 0 0 1 0.36 0.05 1 1 0 1 -5 0 0.6 2
+1 18 0.2 0.6 1 0 1 -2 1 0.9 0.5 2 0 1 0 -2 0 0.36 4
+1 19 0.1 3 1 0 0 0 0 0.9 1 0.3 0 1 1 -5 1 0.36 3
+1 20 0.05 0.3 1 1 0 -5 1 0.36 2 2 0 0 1 0 0 0.9 1
+1 21 0.2 1 0 0 1 -5 1 0.12 0.5 1 1 1 0 0 0 0.9 2
+1 22 0.1 3 0 1 1 -2 0 0.6 1 0.3 1 0 0 -2 1 0.06 3
+1 23 1 3 1 0 1 -5 0 1.2 0.2 0.3 0 1 0 0 1 0.12 1
+1 24 2 0.6 0 0 0 -5 1 0.9 0.05 1 1 1 1 0 0 0.6 4
+1 25 0.5 1 0 1 1 0 0 0.36 0.2 1 1 0 0 -5 1 1.2 2
+1 26 0.2 1 1 1 0 -2 1 1.2 0.5 1 0 0 1 -2 0 0.36 1
+1 27 0.2 0.6 0 1 1 -2 1 0.12 0.5 2 1 0 0 -2 0 0.12 1
+1 28 1 2 0 0 1 0 0 1.2 0.1 0.6 1 1 0 -5 1 0.12 1
+1 29 0.1 0.6 0 1 0 0 0 0.6 1 2 1 0 1 -5 1 0.36 3
+1 30 0.5 0.3 0 0 0 0 0 0.36 0.2 3 1 1 1 -5 1 1.2 2
+1 31 0.05 0.3 0 0 0 -5 0 0.9 2 2 1 1 1 0 1 0.6 3
+1 32 0.1 0.3 1 1 1 -5 0 0.06 1 3 0 0 0 0 1 0.36 3
+1 33 0.5 1 0 0 0 0 1 0.06 0.2 0.6 1 1 1 -5 0 1.2 2
+1 34 1 2 0 1 0 -5 0 0.36 0.1 0.6 1 0 1 0 1 0.06 1
+1 35 0.05 3 1 0 0 -2 1 0.06 2 0.3 0 1 1 -2 0 0.06 2
+1 36 0.2 0.6 0 0 0 -2 1 1.2 0.5 2 1 1 1 -2 0 0.12 4
+||||||||||
design
;alts = alt1, alt2, alt3
;rows = 36
diff --git a/Designs/efficient.ngd b/Designs/efficient.ngd
index cdbfca151754cf3f68683d416a587bc196c669e2..e5b04f775cb78a129daac0b13794da86fc483f9a 100644
--- a/Designs/efficient.ngd
+++ b/Designs/efficient.ngd
@@ -1,41 +1,41 @@
-Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.zugang alt1.gestaltung alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.zugang alt2.gestaltung alt2.beitrag Block
-1 1 0.2 1 0 0 1 -2 1 1.2 0.5 1 1 1 0 -2 0 0.12 2
-1 2 0.5 3 0 1 1 0 1 0.12 0.2 0.3 1 0 0 -5 0 0.6 4
-1 3 0.05 2 1 1 0 0 1 0.36 2 0.3 0 0 1 -5 0 0.9 2
-1 4 0.5 1 1 0 1 -2 1 0.9 0.2 1 0 1 0 -2 0 0.12 3
-1 5 0.1 2 1 1 1 0 0 0.12 1 0.6 0 0 0 -5 1 0.06 3
-1 6 1 3 0 0 1 -5 0 1.2 0.1 0.3 1 1 0 0 1 0.06 2
-1 7 0.2 0.6 0 0 0 -2 0 0.06 0.5 3 1 1 1 -2 1 0.06 4
-1 8 0.1 1 1 1 0 0 0 0.9 1 1 0 0 1 -5 1 0.36 4
-1 9 2 0.3 1 1 1 -5 0 0.36 0.05 3 0 0 0 0 1 0.9 1
-1 10 2 3 1 0 0 -5 1 0.9 0.05 0.3 0 1 1 0 0 0.36 4
-1 11 0.05 2 0 0 0 -5 0 1.2 2 0.6 1 1 1 0 1 0.12 1
-1 12 0.1 0.3 0 1 0 -5 0 0.06 1 3 1 0 1 0 1 0.06 2
-1 13 0.05 0.3 0 1 1 -5 1 0.36 2 2 1 0 0 0 0 1.2 4
-1 14 0.05 3 0 0 1 0 1 0.6 2 0.3 1 1 0 -5 0 0.6 2
-1 15 1 3 1 1 1 -5 0 0.6 0.1 0.3 0 0 0 0 1 0.06 3
-1 16 2 0.6 0 0 0 -2 1 0.12 0.05 2 1 1 1 -2 0 0.9 3
-1 17 0.2 1 0 0 1 -2 0 0.12 0.5 0.6 1 1 0 -2 1 0.9 3
-1 18 2 1 0 1 0 0 1 0.12 0.05 1 1 0 1 -5 0 1.2 3
-1 19 1 0.6 1 0 1 -5 1 0.6 0.1 1 0 1 0 0 0 0.36 3
-1 20 0.1 0.3 1 0 0 0 1 0.06 1 3 0 1 1 -5 0 1.2 1
-1 21 2 0.3 0 1 0 -5 1 0.12 0.05 2 1 0 1 0 0 0.6 2
-1 22 0.1 1 1 0 1 -5 1 0.36 1 1 0 1 0 0 0 1.2 4
-1 23 0.2 1 0 1 1 -2 1 0.9 0.5 1 1 0 0 -2 0 0.12 1
-1 24 0.5 2 1 0 0 -2 0 0.36 0.1 0.6 0 1 1 -2 1 0.6 2
-1 25 0.5 0.6 0 0 0 0 0 0.9 0.2 2 1 1 1 -5 1 0.12 3
-1 26 1 0.3 1 0 1 0 0 0.06 0.1 3 0 1 0 -5 1 0.9 3
-1 27 2 3 1 1 1 0 0 0.6 0.05 0.3 0 0 0 -5 1 0.6 1
-1 28 0.1 2 0 1 0 -5 0 1.2 1 0.6 1 0 1 0 1 0.06 2
-1 29 1 3 0 0 0 0 0 1.2 0.1 0.3 1 1 1 -5 1 0.36 2
-1 30 0.05 2 1 1 0 -5 1 0.6 2 0.6 0 0 1 0 0 0.6 4
-1 31 0.2 0.6 1 1 0 -2 0 1.2 0.5 2 0 0 1 -2 1 0.12 4
-1 32 0.05 0.3 1 0 1 -2 0 0.36 2 3 0 1 0 -2 1 0.9 1
-1 33 0.2 0.6 0 1 1 -2 1 0.6 0.5 2 1 0 0 -2 0 0.36 1
-1 34 0.5 2 1 1 0 -2 1 0.06 0.2 0.6 0 0 1 -2 0 1.2 1
-1 35 1 0.3 0 1 1 0 0 0.06 0.2 3 1 0 0 -5 1 1.2 4
-1 36 0.5 0.6 1 0 0 -2 1 0.9 0.2 2 0 1 1 -2 0 0.36 1
-||||||||||
+Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.zugang alt1.gestaltung alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.zugang alt2.gestaltung alt2.beitrag Block
+1 1 0.2 1 0 0 1 -2 1 1.2 0.5 1 1 1 0 -2 0 0.12 2
+1 2 0.5 3 0 1 1 0 1 0.12 0.2 0.3 1 0 0 -5 0 0.6 4
+1 3 0.05 2 1 1 0 0 1 0.36 2 0.3 0 0 1 -5 0 0.9 2
+1 4 0.5 1 1 0 1 -2 1 0.9 0.2 1 0 1 0 -2 0 0.12 3
+1 5 0.1 2 1 1 1 0 0 0.12 1 0.6 0 0 0 -5 1 0.06 3
+1 6 1 3 0 0 1 -5 0 1.2 0.1 0.3 1 1 0 0 1 0.06 2
+1 7 0.2 0.6 0 0 0 -2 0 0.06 0.5 3 1 1 1 -2 1 0.06 4
+1 8 0.1 1 1 1 0 0 0 0.9 1 1 0 0 1 -5 1 0.36 4
+1 9 2 0.3 1 1 1 -5 0 0.36 0.05 3 0 0 0 0 1 0.9 1
+1 10 2 3 1 0 0 -5 1 0.9 0.05 0.3 0 1 1 0 0 0.36 4
+1 11 0.05 2 0 0 0 -5 0 1.2 2 0.6 1 1 1 0 1 0.12 1
+1 12 0.1 0.3 0 1 0 -5 0 0.06 1 3 1 0 1 0 1 0.06 2
+1 13 0.05 0.3 0 1 1 -5 1 0.36 2 2 1 0 0 0 0 1.2 4
+1 14 0.05 3 0 0 1 0 1 0.6 2 0.3 1 1 0 -5 0 0.6 2
+1 15 1 3 1 1 1 -5 0 0.6 0.1 0.3 0 0 0 0 1 0.06 3
+1 16 2 0.6 0 0 0 -2 1 0.12 0.05 2 1 1 1 -2 0 0.9 3
+1 17 0.2 1 0 0 1 -2 0 0.12 0.5 0.6 1 1 0 -2 1 0.9 3
+1 18 2 1 0 1 0 0 1 0.12 0.05 1 1 0 1 -5 0 1.2 3
+1 19 1 0.6 1 0 1 -5 1 0.6 0.1 1 0 1 0 0 0 0.36 3
+1 20 0.1 0.3 1 0 0 0 1 0.06 1 3 0 1 1 -5 0 1.2 1
+1 21 2 0.3 0 1 0 -5 1 0.12 0.05 2 1 0 1 0 0 0.6 2
+1 22 0.1 1 1 0 1 -5 1 0.36 1 1 0 1 0 0 0 1.2 4
+1 23 0.2 1 0 1 1 -2 1 0.9 0.5 1 1 0 0 -2 0 0.12 1
+1 24 0.5 2 1 0 0 -2 0 0.36 0.1 0.6 0 1 1 -2 1 0.6 2
+1 25 0.5 0.6 0 0 0 0 0 0.9 0.2 2 1 1 1 -5 1 0.12 3
+1 26 1 0.3 1 0 1 0 0 0.06 0.1 3 0 1 0 -5 1 0.9 3
+1 27 2 3 1 1 1 0 0 0.6 0.05 0.3 0 0 0 -5 1 0.6 1
+1 28 0.1 2 0 1 0 -5 0 1.2 1 0.6 1 0 1 0 1 0.06 2
+1 29 1 3 0 0 0 0 0 1.2 0.1 0.3 1 1 1 -5 1 0.36 2
+1 30 0.05 2 1 1 0 -5 1 0.6 2 0.6 0 0 1 0 0 0.6 4
+1 31 0.2 0.6 1 1 0 -2 0 1.2 0.5 2 0 0 1 -2 1 0.12 4
+1 32 0.05 0.3 1 0 1 -2 0 0.36 2 3 0 1 0 -2 1 0.9 1
+1 33 0.2 0.6 0 1 1 -2 1 0.6 0.5 2 1 0 0 -2 0 0.36 1
+1 34 0.5 2 1 1 0 -2 1 0.06 0.2 0.6 0 0 1 -2 0 1.2 1
+1 35 1 0.3 0 1 1 0 0 0.06 0.2 3 1 0 0 -5 1 1.2 4
+1 36 0.5 0.6 1 0 0 -2 1 0.9 0.2 2 0 1 1 -2 0 0.36 1
+||||||||||
design
;alts = alt1*, alt2*, alt3
;rows = 36
diff --git a/Designsparks/bayeffdes.ngd b/Designsparks/bayeffdes.ngd
index 128675cc8e171861bec0f4eedb48496aae4bd5d0..48f98d106c4735d984226177e64e41061c6b827f 100644
--- a/Designsparks/bayeffdes.ngd
+++ b/Designsparks/bayeffdes.ngd
@@ -1,41 +1,41 @@
-Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.toiletten alt1.spiel alt1.pflegeint alt1.pflegeziele alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.toiletten alt2.spiel alt2.pflegeint alt2.pflegeziele alt2.beitrag Block
-1 1 50 0.3 0 1 1 1 0 2 -1 3.6 7 1 1 0 0 0 1 0 1 0.6 4
-1 2 20 3 0 0 1 1 0 0 1 0.6 20 0.3 1 1 0 0 1 2 -1 3.6 1
-1 3 20 3 0 1 1 1 1 0 0 9 14 1 1 0 0 0 0 1 0 9 4
-1 4 35 1 0 1 0 1 1 0 -1 3.6 3.5 0.6 1 0 1 0 0 2 1 1.2 2
-1 5 50 0.3 1 1 1 1 0 0 1 6 7 1 0 0 0 1 1 1 -1 6 2
-1 6 50 3 1 0 0 1 1 0 -1 0.6 3.5 0.3 0 1 1 0 0 2 1 0.6 4
-1 7 7 3 1 1 1 1 1 2 0 6 35 0.6 0 0 0 0 0 0 0 6 1
-1 8 35 0.6 1 0 0 1 0 1 0 9 7 3 0 1 1 0 1 0 1 0.6 2
-1 9 50 3 1 0 1 0 1 2 -1 1.2 3.5 0.3 0 1 0 1 0 0 1 1.2 2
-1 10 35 0.3 0 1 0 1 1 0 1 6 7 3 1 0 1 0 0 2 -1 0.6 2
-1 11 20 1 0 1 1 0 1 1 0 12 20 1 1 0 1 0 0 1 0 9 1
-1 12 3.5 0.3 1 0 0 0 1 0 -1 0.6 50 3 0 1 1 1 0 2 1 3.6 2
-1 13 14 1 1 1 1 0 0 0 -1 6 20 1 0 0 0 1 1 1 1 12 3
-1 14 7 0.6 0 0 1 1 0 1 0 9 50 3 1 1 0 0 1 1 -1 0.6 3
-1 15 7 2 0 1 0 1 0 0 0 1.2 50 0.3 1 0 1 1 1 2 0 9 3
-1 16 3.5 0.6 1 1 1 1 1 2 1 6 50 0.6 0 0 0 0 0 0 -1 0.6 1
-1 17 14 1 0 0 0 0 1 1 0 12 14 2 1 1 0 0 0 1 0 12 1
-1 18 3.5 0.3 0 1 0 0 0 2 -1 0.6 35 3 1 0 1 1 1 0 1 1.2 1
-1 19 14 0.6 0 0 0 1 1 1 0 9 20 2 1 1 1 0 0 1 0 9 3
-1 20 35 0.3 0 0 1 0 1 0 1 1.2 14 2 1 1 0 1 0 2 0 6 3
-1 21 3.5 3 1 1 0 0 1 2 1 0.6 50 0.3 0 0 1 1 0 0 -1 6 4
-1 22 7 1 1 0 0 1 0 2 -1 6 20 1 0 1 1 0 1 0 1 9 1
-1 23 20 2 1 0 0 0 0 1 0 12 14 2 0 1 1 0 1 1 0 12 4
-1 24 7 0.6 1 0 1 1 1 2 -1 3.6 20 2 0 1 0 1 1 1 0 12 4
-1 25 3.5 2 1 1 1 1 0 0 -1 0.6 50 0.3 0 0 0 0 1 2 1 1.2 2
-1 26 7 0.3 0 1 1 0 1 1 -1 3.6 35 2 1 0 0 1 0 2 1 3.6 4
-1 27 3.5 0.6 0 0 0 1 0 2 1 3.6 35 0.6 1 1 1 0 1 0 -1 3.6 4
-1 28 20 2 0 0 0 0 0 1 0 9 14 0.6 1 1 1 1 1 1 0 12 1
-1 29 35 0.6 1 1 1 0 0 2 1 12 7 3 0 0 0 1 1 1 -1 1.2 4
-1 30 20 1 1 1 0 0 0 1 0 9 14 2 0 0 1 1 0 1 0 12 3
-1 31 35 3 0 1 0 0 1 2 1 1.2 3.5 0.3 1 0 1 1 0 0 -1 3.6 3
-1 32 50 0.3 1 1 0 0 0 0 1 1.2 3.5 0.6 0 0 1 1 1 2 -1 1.2 1
-1 33 14 2 0 0 1 0 0 1 1 1.2 35 0.3 1 1 0 1 1 2 -1 6 3
-1 34 14 2 1 0 0 1 1 1 1 12 7 1 0 1 1 0 0 0 0 9 3
-1 35 14 1 1 0 1 0 1 1 0 12 35 3 0 1 0 1 0 2 -1 6 2
-1 36 50 2 0 0 1 0 0 2 -1 3.6 3.5 0.6 1 1 0 1 1 0 1 3.6 2
-||||||||||
+Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.toiletten alt1.spiel alt1.pflegeint alt1.pflegeziele alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.toiletten alt2.spiel alt2.pflegeint alt2.pflegeziele alt2.beitrag Block
+1 1 50 0.3 0 1 1 1 0 2 -1 3.6 7 1 1 0 0 0 1 0 1 0.6 4
+1 2 20 3 0 0 1 1 0 0 1 0.6 20 0.3 1 1 0 0 1 2 -1 3.6 1
+1 3 20 3 0 1 1 1 1 0 0 9 14 1 1 0 0 0 0 1 0 9 4
+1 4 35 1 0 1 0 1 1 0 -1 3.6 3.5 0.6 1 0 1 0 0 2 1 1.2 2
+1 5 50 0.3 1 1 1 1 0 0 1 6 7 1 0 0 0 1 1 1 -1 6 2
+1 6 50 3 1 0 0 1 1 0 -1 0.6 3.5 0.3 0 1 1 0 0 2 1 0.6 4
+1 7 7 3 1 1 1 1 1 2 0 6 35 0.6 0 0 0 0 0 0 0 6 1
+1 8 35 0.6 1 0 0 1 0 1 0 9 7 3 0 1 1 0 1 0 1 0.6 2
+1 9 50 3 1 0 1 0 1 2 -1 1.2 3.5 0.3 0 1 0 1 0 0 1 1.2 2
+1 10 35 0.3 0 1 0 1 1 0 1 6 7 3 1 0 1 0 0 2 -1 0.6 2
+1 11 20 1 0 1 1 0 1 1 0 12 20 1 1 0 1 0 0 1 0 9 1
+1 12 3.5 0.3 1 0 0 0 1 0 -1 0.6 50 3 0 1 1 1 0 2 1 3.6 2
+1 13 14 1 1 1 1 0 0 0 -1 6 20 1 0 0 0 1 1 1 1 12 3
+1 14 7 0.6 0 0 1 1 0 1 0 9 50 3 1 1 0 0 1 1 -1 0.6 3
+1 15 7 2 0 1 0 1 0 0 0 1.2 50 0.3 1 0 1 1 1 2 0 9 3
+1 16 3.5 0.6 1 1 1 1 1 2 1 6 50 0.6 0 0 0 0 0 0 -1 0.6 1
+1 17 14 1 0 0 0 0 1 1 0 12 14 2 1 1 0 0 0 1 0 12 1
+1 18 3.5 0.3 0 1 0 0 0 2 -1 0.6 35 3 1 0 1 1 1 0 1 1.2 1
+1 19 14 0.6 0 0 0 1 1 1 0 9 20 2 1 1 1 0 0 1 0 9 3
+1 20 35 0.3 0 0 1 0 1 0 1 1.2 14 2 1 1 0 1 0 2 0 6 3
+1 21 3.5 3 1 1 0 0 1 2 1 0.6 50 0.3 0 0 1 1 0 0 -1 6 4
+1 22 7 1 1 0 0 1 0 2 -1 6 20 1 0 1 1 0 1 0 1 9 1
+1 23 20 2 1 0 0 0 0 1 0 12 14 2 0 1 1 0 1 1 0 12 4
+1 24 7 0.6 1 0 1 1 1 2 -1 3.6 20 2 0 1 0 1 1 1 0 12 4
+1 25 3.5 2 1 1 1 1 0 0 -1 0.6 50 0.3 0 0 0 0 1 2 1 1.2 2
+1 26 7 0.3 0 1 1 0 1 1 -1 3.6 35 2 1 0 0 1 0 2 1 3.6 4
+1 27 3.5 0.6 0 0 0 1 0 2 1 3.6 35 0.6 1 1 1 0 1 0 -1 3.6 4
+1 28 20 2 0 0 0 0 0 1 0 9 14 0.6 1 1 1 1 1 1 0 12 1
+1 29 35 0.6 1 1 1 0 0 2 1 12 7 3 0 0 0 1 1 1 -1 1.2 4
+1 30 20 1 1 1 0 0 0 1 0 9 14 2 0 0 1 1 0 1 0 12 3
+1 31 35 3 0 1 0 0 1 2 1 1.2 3.5 0.3 1 0 1 1 0 0 -1 3.6 3
+1 32 50 0.3 1 1 0 0 0 0 1 1.2 3.5 0.6 0 0 1 1 1 2 -1 1.2 1
+1 33 14 2 0 0 1 0 0 1 1 1.2 35 0.3 1 1 0 1 1 2 -1 6 3
+1 34 14 2 1 0 0 1 1 1 1 12 7 1 0 1 1 0 0 0 0 9 3
+1 35 14 1 1 0 1 0 1 1 0 12 35 3 0 1 0 1 0 2 -1 6 2
+1 36 50 2 0 0 1 0 0 2 -1 3.6 3.5 0.6 1 1 0 1 1 0 1 3.6 2
+||||||||||
design
;alts = alt1, alt2, alt3
;rows = 36
diff --git a/Designsparks/effdes.ngd b/Designsparks/effdes.ngd
index 30fa062f818532ebb017b0ecc207d6ac4c9884d4..572ab7d69695125d77bda384c3035146cc9d8c6e 100644
--- a/Designsparks/effdes.ngd
+++ b/Designsparks/effdes.ngd
@@ -1,41 +1,41 @@
-Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.toiletten alt1.spiel alt1.pflegeint alt1.pflegeziele alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.toiletten alt2.spiel alt2.pflegeint alt2.pflegeziele alt2.beitrag Block
-1 1 3.5 3 0 0 1 1 1 2 1 1.2 50 0.3 1 1 0 0 0 0 -1 3.6 3
-1 2 3.5 0.3 1 0 0 1 0 0 -1 0.6 50 3 0 1 1 0 1 2 1 1.2 4
-1 3 20 1 0 1 1 1 1 1 -1 12 14 2 1 0 0 0 0 1 0 9 2
-1 4 35 0.3 1 1 1 1 0 2 1 9 7 3 0 0 0 1 1 0 -1 0.6 3
-1 5 14 1 1 0 0 0 1 1 0 9 14 2 0 1 1 1 0 1 0 9 2
-1 6 7 1 0 1 1 0 0 0 0 6 35 0.6 1 0 0 1 1 2 0 6 3
-1 7 50 0.6 0 1 1 1 1 0 1 6 3.5 0.6 1 0 0 0 0 2 -1 0.6 2
-1 8 20 0.6 1 1 0 1 1 2 -1 6 14 0.6 0 0 1 0 0 0 1 1.2 3
-1 9 50 0.3 0 0 1 0 0 2 -1 1.2 3.5 3 1 1 0 1 1 0 1 0.6 1
-1 10 35 0.3 0 0 0 1 0 2 1 6 7 3 1 1 1 0 1 0 -1 0.6 1
-1 11 14 2 1 0 1 0 0 1 0 9 20 1 0 1 0 1 1 1 0 12 1
-1 12 35 2 1 0 0 1 1 2 0 6 7 0.6 0 1 1 0 0 0 0 0.6 2
-1 13 7 0.6 0 0 0 1 1 0 -1 3.6 35 2 1 1 1 1 0 2 1 6 4
-1 14 3.5 0.3 0 1 0 0 0 2 -1 0.6 50 3 1 0 1 1 1 0 1 3.6 1
-1 15 3.5 3 1 1 1 1 0 2 -1 1.2 50 0.3 0 0 0 0 1 0 1 1.2 3
-1 16 14 2 1 0 0 0 1 1 0 9 20 1 0 1 1 1 0 1 0 12 2
-1 17 7 3 0 1 0 1 1 0 -1 0.6 35 0.3 1 0 1 1 0 2 1 9 1
-1 18 50 0.6 0 0 1 1 1 0 -1 3.6 3.5 0.6 1 1 0 0 0 2 1 1.2 1
-1 19 3.5 0.3 1 1 1 0 1 2 1 3.6 50 2 0 0 0 1 0 0 -1 1.2 1
-1 20 50 3 1 1 0 1 0 2 -1 3.6 3.5 0.3 0 0 1 0 1 1 1 3.6 2
-1 21 20 1 0 1 1 0 1 1 0 12 14 2 1 0 0 0 0 1 0 9 2
-1 22 14 2 0 0 1 1 0 1 0 12 20 1 1 1 0 0 1 1 0 12 3
-1 23 14 2 1 0 0 0 1 1 0 9 20 1 0 1 1 0 0 1 0 12 1
-1 24 20 1 0 1 0 0 0 1 0 9 14 0.6 1 0 1 1 1 0 1 6 4
-1 25 7 0.6 1 0 0 0 1 1 1 6 35 1 0 1 1 1 0 1 -1 9 4
-1 26 50 2 0 0 0 0 0 2 1 1.2 3.5 0.3 1 1 1 1 1 0 -1 3.6 4
-1 27 35 0.6 0 1 0 0 1 0 1 1.2 7 2 1 0 1 1 0 2 -1 6 3
-1 28 50 3 1 1 0 0 0 0 1 0.6 3.5 0.3 0 0 1 1 1 2 -1 3.6 1
-1 29 35 3 1 0 1 0 0 0 -1 0.6 7 0.3 0 1 0 1 1 2 1 6 3
-1 30 35 0.3 1 1 1 0 1 0 -1 3.6 7 3 0 0 0 1 0 2 1 1.2 4
-1 31 7 0.3 1 0 1 1 0 0 1 3.6 35 3 0 1 0 0 1 2 -1 0.6 2
-1 32 20 1 1 1 0 1 1 1 0 12 14 2 0 0 1 0 0 1 0 9 4
-1 33 14 1 1 0 1 0 0 1 0 12 20 1 0 1 0 0 1 1 0 12 3
-1 34 3.5 0.6 0 1 0 1 0 0 1 1.2 50 0.6 1 0 1 0 1 2 -1 3.6 2
-1 35 7 3 0 0 1 0 1 2 1 0.6 35 0.3 1 1 0 1 0 0 -1 6 4
-1 36 20 2 0 1 1 1 0 1 0 12 20 1 1 0 0 0 1 1 0 12 4
-||||||||||
+Design Choice situation alt1.groesse alt1.entfernung alt1.gemeinschaft alt1.kultur alt1.umweltbildung alt1.toiletten alt1.spiel alt1.pflegeint alt1.pflegeziele alt1.beitrag alt2.groesse alt2.entfernung alt2.gemeinschaft alt2.kultur alt2.umweltbildung alt2.toiletten alt2.spiel alt2.pflegeint alt2.pflegeziele alt2.beitrag Block
+1 1 3.5 3 0 0 1 1 1 2 1 1.2 50 0.3 1 1 0 0 0 0 -1 3.6 3
+1 2 3.5 0.3 1 0 0 1 0 0 -1 0.6 50 3 0 1 1 0 1 2 1 1.2 4
+1 3 20 1 0 1 1 1 1 1 -1 12 14 2 1 0 0 0 0 1 0 9 2
+1 4 35 0.3 1 1 1 1 0 2 1 9 7 3 0 0 0 1 1 0 -1 0.6 3
+1 5 14 1 1 0 0 0 1 1 0 9 14 2 0 1 1 1 0 1 0 9 2
+1 6 7 1 0 1 1 0 0 0 0 6 35 0.6 1 0 0 1 1 2 0 6 3
+1 7 50 0.6 0 1 1 1 1 0 1 6 3.5 0.6 1 0 0 0 0 2 -1 0.6 2
+1 8 20 0.6 1 1 0 1 1 2 -1 6 14 0.6 0 0 1 0 0 0 1 1.2 3
+1 9 50 0.3 0 0 1 0 0 2 -1 1.2 3.5 3 1 1 0 1 1 0 1 0.6 1
+1 10 35 0.3 0 0 0 1 0 2 1 6 7 3 1 1 1 0 1 0 -1 0.6 1
+1 11 14 2 1 0 1 0 0 1 0 9 20 1 0 1 0 1 1 1 0 12 1
+1 12 35 2 1 0 0 1 1 2 0 6 7 0.6 0 1 1 0 0 0 0 0.6 2
+1 13 7 0.6 0 0 0 1 1 0 -1 3.6 35 2 1 1 1 1 0 2 1 6 4
+1 14 3.5 0.3 0 1 0 0 0 2 -1 0.6 50 3 1 0 1 1 1 0 1 3.6 1
+1 15 3.5 3 1 1 1 1 0 2 -1 1.2 50 0.3 0 0 0 0 1 0 1 1.2 3
+1 16 14 2 1 0 0 0 1 1 0 9 20 1 0 1 1 1 0 1 0 12 2
+1 17 7 3 0 1 0 1 1 0 -1 0.6 35 0.3 1 0 1 1 0 2 1 9 1
+1 18 50 0.6 0 0 1 1 1 0 -1 3.6 3.5 0.6 1 1 0 0 0 2 1 1.2 1
+1 19 3.5 0.3 1 1 1 0 1 2 1 3.6 50 2 0 0 0 1 0 0 -1 1.2 1
+1 20 50 3 1 1 0 1 0 2 -1 3.6 3.5 0.3 0 0 1 0 1 1 1 3.6 2
+1 21 20 1 0 1 1 0 1 1 0 12 14 2 1 0 0 0 0 1 0 9 2
+1 22 14 2 0 0 1 1 0 1 0 12 20 1 1 1 0 0 1 1 0 12 3
+1 23 14 2 1 0 0 0 1 1 0 9 20 1 0 1 1 0 0 1 0 12 1
+1 24 20 1 0 1 0 0 0 1 0 9 14 0.6 1 0 1 1 1 0 1 6 4
+1 25 7 0.6 1 0 0 0 1 1 1 6 35 1 0 1 1 1 0 1 -1 9 4
+1 26 50 2 0 0 0 0 0 2 1 1.2 3.5 0.3 1 1 1 1 1 0 -1 3.6 4
+1 27 35 0.6 0 1 0 0 1 0 1 1.2 7 2 1 0 1 1 0 2 -1 6 3
+1 28 50 3 1 1 0 0 0 0 1 0.6 3.5 0.3 0 0 1 1 1 2 -1 3.6 1
+1 29 35 3 1 0 1 0 0 0 -1 0.6 7 0.3 0 1 0 1 1 2 1 6 3
+1 30 35 0.3 1 1 1 0 1 0 -1 3.6 7 3 0 0 0 1 0 2 1 1.2 4
+1 31 7 0.3 1 0 1 1 0 0 1 3.6 35 3 0 1 0 0 1 2 -1 0.6 2
+1 32 20 1 1 1 0 1 1 1 0 12 14 2 0 0 1 0 0 1 0 9 4
+1 33 14 1 1 0 1 0 0 1 0 12 20 1 0 1 0 0 1 1 0 12 3
+1 34 3.5 0.6 0 1 0 1 0 0 1 1.2 50 0.6 1 0 1 0 1 2 -1 3.6 2
+1 35 7 3 0 0 1 0 1 2 1 0.6 35 0.3 1 1 0 1 0 0 -1 6 4
+1 36 20 2 0 1 1 1 0 1 0 12 20 1 1 0 0 0 1 1 0 12 4
+||||||||||
design
;alts = alt1*, alt2*, alt3
;rows = 36
diff --git a/SE_DRIVE/auswertungpretest.Rmd b/SE_DRIVE/auswertungpretest.Rmd
new file mode 100644
index 0000000000000000000000000000000000000000..52d101b560d7bcfe8e8f52090dd4ff45ce81c851
--- /dev/null
+++ b/SE_DRIVE/auswertungpretest.Rmd
@@ -0,0 +1,276 @@
+---
+title: "Workshop Drive Project"
+author: " "
+date: "14/08/2023"
+output:
+ bookdown::html_document2
+editor_options:
+ chunk_output_type: console
+---
+
+
+
+
+
+# Prepare Data
+
+
+
+
+
+First, load packages and user written functions.
+
+```{r setup, include=FALSE, warning=FALSE, message=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+
+rm(list = ls())
+
+library("flextable")
+library("dplyr")
+library("evd")
+library("boot")
+library("apollo")
+library("sjlabelled")
+library("readxl")
+library("httr")
+library("ggplot2")
+library("tidyr")
+library("magrittr")
+library("psych")
+library("kableExtra")
+library("tidylog")
+library("texreg")
+library("stringr")
+
+
+
+source("SE_DRIVE/helpfunctions.R")
+
+
+No_draws =1500 # for mixed logit models
+
+```
+
+
+
+
+```{r prep}
+
+
+# read in all datasets and merge
+
+database <- read_excel("SE_DRIVE/data/pretest/drive2_dce_live_drive_dce.xlsx") %>%
+ left_join(read_excel("SE_DRIVE/data/pretest/drive2_dce_live_covariates.xlsx"), by ="RID") #%>%
+
+
+
+dce_dict<- read_excel("SE_DRIVE/data/pretest/drive2_dce_live_drive_dce.xlsx" , sheet = "dictionary")
+cov_dict<- read_excel("SE_DRIVE/data/pretest/drive2_dce_live_covariates.xlsx" , sheet = "dictionary")
+
+
+
+
+ # Merge with time stamp data to identify speeders
+
+
+
+ length(unique(database$RID))
+
+```
+
+
+
+
+# Descriptive Analysis
+We look at the frequencies of attribute levels and correlations between attributes. This should confirm that the design is balanced and orthogonal.
+
+
+
+
+```{r design, eval=TRUE}
+
+table(database$DESIGN_ROW)
+
+ ## For all respondents
+ table(database$a1_x1)
+ table(database$a1_x2)
+ table(database$a1_x3)
+ table(database$a1_x4)
+
+
+ #Only for the first respondents
+ table(database$a1_x1[unique(database$DESIGN_ROW)], useNA = "always")
+ table(database$a1_x2[unique(database$DESIGN_ROW)], useNA = "always")
+ table(database$a1_x3[unique(database$DESIGN_ROW)], useNA = "always")
+ table(database$a1_x4[unique(database$DESIGN_ROW)], useNA = "always")
+
+
+
+ ## Over all observations
+ cor(database[, 6:15])
+
+ ## Only design
+ cor(database[(unique(database$DESIGN_ROW)), 6:15])
+
+
+
+ table(database$pref1)
+
+ #as probabilities
+
+ table(database$pref1)/length(database$pref1)
+
+```
+
+ We plot the choice probabilities conditional on the attribute level. It should confirm the direction and magnitute of our estimates.
+
+
+```{r choiceprobs, eval=TRUE}
+
+ barplot(prop.table(table(database$pref1)), ylim = c(0,1), col=c("cyan1", "cyan3", "darkcyan"))
+
+
+ aggregate(as.numeric(pref1)==1 ~ a1_x1 , data=database , mean)
+ aggregate(as.numeric(pref1)==2 ~ a2_x1 , data=database , mean)
+
+ plot(aggregate(as.numeric(pref1)==1 ~ a1_x1 , data=database , mean), type="l")
+ plot(aggregate(as.numeric(pref1)==2 ~ a2_x1 , data=database , mean), type="l")
+
+
+ plot(aggregate(as.numeric(pref1)==1 ~ a1_x2 , data=database , mean), type="o")
+ plot(aggregate(as.numeric(pref1)==2 ~ a2_x2 , data=database , mean), type="o")
+
+ plot(aggregate(as.numeric(pref1)==1 ~ a1_x3 , data=database , mean), type="o")
+ plot(aggregate(as.numeric(pref1)==2 ~ a2_x3 , data=database , mean), type="o")
+
+ plot(aggregate(as.numeric(pref1)==1 ~ a1_x4 , data=database , mean), type="o")
+ plot(aggregate(as.numeric(pref1)==2 ~ a2_x4 , data=database , mean), type="o")
+
+ plot(aggregate(as.numeric(pref1)==1 ~ a1_x5 , data=database , mean), type="o")
+ plot(aggregate(as.numeric(pref1)==2 ~ a2_x5 , data=database , mean), type="o")
+
+
+
+```
+
+
+The following table shows how often respondents have chosen which alternatives. For example the 151 in column Alt1 means that 151 respondents have chosen alternative 1 only once in all nine choice situations.
+
+```{r individualswitching, eval =TRUE}
+ind_switch <- database %>%
+ group_by(RID, pref1) %>%
+ summarise(n = n()) %>%
+ ungroup %>%
+ group_by(n, pref1) %>%
+ summarise(freq=n()) %>%
+ arrange(pref1) %>%
+ spread(pref1,freq,fill = 0) %>%
+ rename(nchosen=n, Alt1 =2 , Alt2 = 3 )
+
+kable(ind_switch) %>% kable_classic_2(full_width = T)
+```
+</details>
+
+# Logit Models
+
+
+```{r}
+## recode variables
+
+x3 <- dce_dict %>% slice((which(...2=="a1_x3")+1):(which(...2=="a1_x4")-2)) %>%
+ select(-...2, -`Data Dictionary`) %>%
+rename("a1_x3"=...3, a1_LADEZEIT =...4) %>%
+ mutate(across(everything(), as.numeric), a2_LADEZEIT=a1_LADEZEIT)
+
+
+x4 <- dce_dict %>% slice((which(...2=="a1_x4")+1):(which(...2=="a1_x5")-2)) %>%
+ select(-...2, -`Data Dictionary`) %>%
+rename("a1_x4"=...3, a1_WARTEZEIT =...4) %>%
+ mutate(across(everything(), as.numeric), a2_WARTEZEIT=a1_WARTEZEIT)
+
+x5 <- dce_dict %>% slice((which(...2=="a1_x5")+1):(which(...2=="a2_x1")-2)) %>%
+ select(-...2, -`Data Dictionary`) %>%
+rename("a1_x5"=...3, a1_GESAMTZEIT =...4) %>%
+ mutate(across(everything(), as.numeric), a2_GESAMTZEIT=a1_GESAMTZEIT)
+
+
+database <- database %>%
+ left_join(x3 %>% select(-a2_LADEZEIT), by="a1_x3") %>%
+ left_join(x3 %>% select(-a1_LADEZEIT), by=c("a2_x3"="a1_x3")) %>%
+ left_join(x4 %>% select(-a2_WARTEZEIT),by="a1_x4") %>%
+ left_join(x4 %>% select(-a1_WARTEZEIT),by=c("a2_x4"="a1_x4")) %>%
+ left_join(x5 %>% select(-a2_GESAMTZEIT),by="a1_x5") %>%
+ left_join(x5 %>% select(-a1_GESAMTZEIT),by=c("a2_x5"="a1_x5")) %>%
+ mutate(across(ends_with("x1"), ~.x*20))
+
+```
+
+
+
+```{r estimate logitmodels, eval=FALSE}
+
+source("SE_DRIVE/scripts/clogitmodels.R")
+
+
+delfiles <- dir(path="modeloutput/", pattern=".csv" ,full.names = T)
+
+file.remove(delfiles)
+
+```
+
+
+
+
+```{r readinsavedWTPModel, eval=T, results='hide'}
+
+
+delfiles <- dir(path="modeloutput/", pattern="OLD" ,full.names = T,recursive = TRUE)
+
+file.remove(delfiles)
+
+
+
+
+
+
+modelpath <-list.files(path = "modeloutput/WTPSpace/",pattern = "rds$" , full.names = TRUE, recursive = TRUE)
+models_tex <- list()
+models <- list()
+
+
+for (name in modelpath) {
+
+t <- readRDS(name)
+#twtp <- wtp("b_cost",attr = names(t$estimate) , modelname=t) # only relevant if models are not estimated in WTP space
+
+models[[t$apollo_control$modelName]] <-t
+models_tex[[t$apollo_control$modelName]] <-quicktexregapollo(t)
+
+}
+
+rm(t, name, modelpath)
+
+
+
+```
+
+
+# Conditional Logit models
+
+
+
+```{r clogittables, results='asis'}
+
+
+
+
+screenreg(l = models_tex ,
+ custom.model.names = c("Full Sample") , caption = "Results from Pretest Data" , single.row = T ,
+ custom.note = "***p < 0.001; **p < 0.01; *p < 0.05 Standard Errors in Brackets" , center = TRUE, doctype = FALSE )
+
+
+```
+
+
+
+
diff --git a/SE_DRIVE/data/pretest/drive2_dce_live_covariates.xlsx b/SE_DRIVE/data/pretest/drive2_dce_live_covariates.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..4d7bc465d1f96210aa964004b78dc0803a8b15af
Binary files /dev/null and b/SE_DRIVE/data/pretest/drive2_dce_live_covariates.xlsx differ
diff --git a/SE_DRIVE/data/pretest/drive2_dce_live_drive_dce.xlsx b/SE_DRIVE/data/pretest/drive2_dce_live_drive_dce.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..df2002c7a4c39350d2e4e846b3c10d209f7fc2c8
Binary files /dev/null and b/SE_DRIVE/data/pretest/drive2_dce_live_drive_dce.xlsx differ
diff --git a/SE_DRIVE/data/pretest/drive2_dce_live_pages.xlsx b/SE_DRIVE/data/pretest/drive2_dce_live_pages.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..065741fca4018ead1b5f055c35b74490bffa991f
Binary files /dev/null and b/SE_DRIVE/data/pretest/drive2_dce_live_pages.xlsx differ
diff --git a/SE_DRIVE/effdesign_norestr.ngd b/SE_DRIVE/effdesign_norestr.ngd
new file mode 100644
index 0000000000000000000000000000000000000000..76809c97c022268d0b648f639be5bb20030430ab
--- /dev/null
+++ b/SE_DRIVE/effdesign_norestr.ngd
@@ -0,0 +1,138 @@
+Design Choice situation opt1.x1 opt1.x2 opt1.x3 opt2.x1 opt2.x2 opt2.x3 Block
+1 1 60 200 100 80 25 0 10
+1 2 40 25 0 20 200 100 8
+1 3 40 200 100 60 25 0 9
+1 4 60 25 0 20 200 100 9
+1 5 80 25 0 40 200 100 4
+1 6 80 25 100 40 200 0 7
+1 7 40 200 100 60 50 0 6
+1 8 20 200 0 60 25 100 6
+1 9 80 50 0 60 200 100 1
+1 10 20 200 100 40 50 0 5
+1 11 40 25 100 20 200 0 2
+1 12 60 25 100 40 200 0 1
+1 13 20 200 100 80 25 0 1
+1 14 40 25 100 80 200 0 9
+1 15 80 25 100 20 200 0 5
+1 16 20 25 100 60 200 0 3
+1 17 40 200 0 80 50 100 8
+1 18 80 50 0 40 200 100 5
+1 19 60 25 100 80 200 0 6
+1 20 20 200 0 60 50 100 4
+1 21 60 50 0 20 200 100 5
+1 22 60 200 0 40 25 100 2
+1 23 20 25 100 40 200 0 3
+1 24 60 25 25 40 200 100 7
+1 25 80 50 0 20 200 100 10
+1 26 80 50 100 20 200 0 9
+1 27 40 25 25 20 200 100 5
+1 28 60 200 0 60 25 100 7
+1 29 60 200 100 80 25 25 8
+1 30 20 200 25 80 25 100 8
+1 31 80 200 0 20 25 100 3
+1 32 60 200 0 20 50 100 6
+1 33 20 200 100 80 25 25 7
+1 34 60 100 100 20 200 0 6
+1 35 40 50 100 80 200 0 10
+1 36 80 100 100 40 200 0 9
+1 37 40 200 25 80 25 100 4
+1 38 80 25 25 40 200 100 5
+1 39 20 25 100 60 200 25 9
+1 40 60 25 100 20 200 25 8
+1 41 60 25 25 20 200 100 6
+1 42 20 200 0 20 25 100 4
+1 43 80 25 100 80 200 0 2
+1 44 60 25 100 20 50 0 7
+1 45 40 50 0 80 25 100 7
+1 46 20 50 100 80 200 0 4
+1 47 20 100 0 60 25 100 5
+1 48 40 25 50 80 200 0 10
+1 49 40 25 100 40 200 0 6
+1 50 40 100 0 80 25 100 6
+1 51 60 50 100 20 100 0 2
+1 52 20 200 100 80 50 25 2
+1 53 60 200 0 20 25 50 10
+1 54 60 200 0 80 50 100 5
+1 55 40 25 100 80 200 50 7
+1 56 20 200 100 80 100 0 3
+1 57 40 200 0 60 50 100 3
+1 58 60 200 50 20 25 100 4
+1 59 20 25 50 60 200 25 10
+1 60 80 25 0 20 200 50 6
+1 61 40 25 50 80 200 25 9
+1 62 20 200 100 80 25 50 3
+1 63 40 50 100 60 200 0 8
+1 64 20 200 0 80 100 100 6
+1 65 40 200 0 20 50 100 3
+1 66 80 50 25 60 200 100 6
+1 67 80 200 0 60 50 100 3
+1 68 60 200 0 20 25 25 10
+1 69 40 50 25 20 200 100 7
+1 70 80 50 100 20 200 25 1
+1 71 60 200 100 80 100 0 1
+1 72 40 25 25 80 200 0 2
+1 73 60 100 0 40 200 100 1
+1 74 80 25 100 60 200 0 5
+1 75 20 200 50 80 25 100 10
+1 76 60 200 25 40 25 100 10
+1 77 40 100 0 20 200 100 9
+1 78 20 200 0 80 25 50 3
+1 79 20 100 100 80 25 0 2
+1 80 40 200 25 20 25 100 7
+1 81 60 25 100 80 200 25 1
+1 82 40 200 100 60 50 25 2
+1 83 40 200 100 80 50 25 3
+1 84 80 25 100 20 100 0 4
+1 85 40 25 100 80 200 25 7
+1 86 60 100 0 20 200 100 5
+1 87 40 200 25 80 50 100 7
+1 88 80 200 25 20 25 100 8
+1 89 80 100 0 40 200 100 2
+1 90 60 50 100 20 200 25 4
+1 91 40 50 100 80 200 25 3
+1 92 60 50 25 20 200 100 8
+1 93 60 200 25 20 50 100 1
+1 94 80 200 0 80 50 100 10
+1 95 80 200 0 20 100 100 10
+1 96 40 100 0 80 50 100 1
+1 97 40 200 0 40 50 100 1
+1 98 80 25 0 60 200 50 2
+1 99 20 100 0 80 50 100 8
+1 100 60 25 0 20 200 50 1
+1 101 20 200 0 20 50 100 6
+1 102 20 200 0 40 50 100 4
+1 103 20 200 50 80 25 25 2
+1 104 80 200 25 20 50 100 2
+1 105 60 200 0 60 50 100 9
+1 106 40 25 50 20 200 100 1
+1 107 20 50 0 80 25 100 4
+1 108 20 25 100 80 100 0 3
+1 109 20 200 100 60 25 50 10
+1 110 40 25 0 20 200 50 4
+1 111 60 200 0 20 100 100 5
+1 112 80 25 25 20 200 0 8
+1 113 40 100 100 80 200 0 9
+1 114 40 200 100 60 25 50 7
+1 115 80 25 50 20 200 25 9
+1 116 20 25 100 80 200 50 8
+1 117 60 100 100 40 200 0 5
+1 118 20 100 100 80 50 0 8
+1 119 40 200 50 60 25 0 9
+1 120 20 25 50 80 200 0 4
+||||||||||
+design
+;alts = opt1*, opt2*
+;block = 10
+;eff = (mnl, d)
+;alg = mfederov
+;rows = 120
+
+;model:
+U(opt1) = b1[-0.036] * x1[20,40,60,80]
+ + b2[-0.0034] * x2[25,50,100,200]
+ + b3[-0.0049] * x3[0,25,50,100]
+/
+U(opt2) = b1 * x1
+ + b2 * x2
+ + b3 * x3
+$
\ No newline at end of file
diff --git a/SE_DRIVE/helpfunctions.R b/SE_DRIVE/helpfunctions.R
new file mode 100644
index 0000000000000000000000000000000000000000..93acf7898ddcbe031897426759d2581313f0d210
--- /dev/null
+++ b/SE_DRIVE/helpfunctions.R
@@ -0,0 +1,266 @@
+#function to clean the output folders from old files generated automatically with apollo
+
+moveold <- function(){
+ oldfiles <- list.files(pattern = "OLD[1-9]", recursive = TRUE)
+
+
+ for(file in oldfiles) {
+
+ file.rename(from = file, to = paste0("OLD/",gsub("^.*/", "",file)))
+
+ }
+
+}
+
+
+
+
+## This function is used to create WTP values using the delta method.
+
+
+wtp <- function(cost, attr, modelname, mediancost=FALSE) {
+
+ wtp_values =data.frame(wtp =numeric(), robse=numeric() , robt= numeric() )
+ attr <- attr[-which(attr==cost)]
+
+
+ if (mediancost) {cost=paste0("-exp(",cost,")")
+
+ }
+
+ for (a in attr) {
+
+ ex <- paste0(a,"/",cost)
+
+ deltaMethod_settings=list(expression=(temp=ex))
+ #deltaMethod_settings=list(operation="ratio", parName1=a, parName2=cost)
+ wtp_values[which(attr==a),]<- apollo_deltaMethod(modelname, deltaMethod_settings)[,2:4]
+ }
+
+ #names(wtp_values) <- c("Expression" , "wtp" , "robse" , "robt")
+ wtp_values$wtp <- wtp_values$wtp*-1
+ wtp_values$robse <- wtp_values$robse*1
+ wtp_values$robt <- wtp_values$robt*-1
+ wtp_values$pVal <- (1-pnorm((abs(wtp_values$robt))))*2
+
+ rownames(wtp_values) <- attr
+ return(wtp_values)
+
+}
+
+
+#this function uses the wtp function and wraps it for all Classes in a LC model.
+
+wtp_lc <-function(modelname) {
+
+
+ wtpvalues=list()
+
+ for (class in 1:(length(modelname$LL0)-1)) {
+
+
+ clet <- intToUtf8(96+class)
+ coefs<- data.frame(apollo_modelOutput(modelname, modelOutput_settings = list(printPVal=T)))
+ coefs<- coefs[grep(paste0("delta_.*", clet,"$"),x = rownames(coefs), value=TRUE, perl = TRUE), c(1,5:7)]
+
+
+
+
+ print(clet)
+
+ wtpvalues[[paste0("Class ",class)]] <-
+ wtp(paste0("cost_",clet),
+ grep(paste0("^(?=.*_",clet,")(?!.*delta)"), names(modelname$estimate) , value=T, perl = TRUE),modelname = modelname)
+
+colnames(coefs)<- colnames(wtpvalues[[paste0("Class ",class)]])
+
+wtpvalues[[paste0("Class ",class)]] <- rbind(wtpvalues[[paste0("Class ",class)]],coefs)
+
+ # print("this is other")
+ # print(other)
+ # if(class(other) =="numeric") other<-as.data.frame(as.list(other))
+ # colnames(other) <- names(wtpvalues[[paste0("Class ",class)]])
+ # print("this is other after rename")
+ # print(other)
+ # wtpvalues[[paste0("Class ",class)]] <-rbind(wtpvalues[[paste0("Class ",class)]],other)
+ }
+
+ return(wtpvalues)
+
+}
+
+# This function lets us create output tables with texreg with objects from Apollo
+
+
+quicktexregapollo <- function(model =model, wtpest=NULL) {
+
+ modelOutput_settings = list(printPVal=T)
+
+ if (is.null(wtpest)) { estimated <- janitor::clean_names(as.data.frame(apollo_modelOutput(model, modelOutput_settings)))
+ } else{
+ estimated <- wtpest
+ colnames(estimated)<- c("estimate", "rob_s_e", "robt", "p_1_sided_2")
+
+ }
+
+
+ coefnames <- gsub(pattern = "_[a-z]$", "" ,rownames(estimated))
+
+ texout <- createTexreg(coef.names = coefnames , coef = estimated[["estimate"]] , se = estimated[["rob_s_e"]] , pvalues = estimated$p_1_sided_2,
+ gof.names = c("No Observations" , "No Respondents" , "Log Likelihood (Null)" , "Log Likelihood (Converged)") ,
+ gof = c(model[["nObsTot"]] , model[["nIndivs"]], model[["LL0"]][[1]] , model[["LLout"]][[1]] ) ,
+ gof.decimal = c(FALSE,FALSE,TRUE,TRUE)
+ )
+
+
+ return(texout)
+
+}
+
+
+
+
+
+quicktexregapollo_old <- function(model =model) {
+
+ modelOutput_settings = list(printPVal=T)
+
+ estimated <- janitor::clean_names(as.data.frame(apollo_modelOutput(model, modelOutput_settings)))
+
+
+ texout <- createTexreg(coef.names = names(model[["estimate"]]) , coef = model[["estimate"]] , se = model[["robse"]] , pvalues = estimated$p_1_sided_2,
+ gof.names = c("No Observations" , "No Respondents" , "Log Likelihood (Null)" , "Log Likelihood (Converged)") ,
+ gof = c(model[["nObsTot"]] , model[["nIndivs"]], model[["LL0"]][[1]] , model[["LLout"]][[1]] ) ,
+ gof.decimal = c(FALSE,FALSE,TRUE,TRUE)
+ )
+
+
+ return(texout)
+
+}
+
+
+
+
+#function to assign labels for data generated by SurveyEngine.
+
+ass_labels <- function(path, seelab=TRUE, lower=FALSE) {
+
+ labels <- read_excel(path = path, sheet = "dictionary", col_names = TRUE)[-2:-1,2:4] %>% rename( "Variable"=1 , "Labels"=2 , "valuelabels"=3)
+
+
+ if (lower==TRUE) labels$Variable <- tolower(labels$Variable)
+
+
+ covdata <- read_excel(path = path)
+
+
+ ## check if variables are all present
+ #test 1
+
+ cat("Number of variables in dataset: ",length(names(covdata)) ,
+ "\n Number of variables in label set:", length(labels$Variable[!is.na(labels$Variable)]), "\n")
+
+ if (length(names(covdata)) != length(labels$Variable[!is.na(labels$Variable)]) ){
+ cat("\n labels do not match, will be ignored, but make sure this is ok.
+ \n Below are the variables that are problematic. \n \n These variables exist in the dataset but not in the label set:",
+ setdiff(names(covdata) , labels$Variable[!is.na(labels$Variable)]) ,
+ "\n and these exist in the labels and not in the dataset:" ,
+ setdiff(labels$Variable[!is.na(labels$Variable)], names(covdata)))
+ }
+
+ for (vn in names(covdata)) {
+ attr(covdata[[vn]], "label") <- toString(labels[which(labels$Variable==vn),"Labels"])
+ }
+
+
+ labels$Variable <- na.locf(labels$Variable)
+ labels <- labels[!is.na(labels$Labels),]
+
+ X <- base::split(labels, labels$Variable)
+
+ Y=lapply(X, row_to_names , row_number = 1)
+
+
+ for (vn in names(covdata)) {
+
+ if (is.null(attr(covdata[[vn]], "class")) == TRUE) {
+ attr(covdata[[vn]], "class") <- "haven_labelled"
+ }
+
+
+ if (exists(vn,Y)==TRUE && nrow(Y[[vn]])>1 ) {
+
+ if(seelab==TRUE) cat("\n the variable", vn , " will be labelled \n" )
+
+ t <- as.numeric(unlist(Y[[vn]][2]))
+ names(t) <- unlist(Y[[vn]][3])
+ attr(covdata[[vn]] , "labels") <- t
+ }
+ else{ if(seelab==TRUE) cat("\n Attention: the values of the variable", vn , " will NOT be labelled \n" )}
+
+ }
+
+ return(covdata)
+
+}
+
+
+
+# function to split model into different columns for texreg
+
+subcoef <- function(condition, mname){
+
+ sub <- grep(condition,slot(mname,"coef.names"))
+
+ for (ele in c("coef.names","coef","se","pvalues")) {
+ elements<- slot(mname,ele)[sub]
+ slot(mname,ele) <- elements
+ }
+
+ slot(mname,"coef.names")<-gsub(pattern = condition,replacement = "",x =slot(mname,"coef.names") )
+
+ slot(mname,"model.name")<-gsub("_","",condition)
+
+
+ return(mname)
+
+}
+
+#ff<- subcoef(mname=rpl,condition = "mean_")
+
+
+
+remGOF<- function(models){
+
+ gof<- function(m){
+slot(m,"gof.names")<- character(0)
+slot(m,"gof")<- numeric(0)
+slot(m,"gof.decimal")<- logical(0)
+
+return(m)
+}
+
+return(purrr::map(models,gof))
+
+}
+
+#tes <- remGOF(rpl_cols[2:5])
+
+
+#z-test with apollo data
+
+apollo_ztest <- function(model1, model2, hyp=0){
+
+ comp = data.frame(m1par =model1[["estimate"]] ,m2par = model2[["estimate"]] , m1se=model1[["robse"]] , m2se=model2[["robse"]]) %>%
+
+ mutate(diffmean=m1par-m2par , error= sqrt(m1se^2+m2se^2) , z= diffmean/error , p_value=2*pnorm(-abs(z)))
+
+
+ print(comp)
+
+ cat(class(comp))
+
+ return(comp)
+
+}
diff --git a/SE_DRIVE/scripts/clogitbody.R b/SE_DRIVE/scripts/clogitbody.R
new file mode 100644
index 0000000000000000000000000000000000000000..50a6020ad003ec342f1609a10523f1694401629e
--- /dev/null
+++ b/SE_DRIVE/scripts/clogitbody.R
@@ -0,0 +1,60 @@
+
+
+
+apollo_beta=c(asc = 0,
+ preis =0,
+# gesamtzeit = 0
+ ladezeit =0,
+ wartezeit=0
+ )
+
+
+### keine Parameter fix halten
+apollo_fixed = c("asc")
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = preis*(asc -a1_x1 + ladezeit*a1_LADEZEIT + wartezeit*a1_WARTEZEIT)
+ V[['alt2']] = preis*(asc -a2_x1 + ladezeit*a2_LADEZEIT + wartezeit*a2_WARTEZEIT)
+
+ # V[['alt1']] = (asc +preis*a1_x1 + gesamtzeit*a1_GESAMTZEIT)
+ # V[['alt2']] = (asc +preis*a2_x1 + gesamtzeit*a2_GESAMTZEIT)
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2) ,
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = pref1,
+ V = V # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ ### P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+}
+
diff --git a/SE_DRIVE/scripts/clogitmodels.R b/SE_DRIVE/scripts/clogitmodels.R
new file mode 100644
index 0000000000000000000000000000000000000000..657bafef126b92daa0ab9bca49e3dabc18c3494c
--- /dev/null
+++ b/SE_DRIVE/scripts/clogitmodels.R
@@ -0,0 +1,39 @@
+## Estimate clogit model
+
+
+clpath <- "modeloutput/WTPSpace/clogit_wtpspace/"
+
+##### All respondents ####
+
+
+
+apollo_initialise()
+
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="clog_pretest",
+ modelDescr ="Simple MNL model with all respondents",
+ indivID ="RID",
+ nCores=1,
+ outputDirectory = clpath
+)
+
+
+source("scripts/clogitbody.R")
+
+
+clog_allsamples = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=4000, estimationRoutine="bhhh",
+ hessianRoutine="analytic"))
+
+
+
+
+
+apollo_saveOutput(clog_allsamples, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+
diff --git a/SE_DRIVE/scripts/compare_lcmodels_correct.R b/SE_DRIVE/scripts/compare_lcmodels_correct.R
new file mode 100644
index 0000000000000000000000000000000000000000..f018ae6de6c203477e53ca209ce9048e5f4a8b72
--- /dev/null
+++ b/SE_DRIVE/scripts/compare_lcmodels_correct.R
@@ -0,0 +1,46 @@
+
+library(tidyr)
+library(ggplot2)
+library(dplyr)
+
+lcmodels=list()
+
+fitstats <- data.frame(no_classes= integer() ,N= integer(), no_pars = integer(), LL = numeric(), BIC = numeric() , AIC=numeric() , AICc=numeric())
+
+texoutput <- list()
+
+for (i in 2:6){
+
+ t <- readRDS(paste0("modeloutput/lclogit/lc_",i,"class_model.rds") )
+
+ texoutput[[i-1]] <- quicktexregapollo(t)
+
+ n= t$nIndivs
+ k= length(t$fixed)
+ ll=t$LLout[1]
+
+BIC <- -2*ll+k*log(n)
+AIC <- -2*ll + 2*k
+AICc <- AIC +(2*k^2+2*k)/(n-k-1)
+
+
+lcmodels[[paste0(i," Classes")]] <- t
+
+fitstats[i-1,] <- c(i,n,k,ll , BIC, AIC , AICc)
+
+
+}
+
+
+gof_lc_graph <-fitstats %>% select(-c(N,no_pars, LL )) %>%
+ gather(key = "variable", value = "value", -no_classes) %>%
+ggplot(aes(x=no_classes, y=value)) +
+ geom_line(aes(color = variable, linetype = variable))
+
+
+
+
+
+
+gof_lc_graph
+
diff --git a/SE_DRIVE/scripts/function_lc_avg.R b/SE_DRIVE/scripts/function_lc_avg.R
new file mode 100644
index 0000000000000000000000000000000000000000..80f0ae41a0efdaa5209758fa93bfc155e8960b12
--- /dev/null
+++ b/SE_DRIVE/scripts/function_lc_avg.R
@@ -0,0 +1,27 @@
+
+calculate_avg_lc_wtp <- function(lcmodels, lcmodelsWTP, num_classes) {
+ # Extract probability values
+ prob <- numeric(length = num_classes)
+ for (i in 1:num_classes) {
+ prob[i] <- as.numeric(str_extract(lcmodels$componentReport$model$para[[3+i]], "\\d+\\.\\d+"))
+ }
+
+ # Calculate WTP for all attributes
+ num_attributes <- length(lcmodelsWTP[[1]]$wtp)
+ wtp <- numeric(length = num_attributes)
+ for (j in 1:num_attributes) {
+ wtp_att <- 0
+ for (i in 1:num_classes) {
+ wtp_att <- wtp_att + prob[i] * lcmodelsWTP[[paste0("Class ", i)]]$wtp[[j]]
+ }
+ wtp[j] <- wtp_att
+ }
+
+ return(wtp)
+}
+
+
+
+
+
+
diff --git a/SE_DRIVE/scripts/lcmodels/compare_lcmodels.R b/SE_DRIVE/scripts/lcmodels/compare_lcmodels.R
new file mode 100644
index 0000000000000000000000000000000000000000..35f634117d732fd1596a1725594b605433061c06
--- /dev/null
+++ b/SE_DRIVE/scripts/lcmodels/compare_lcmodels.R
@@ -0,0 +1,46 @@
+
+library(tidyr)
+library(ggplot2)
+library(dplyr)
+
+lcmodels=list()
+
+fitstats <- data.frame(no_classes= integer() ,N= integer(), no_pars = integer(), LL = numeric(), BIC = numeric() , AIC=numeric() , AICc=numeric())
+
+texoutput <- list()
+
+for (i in 2:6){
+
+ t <- readRDS(paste0("modeloutput/lclogit/lc_",2,"class_model.rds") )
+
+ texoutput[[i-1]] <- quicktexregapollo(t)
+
+ n= t$nObs
+ k= length(t$fixed)
+ ll=t$LLout[1]
+
+BIC <- -2*ll+k*log(n)
+AIC <- -2*ll + 2*k
+AICc <- AIC +(2*k^2+2*k)/(n-k-1)
+
+
+lcmodels[[paste0(i," Classes")]] <- t
+
+fitstats[i-1,] <- c(i,n,k,ll , BIC, AIC , AICc)
+
+
+}
+
+
+gof_lc_graph <-fitstats %>% select(-c(N,no_pars, LL )) %>%
+ gather(key = "variable", value = "value", -no_classes) %>%
+ggplot(aes(x=no_classes, y=value)) +
+ geom_line(aes(color = variable, linetype = variable))
+
+
+
+
+
+
+gof_lc_graph
+
diff --git a/SE_DRIVE/scripts/lcmodels/lc_body.R b/SE_DRIVE/scripts/lcmodels/lc_body.R
new file mode 100644
index 0000000000000000000000000000000000000000..158208e7cdc2e1b88f26e1285a15035e03252c5c
--- /dev/null
+++ b/SE_DRIVE/scripts/lcmodels/lc_body.R
@@ -0,0 +1,173 @@
+
+
+rm(list = ls(, pattern = "^apollo_"))
+
+
+
+
+
+database <- database2
+
+
+
+fixednames <- c("asc" , "cost" , "EUBIO" , "BIOVER" ,"REG" , "DE", "ESP", "EIGENT" , "EIGENTxOC","delta" )
+
+
+apollo_initialise()
+
+### Set core controls
+apollo_control = list(
+ modelName =paste0("lc_",n_classes,"class"),
+ modelDescr =paste0(n_classes," Class LCL model for Tomato DCE"),
+ indivID ="RID",
+ nCores = 1,
+ outputDirectory = "modeloutput/lclogit/"
+)
+
+# ################################################################# #
+#### DEFINE MODEL PARAMETERS ####
+# ################################################################# #
+### Vector of parameters, including any that are kept fixed in estimation
+
+beta <-as.vector(t(outer(fixednames, letters[seq_along(1:n_classes)], paste, sep="_")))
+apollo_beta= rep(0,length(beta))
+names(apollo_beta) <-beta
+
+### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta, use apollo_beta_fixed = c() if none
+apollo_fixed = c("delta_a")
+
+# ################################################################# #
+#### DEFINE LATENT CLASS COMPONENTS ####
+# ####################################################
+
+apollo_lcPars=function(apollo_beta, apollo_inputs){
+
+
+ attrib <- fixednames[-grep("delta",fixednames)]
+ lcpars <- list()
+
+ for (f in fixednames) {
+ lcpars[[f]] <- map(seq_along(1:n_classes), ~ get(paste0(f, "_", letters[.])))
+
+ }
+
+ V=list()
+
+ for (c in letters[seq_along(1:n_classes)]) {
+ V[paste0("class_",c)] = get(paste0("delta_",c))
+
+ }
+
+
+ mnl_settings = list(
+ alternatives = set_names(1:n_classes, paste0("class_",letters[1:n_classes])),
+ avail = 1,
+ choiceVar = NA,
+ V = V
+ )
+
+ lcpars[["pi_values"]] = apollo_mnl(mnl_settings, functionality="raw")
+ lcpars[["pi_values"]] = apollo_firstRow(lcpars[["pi_values"]], apollo_inputs)
+
+ return(lcpars)
+
+
+}
+
+
+# ################################################################# #
+#### GROUP AND VALIDATE INPUTS ####
+# ################################################################# #
+
+apollo_inputs = apollo_validateInputs()
+
+# ################################################################# #
+#### DEFINE MODEL AND LIKELIHOOD FUNCTION ####
+# ################################################################# #
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+
+
+ ### Define settings for MNL model component that are generic across classes
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3),
+ avail = 1,
+ choiceVar = pref1
+ )
+
+
+ ### Loop over classes
+
+
+ for (s in seq_along(1:n_classes)) {
+
+ ### Compute class-specific utilities
+ V=list()
+
+
+ # V[['alt1']] = -cost[[s]]*(-a1_x1 +
+ # asc[[s]] +
+ # EUBIO[[s]]*a1_EUBIO + BIOVER[[s]]*a1_BIOVER + REG[[s]]*a1_REG + DE[[s]]*a1_DE + ESP[[s]]*a1_ESP + EIGENT[[s]]*a1_x4 +EIGENTxOC[[s]]*a1_x4*oc)
+ #
+ # V[['alt2']] = -cost[[s]]*(-a2_x1 +
+ # asc[[s]] +
+ # EUBIO[[s]]*a2_EUBIO + BIOVER[[s]]*a2_BIOVER + REG[[s]]*a2_REG + DE[[s]]*a2_DE + ESP[[s]]*a2_ESP + EIGENT[[s]]*a2_x4 +EIGENTxOC[[s]]*a2_x4*oc)
+
+
+
+ V[['alt1']] = cost[[s]]*a1_x1 +
+ asc[[s]] +
+ EUBIO[[s]]*a1_EUBIO + BIOVER[[s]]*a1_BIOVER + REG[[s]]*a1_REG + DE[[s]]*a1_DE + ESP[[s]]*a1_ESP + EIGENT[[s]]*a1_x4 +EIGENTxOC[[s]]*a1_x4*oc
+
+ V[['alt2']] = cost[[s]]*a2_x1 +
+ asc[[s]] +
+ EUBIO[[s]]*a2_EUBIO + BIOVER[[s]]*a2_BIOVER + REG[[s]]*a2_REG + DE[[s]]*a2_DE + ESP[[s]]*a2_ESP + EIGENT[[s]]*a2_x4 +EIGENTxOC[[s]]*a2_x4*oc
+
+
+ V[['alt3']] = 0
+
+
+ mnl_settings$V = V
+ mnl_settings$componentName = paste0("Class_",s)
+
+ ### Compute within-class choice probabilities using MNL model
+ P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality)
+
+
+ }
+
+ ### Compute latent class model probabilities
+ lc_settings = list(inClassProb = P, classProb=pi_values)
+ P[["model"]] = apollo_lc(lc_settings, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+}
+
+
+lc2cl = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(hessianRoutine="maxLik"))
+
+
+lc2cl[["unconditionals"]] <- apollo_lcUnconditionals(lc2cl,apollo_probabilities,apollo_inputs)
+
+apollo_saveOutput(lc2cl, saveOutput_settings = list(saveEst=TRUE, saveCov=FALSE, saveCorr=FALSE))
+
+
+
+
+
diff --git a/SE_DRIVE/scripts/mixedlogit_scriptinteractions.R b/SE_DRIVE/scripts/mixedlogit_scriptinteractions.R
new file mode 100644
index 0000000000000000000000000000000000000000..5bab976c096e18cfa36c8117db0f063fe806456a
--- /dev/null
+++ b/SE_DRIVE/scripts/mixedlogit_scriptinteractions.R
@@ -0,0 +1,161 @@
+## Estimate Mixed logit models
+
+
+mixwtppath <- "modeloutput/WTPSpace/mixlogit_wtpspace/"
+
+##### All respondents ####
+
+database<- database2
+
+apollo_initialise()
+
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_scriptint",
+ modelDescr ="Simple mixedlogit model with script interactions",
+ indivID ="RID",
+ nCores=11,
+ mixing = TRUE, # Mixed Logit
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+
+
+#rm(apollo_inputs)
+
+randnames <- c( "asc", "EUBIO" , "BIOVER" , "REG", "DE", "ESP" , "EIGENT", "cost" )
+fixednames <- c( "ascxInfo2", "EUBIOxInfo2" , "BIOVERxInfo2" , "REGxInfo2", "DExInfo2", "ESPxInfo2" , "EIGENTxInfo2", "ascxInfo3", "EUBIOxInfo3" , "BIOVERxInfo3" , "REGxInfo3", "DExInfo3", "ESPxInfo3" , "EIGENTxInfo3" )
+
+beta <- vector()
+
+for (n in randnames) {
+ beta[paste0("mean_",n)]=0.5
+ beta[paste0("sd_",n)]=1
+}
+
+for (n in fixednames) {
+ beta[n]= 0
+}
+
+
+apollo_beta <-beta
+
+
+draws <- paste0("draws_",randnames)
+### keine Parameter fix halten
+apollo_fixed <- c()
+
+# ################################################################# #
+#### DEFINE RANDOM COMPONENTS ####
+# ################################################################# #
+
+### Set parameters for generating draws
+apollo_draws <- list(
+ interDrawsType = "sobol",
+ interNDraws = No_draws, #50 für Code testen, min. 500 für verlässliche Ergebnisse
+ interUnifDraws = c(),
+ # nur normal distributed: (für cost lognormal transformieren)
+ interNormDraws = draws,
+ # keine Intra-Individuen Heterogenität: (das wären abweichende Präferenzen selbe Individuen zwischen verschiedenen Choices)
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters
+apollo_randCoeff <- function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ # for (normpar in randnames) {
+ #
+ # randcoeff[[normpar]] = get(paste0("mean_",normpar)) + get(paste0("sd_",normpar)) * get(paste0("draws_",normpar))
+ #
+ # }
+
+ randcoeff[["asc"]] = mean_asc + sd_asc * draws_asc
+ randcoeff[["EUBIO"]] = mean_EUBIO + sd_EUBIO * draws_EUBIO
+ randcoeff[["BIOVER"]] = mean_BIOVER + sd_BIOVER * draws_BIOVER
+ randcoeff[["REG"]] = mean_REG + sd_REG * draws_REG
+ randcoeff[["DE"]] = mean_DE + sd_DE * draws_DE
+ randcoeff[["ESP"]] = mean_ESP + sd_ESP * draws_ESP
+ randcoeff[["EIGENT"]] = mean_EIGENT + sd_EIGENT * draws_EIGENT
+ randcoeff[["cost"]] = - exp(mean_cost + sd_cost * draws_cost)
+
+
+ return(randcoeff)
+
+
+}
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = -cost*(-a1_x1 +
+ asc + ascxInfo2*(info_text==2)+ascxInfo3*(info_text==3) +
+ EUBIO*a1_EUBIO + BIOVER*a1_BIOVER + REG*a1_REG + DE*a1_DE + ESP*a1_ESP + EIGENT*a1_x4 +
+ EUBIOxInfo2*a1_EUBIO*(info_text==2) + BIOVERxInfo2*a1_BIOVER*(info_text==2) + REGxInfo2*a1_REG*(info_text==2) + DExInfo2*a1_DE*(info_text==2) + ESPxInfo2*a1_ESP*(info_text==2) + EIGENTxInfo2*a1_x4*(info_text==2) +
+ EUBIOxInfo3*a1_EUBIO*(info_text==3) + BIOVERxInfo3*a1_BIOVER*(info_text==3) + REGxInfo3*a1_REG*(info_text==3) + DExInfo3*a1_DE*(info_text==3) + ESPxInfo3*a1_ESP*(info_text==3) + EIGENTxInfo3*a1_x4*(info_text==3)
+ )
+ V[['alt2']] = -cost*(-a2_x1 +
+ asc+ ascxInfo2*(info_text==2)+ascxInfo3*(info_text==3) +
+ EUBIO*a2_EUBIO + BIOVER*a2_BIOVER + REG*a2_REG + DE*a2_DE + ESP*a2_ESP + EIGENT*a2_x4 +
+ EUBIOxInfo2*a2_EUBIO*(info_text==2) + BIOVERxInfo2*a2_BIOVER*(info_text==2) + REGxInfo2*a2_REG*(info_text==2) + DExInfo2*a2_DE*(info_text==2) + ESPxInfo2*a2_ESP*(info_text==2) + EIGENTxInfo2*a2_x4*(info_text==2) +
+ EUBIOxInfo3*a2_EUBIO*(info_text==3) + BIOVERxInfo3*a2_BIOVER*(info_text==3) + REGxInfo3*a2_REG*(info_text==3) + DExInfo3*a2_DE*(info_text==3) + ESPxInfo3*a2_ESP*(info_text==3) + EIGENTxInfo3*a2_x4*(info_text==3)
+
+ )
+ V[['alt3']] = 0
+
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3) ,
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = pref1,
+ V = V # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+
+ return(P)
+}
+
+
+
+
+mixlog_scriptint = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+apollo_saveOutput(mixlog_scriptint, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
diff --git a/SE_DRIVE/scripts/mixlogitbody.R b/SE_DRIVE/scripts/mixlogitbody.R
new file mode 100644
index 0000000000000000000000000000000000000000..9708c8b1797dfbee6ce210f6067645218e03a190
--- /dev/null
+++ b/SE_DRIVE/scripts/mixlogitbody.R
@@ -0,0 +1,116 @@
+
+
+
+
+randnames <- c( "asc", "EUBIO" , "BIOVER" , "REG", "DE", "ESP" , "EIGENT", "cost" )
+fixednames <- c( "ascwinter" , "EIGENTxOC")
+
+beta <- vector()
+
+for (n in randnames) {
+ beta[paste0("mean_",n)]=0.5
+ beta[paste0("sd_",n)]=1
+}
+
+for (n in fixednames) {
+ beta[n]= 0
+}
+
+apollo_beta <- beta
+draws <- paste0("draws_",randnames)
+### keine Parameter fix halten
+apollo_fixed <- c()
+
+# ################################################################# #
+#### DEFINE RANDOM COMPONENTS ####
+# ################################################################# #
+
+### Set parameters for generating draws
+apollo_draws <- list(
+ interDrawsType = "sobol",
+ interNDraws = No_draws, #50 für Code testen, min. 500 für verlässliche Ergebnisse
+ interUnifDraws = c(),
+ # nur normal distributed: (für cost lognormal transformieren)
+ interNormDraws = draws,
+ # keine Intra-Individuen Heterogenität: (das wären abweichende Präferenzen selbe Individuen zwischen verschiedenen Choices)
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters
+apollo_randCoeff <- function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ # for (normpar in randnames) {
+ #
+ # randcoeff[[normpar]] = get(paste0("mean_",normpar)) + get(paste0("sd_",normpar)) * get(paste0("draws_",normpar))
+ #
+ # }
+
+ randcoeff[["asc"]] = mean_asc + sd_asc * draws_asc
+ randcoeff[["EUBIO"]] = mean_EUBIO + sd_EUBIO * draws_EUBIO
+ randcoeff[["BIOVER"]] = mean_BIOVER + sd_BIOVER * draws_BIOVER
+ randcoeff[["REG"]] = mean_REG + sd_REG * draws_REG
+ randcoeff[["DE"]] = mean_DE + sd_DE * draws_DE
+ randcoeff[["ESP"]] = mean_ESP + sd_ESP * draws_ESP
+ randcoeff[["EIGENT"]] = mean_EIGENT + sd_EIGENT * draws_EIGENT
+ randcoeff[["cost"]] = - exp(mean_cost + sd_cost * draws_cost)
+
+
+ return(randcoeff)
+
+
+}
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = -cost*(-a1_x1 +
+ asc + ascwinter*winter +
+ EUBIO*a1_EUBIO + BIOVER*a1_BIOVER + REG*a1_REG + DE*a1_DE + ESP*a1_ESP + EIGENT*a1_x4 +EIGENTxOC*a1_x4*oc)
+ V[['alt2']] = -cost*(-a2_x1 +
+ asc+ ascwinter*winter +
+ EUBIO*a2_EUBIO + BIOVER*a2_BIOVER + REG*a2_REG + DE*a2_DE + ESP*a2_ESP + EIGENT*a2_x4 +EIGENTxOC*a2_x4*oc)
+ V[['alt3']] = 0
+
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3) ,
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = pref1,
+ V = V # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+
+ return(P)
+}
+
diff --git a/SE_DRIVE/scripts/mixlogitbody_fix_price.R b/SE_DRIVE/scripts/mixlogitbody_fix_price.R
new file mode 100644
index 0000000000000000000000000000000000000000..98d6e4145e7bf6a8b1a719e2df51e17aabbcdf51
--- /dev/null
+++ b/SE_DRIVE/scripts/mixlogitbody_fix_price.R
@@ -0,0 +1,115 @@
+
+
+#rm(apollo_inputs)
+
+randnames <- c( "asc", "EUBIO" , "BIOVER" , "REG", "DE", "ESP" , "EIGENT" )
+fixednames <- c( "ascwinter" , "mean_cost", "EIGENTxOC")
+
+beta <- vector()
+
+for (n in randnames) {
+ beta[paste0("mean_",n)]=0.5
+ beta[paste0("sd_",n)]=1
+}
+
+for (n in fixednames) {
+ beta[n]= 0
+}
+
+apollo_beta <- beta
+draws <- paste0("draws_",randnames)
+### keine Parameter fix halten
+apollo_fixed <- c()
+
+# ################################################################# #
+#### DEFINE RANDOM COMPONENTS ####
+# ################################################################# #
+
+### Set parameters for generating draws
+apollo_draws <- list(
+ interDrawsType = "sobol",
+ interNDraws = No_draws, #50 für Code testen, min. 500 für verlässliche Ergebnisse
+ interUnifDraws = c(),
+ # nur normal distributed: (für cost lognormal transformieren)
+ interNormDraws = draws,
+ # keine Intra-Individuen Heterogenität: (das wären abweichende Präferenzen selbe Individuen zwischen verschiedenen Choices)
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters
+apollo_randCoeff <- function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ # for (normpar in randnames) {
+ #
+ # randcoeff[[normpar]] = get(paste0("mean_",normpar)) + get(paste0("sd_",normpar)) * get(paste0("draws_",normpar))
+ #
+ # }
+
+ randcoeff[["asc"]] = mean_asc + sd_asc * draws_asc
+ randcoeff[["EUBIO"]] = mean_EUBIO + sd_EUBIO * draws_EUBIO
+ randcoeff[["BIOVER"]] = mean_BIOVER + sd_BIOVER * draws_BIOVER
+ randcoeff[["REG"]] = mean_REG + sd_REG * draws_REG
+ randcoeff[["DE"]] = mean_DE + sd_DE * draws_DE
+ randcoeff[["ESP"]] = mean_ESP + sd_ESP * draws_ESP
+ randcoeff[["EIGENT"]] = mean_EIGENT + sd_EIGENT * draws_EIGENT
+
+
+ return(randcoeff)
+
+
+}
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = mean_cost*a1_x1 +
+ asc + ascwinter*winter +
+ EUBIO*a1_EUBIO + BIOVER*a1_BIOVER + REG*a1_REG + DE*a1_DE + ESP*a1_ESP + EIGENT*a1_x4 +EIGENTxOC*a1_x4*oc
+ V[['alt2']] = mean_cost*a2_x1 +
+ asc+ ascwinter*winter +
+ EUBIO*a2_EUBIO + BIOVER*a2_BIOVER + REG*a2_REG + DE*a2_DE + ESP*a2_ESP + EIGENT*a2_x4 +EIGENTxOC*a2_x4*oc
+ V[['alt3']] = 0
+
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3) ,
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = pref1,
+ V = V # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+
+ return(P)
+}
+
diff --git a/SE_DRIVE/scripts/mixlogitbody_interaction_tab4.R b/SE_DRIVE/scripts/mixlogitbody_interaction_tab4.R
new file mode 100644
index 0000000000000000000000000000000000000000..a6f7891da72ae1e4b63587b26e7310495497c527
--- /dev/null
+++ b/SE_DRIVE/scripts/mixlogitbody_interaction_tab4.R
@@ -0,0 +1,118 @@
+
+
+#rm(apollo_inputs)
+
+randnames <- c( "asc", "EUBIO" , "BIOVER" , "REG", "DE", "ESP" , "EIGENT", "cost" )
+fixednames <- c( "ascwinter" , "EIGENTxOC", "EUBIOx", "BIOVERx", "EIGENTx")
+
+beta <- vector()
+
+for (n in randnames) {
+ beta[paste0("mean_",n)]=0.5
+ beta[paste0("sd_",n)]=1
+}
+
+for (n in fixednames) {
+ beta[n]= 0
+}
+
+apollo_beta <- beta
+draws <- paste0("draws_",randnames)
+### keine Parameter fix halten
+apollo_fixed <- c()
+
+# ################################################################# #
+#### DEFINE RANDOM COMPONENTS ####
+# ################################################################# #
+
+### Set parameters for generating draws
+apollo_draws <- list(
+ interDrawsType = "sobol",
+ interNDraws = No_draws, #50 für Code testen, min. 500 für verlässliche Ergebnisse
+ interUnifDraws = c(),
+ # nur normal distributed: (für cost lognormal transformieren)
+ interNormDraws = draws,
+ # keine Intra-Individuen Heterogenität: (das wären abweichende Präferenzen selbe Individuen zwischen verschiedenen Choices)
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters
+apollo_randCoeff <- function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ # for (normpar in randnames) {
+ #
+ # randcoeff[[normpar]] = get(paste0("mean_",normpar)) + get(paste0("sd_",normpar)) * get(paste0("draws_",normpar))
+ #
+ # }
+
+ randcoeff[["asc"]] = mean_asc + sd_asc * draws_asc
+ randcoeff[["EUBIO"]] = mean_EUBIO + sd_EUBIO * draws_EUBIO
+ randcoeff[["BIOVER"]] = mean_BIOVER + sd_BIOVER * draws_BIOVER
+ randcoeff[["REG"]] = mean_REG + sd_REG * draws_REG
+ randcoeff[["DE"]] = mean_DE + sd_DE * draws_DE
+ randcoeff[["ESP"]] = mean_ESP + sd_ESP * draws_ESP
+ randcoeff[["EIGENT"]] = mean_EIGENT + sd_EIGENT * draws_EIGENT
+ randcoeff[["cost"]] = - exp(mean_cost + sd_cost * draws_cost)
+
+
+ return(randcoeff)
+
+
+}
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = -cost*(-a1_x1 +
+ asc + ascwinter*winter +
+ EUBIO*a1_EUBIO + BIOVER*a1_BIOVER + REG*a1_REG + DE*a1_DE + ESP*a1_ESP + EIGENT*a1_x4 +EIGENTxOC*a1_x4*oc +
+ EUBIOx * a1_EUBIO * q15 + BIOVERx * a1_BIOVER * q16 + EIGENTx * a1_x4 * q14)
+ V[['alt2']] = -cost*(-a2_x1 +
+ asc+ ascwinter*winter +
+ EUBIO*a2_EUBIO + BIOVER*a2_BIOVER + REG*a2_REG + DE*a2_DE + ESP*a2_ESP + EIGENT*a2_x4 +EIGENTxOC*a2_x4*oc +
+ EUBIOx * a2_EUBIO * q15 + BIOVERx * a2_BIOVER * q16 + EIGENTx * a2_x4 * q14)
+ V[['alt3']] = 0
+
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3) ,
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = pref1,
+ V = V # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+
+ return(P)
+}
+
diff --git a/SE_DRIVE/scripts/mixlogitbody_old.R b/SE_DRIVE/scripts/mixlogitbody_old.R
new file mode 100644
index 0000000000000000000000000000000000000000..2637727f2ae09cca5ccc1d35d6757c1136ba261b
--- /dev/null
+++ b/SE_DRIVE/scripts/mixlogitbody_old.R
@@ -0,0 +1,140 @@
+
+
+#rm(apollo_inputs)
+
+randnames <- c( "EUBIO" , "BIOVER" , "REG", "DE", "ESP" , "EIGENT", "cost" )
+fixednames <- c("asc" , "ascwinter" , "EIGENTxOC")
+
+beta <- vector()
+
+for (n in randnames) {
+ beta[paste0("mean_",n)]=0.5
+ beta[paste0("sd_",n)]=1
+}
+
+for (n in fixednames) {
+ beta[n]= 0
+}
+
+
+
+apollo_beta=c(asc = 1.2,
+ ascwinter =-0.04,
+ mean_EUBIO = 1,
+ mean_BIOVER = 1.3,
+ mean_REG = 2.5,
+ mean_DE = 2,
+ mean_ESP = 0.5,
+ mean_EIGENT = 0.5,
+ EIGENTxOC = -0.06,
+ sd_EUBIO = 0.5,
+ sd_BIOVER = 0.5,
+ sd_REG= 0.5,
+ sd_DE = 0.5,
+ sd_ESP = 0.5,
+ sd_EIGENT = 0.5,
+ mean_cost = 0.7,
+ sd_cost=0.4
+)
+
+
+
+draws <- paste0("draws_",randnames)
+
+### keine Parameter fix halten
+apollo_fixed <- c()
+
+
+# ################################################################# #
+#### DEFINE RANDOM COMPONENTS ####
+# ################################################################# #
+
+### Set parameters for generating draws
+apollo_draws <- list(
+ interDrawsType = "sobol",
+ interNDraws = No_draws, #50 für Code testen, min. 500 für verlässliche Ergebnisse
+ interUnifDraws = c(),
+ # nur normal distributed: (für cost lognormal transformieren)
+ interNormDraws = draws,
+ # keine Intra-Individuen Heterogenität: (das wären abweichende Präferenzen selbe Individuen zwischen verschiedenen Choices)
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters
+apollo_randCoeff <- function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+
+ # for (normpar in randnames) {
+ #
+ # randcoeff[[normpar]] = get(paste0("mean_",normpar)) + get(paste0("sd_",normpar)) * get(paste0("draws_",normpar))
+ #
+ # }
+
+ #
+ randcoeff[["EUBIO"]] = mean_EUBIO + sd_EUBIO * draws_EUBIO
+ randcoeff[["BIOVER"]] = mean_BIOVER + sd_BIOVER * draws_BIOVER
+ randcoeff[["REG"]] = mean_REG + sd_REG * draws_REG
+ randcoeff[["DE"]] = mean_DE + sd_DE * draws_DE
+ randcoeff[["ESP"]] = mean_ESP + sd_ESP * draws_ESP
+ randcoeff[["EIGENT"]] = mean_EIGENT + sd_EIGENT * draws_EIGENT
+ randcoeff[["cost"]] = - exp(mean_cost + sd_cost * draws_cost)
+
+
+ return(randcoeff)
+
+
+}
+
+
+
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = -cost*(-a1_x1 +asc+ ascwinter*winter + EUBIO*a1_EUBIO + BIOVER*a1_BIOVER + REG*a2_REG + DE*a1_DE + ESP*a1_ESP + EIGENT*a1_x4 +EIGENTxOC*a1_x4*oc)
+ V[['alt2']] = -cost*(-a2_x1 + asc+ ascwinter*winter + EUBIO*a2_EUBIO + BIOVER*a2_BIOVER + REG*a2_REG + DE*a2_DE + ESP*a2_ESP + EIGENT*a2_x4 +EIGENTxOC*a2_x4*oc)
+ V[['alt3']] = 0
+
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3) ,
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = pref1,
+ V = V # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+
+ return(P)
+}
+
diff --git a/SE_DRIVE/scripts/mixlogitbody_pref_space.R b/SE_DRIVE/scripts/mixlogitbody_pref_space.R
new file mode 100644
index 0000000000000000000000000000000000000000..5fcef5ae9007a3bfe631022734640ec3b8316181
--- /dev/null
+++ b/SE_DRIVE/scripts/mixlogitbody_pref_space.R
@@ -0,0 +1,116 @@
+
+
+#rm(apollo_inputs)
+
+randnames <- c( "asc", "EUBIO" , "BIOVER" , "REG", "DE", "ESP" , "EIGENT", "cost" )
+fixednames <- c( "ascwinter" , "EIGENTxOC")
+
+beta <- vector()
+
+for (n in randnames) {
+ beta[paste0("mean_",n)]=0.5
+ beta[paste0("sd_",n)]=1
+}
+
+for (n in fixednames) {
+ beta[n]= 0
+}
+
+apollo_beta <- beta
+draws <- paste0("draws_",randnames)
+### keine Parameter fix halten
+apollo_fixed <- c()
+
+# ################################################################# #
+#### DEFINE RANDOM COMPONENTS ####
+# ################################################################# #
+
+### Set parameters for generating draws
+apollo_draws <- list(
+ interDrawsType = "sobol",
+ interNDraws = No_draws, #50 für Code testen, min. 500 für verlässliche Ergebnisse
+ interUnifDraws = c(),
+ # nur normal distributed: (für cost lognormal transformieren)
+ interNormDraws = draws,
+ # keine Intra-Individuen Heterogenität: (das wären abweichende Präferenzen selbe Individuen zwischen verschiedenen Choices)
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters
+apollo_randCoeff <- function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ # for (normpar in randnames) {
+ #
+ # randcoeff[[normpar]] = get(paste0("mean_",normpar)) + get(paste0("sd_",normpar)) * get(paste0("draws_",normpar))
+ #
+ # }
+
+ randcoeff[["asc"]] = mean_asc + sd_asc * draws_asc
+ randcoeff[["EUBIO"]] = mean_EUBIO + sd_EUBIO * draws_EUBIO
+ randcoeff[["BIOVER"]] = mean_BIOVER + sd_BIOVER * draws_BIOVER
+ randcoeff[["REG"]] = mean_REG + sd_REG * draws_REG
+ randcoeff[["DE"]] = mean_DE + sd_DE * draws_DE
+ randcoeff[["ESP"]] = mean_ESP + sd_ESP * draws_ESP
+ randcoeff[["EIGENT"]] = mean_EIGENT + sd_EIGENT * draws_EIGENT
+ randcoeff[["cost"]] = - exp(mean_cost + sd_cost * draws_cost)
+
+
+ return(randcoeff)
+
+
+}
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = a1_x1*cost +
+ asc + ascwinter*winter +
+ EUBIO*a1_EUBIO + BIOVER*a1_BIOVER + REG*a1_REG + DE*a1_DE + ESP*a1_ESP + EIGENT*a1_x4 +EIGENTxOC*a1_x4*oc
+ V[['alt2']] = a2_x1*cost +
+ asc+ ascwinter*winter +
+ EUBIO*a2_EUBIO + BIOVER*a2_BIOVER + REG*a2_REG + DE*a2_DE + ESP*a2_ESP + EIGENT*a2_x4 +EIGENTxOC*a2_x4*oc
+ V[['alt3']] = 0
+
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3) ,
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = pref1,
+ V = V # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+
+ return(P)
+}
+
diff --git a/SE_DRIVE/scripts/mixlogitbody_season.R b/SE_DRIVE/scripts/mixlogitbody_season.R
new file mode 100644
index 0000000000000000000000000000000000000000..b2e47b242466e1ea080775ddfc9e0aa01f73aad4
--- /dev/null
+++ b/SE_DRIVE/scripts/mixlogitbody_season.R
@@ -0,0 +1,116 @@
+
+
+#rm(apollo_inputs)
+
+randnames <- c( "asc", "EUBIO" , "BIOVER" , "REG", "DE", "ESP" , "EIGENT", "cost" )
+fixednames <- c("EIGENTxOC")
+
+beta <- vector()
+
+for (n in randnames) {
+ beta[paste0("mean_",n)]=0.5
+ beta[paste0("sd_",n)]=1
+}
+
+for (n in fixednames) {
+ beta[n]= 0
+}
+
+apollo_beta <- beta
+draws <- paste0("draws_",randnames)
+### keine Parameter fix halten
+apollo_fixed <- c()
+
+# ################################################################# #
+#### DEFINE RANDOM COMPONENTS ####
+# ################################################################# #
+
+### Set parameters for generating draws
+apollo_draws <- list(
+ interDrawsType = "sobol",
+ interNDraws = No_draws, #50 für Code testen, min. 500 für verlässliche Ergebnisse
+ interUnifDraws = c(),
+ # nur normal distributed: (für cost lognormal transformieren)
+ interNormDraws = draws,
+ # keine Intra-Individuen Heterogenität: (das wären abweichende Präferenzen selbe Individuen zwischen verschiedenen Choices)
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters
+apollo_randCoeff <- function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ # for (normpar in randnames) {
+ #
+ # randcoeff[[normpar]] = get(paste0("mean_",normpar)) + get(paste0("sd_",normpar)) * get(paste0("draws_",normpar))
+ #
+ # }
+
+ randcoeff[["asc"]] = mean_asc + sd_asc * draws_asc
+ randcoeff[["EUBIO"]] = mean_EUBIO + sd_EUBIO * draws_EUBIO
+ randcoeff[["BIOVER"]] = mean_BIOVER + sd_BIOVER * draws_BIOVER
+ randcoeff[["REG"]] = mean_REG + sd_REG * draws_REG
+ randcoeff[["DE"]] = mean_DE + sd_DE * draws_DE
+ randcoeff[["ESP"]] = mean_ESP + sd_ESP * draws_ESP
+ randcoeff[["EIGENT"]] = mean_EIGENT + sd_EIGENT * draws_EIGENT
+ randcoeff[["cost"]] = - exp(mean_cost + sd_cost * draws_cost)
+
+
+ return(randcoeff)
+
+
+}
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = -cost*(-a1_x1 +
+ asc +
+ EUBIO*a1_EUBIO + BIOVER*a1_BIOVER + REG*a1_REG + DE*a1_DE + ESP*a1_ESP + EIGENT*a1_x4 +EIGENTxOC*a1_x4*oc)
+ V[['alt2']] = -cost*(-a2_x1 +
+ asc +
+ EUBIO*a2_EUBIO + BIOVER*a2_BIOVER + REG*a2_REG + DE*a2_DE + ESP*a2_ESP + EIGENT*a2_x4 +EIGENTxOC*a2_x4*oc)
+ V[['alt3']] = 0
+
+
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3) ,
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = pref1,
+ V = V # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+
+ return(P)
+}
+
diff --git a/SE_DRIVE/scripts/mixlogitmodels.R b/SE_DRIVE/scripts/mixlogitmodels.R
new file mode 100644
index 0000000000000000000000000000000000000000..4b4b0c053e660dbfdbf28c605db08d26025d997d
--- /dev/null
+++ b/SE_DRIVE/scripts/mixlogitmodels.R
@@ -0,0 +1,370 @@
+## Estimate Mixed logit models
+
+mixwtppath <- "modeloutput/WTPSpace/mixlogit_wtpspace/"
+mixprefpath <-"modeloutput/mixlogit_prefspace/"
+
+dir.create("modeloutput/Individual_wtp")
+
+## change sign of cost
+
+# database2 <- database2 %>%
+# mutate(across(ends_with("x1"), ~abs(.x)*(-1)))
+
+##### All respondents ####
+
+database<- database2
+
+apollo_initialise()
+
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_allsamples",
+ modelDescr ="Simple mixedlogit model with all respondents",
+ indivID ="RID",
+ nCores=11,
+ mixing = TRUE, # Mixed Logit
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+source("scripts/mixlogitbody.R")
+
+
+mixlog_allsamples = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+apollo_saveOutput(mixlog_allsamples, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+#### Split1 No Text####
+apollo_initialise()
+
+
+database<-database2[which(database2$info_text==1),]
+
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_split_notext",
+ modelDescr ="Simple mixedlogit model split samples No Text",
+ indivID ="RID",
+ nCores=11,
+ mixing = TRUE, # Mixed Logit
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+
+source("scripts/mixlogitbody.R")
+
+mixlog_split_notext = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+apollo_saveOutput(mixlog_split_notext, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+individualWTP_notext <-apollo_conditionals(mixlog_split_notext, apollo_probabilities, apollo_inputs) #derive individual WTP values
+
+saveRDS(individualWTP_notext, file = "modeloutput/Individual_wtp/individualWTP_notext.rds")
+
+
+
+#### Split2 Environment ####
+
+
+
+database<-database2[which(database2$info_text==2),]
+
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_split_env",
+ modelDescr ="Simple mixedlogit model split samples Environment",
+ indivID ="RID",
+ nCores=11,
+ mixing = TRUE, # Mixed Logit
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+source("scripts/mixlogitbody.R")
+
+mixlog_split_env = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_split_env, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+individualWTP_env <-apollo_conditionals(mixlog_split_env, apollo_probabilities, apollo_inputs) #derive individual WTP values
+
+saveRDS(individualWTP_env, file = "modeloutput/Individual_wtp/individualWTP_env.rds")
+
+##### Split 3 ####
+
+
+
+
+
+database<-database2[which(database2$info_text==3),]
+
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_split_ind",
+ modelDescr ="Simple mixedlogit model pilot split samples Industry",
+ indivID ="RID", mixing = TRUE, # Mixed Logit
+ nCores=11,
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+source("scripts/mixlogitbody.R")
+
+mixlog_split_ind = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_split_ind, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+
+individualWTP_ind <-apollo_conditionals(mixlog_split_ind , apollo_probabilities, apollo_inputs) #derive individual WTP values
+
+saveRDS(individualWTP_ind, file = "modeloutput/Individual_wtp/individualWTP_ind.rds")
+
+##### Revisions #####
+
+### Split Sample summer/winter ###
+
+### Estimate model for Summer
+
+
+database<-database2[which(database2$jahreszeit_string=="Sommer"),]
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_season_summer",
+ modelDescr ="Simple mixedlogit model split summer",
+ indivID ="RID", mixing = TRUE, # Mixed Logit
+ nCores=11,
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+source("scripts/mixlogitbody_season.R")
+
+mixlog_season_summer = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_season_summer, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+### Estimate model for Winter
+
+
+database<-database2[which(database2$jahreszeit_string=="Winter"),]
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_season_winter",
+ modelDescr ="Simple mixedlogit model split winter",
+ indivID ="RID", mixing = TRUE, # Mixed Logit
+ nCores=11,
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+source("scripts/mixlogitbody_season.R")
+
+mixlog_season_winter = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_season_winter, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+### Split samples for different tomato types ###
+
+### Cocktail- / Cherrytomatos / Small tomatos
+
+database<-database2[which(database2$q3_new==1),]
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_small_tomato",
+ modelDescr ="Simple mixedlogit model small tomato",
+ indivID ="RID", mixing = TRUE, # Mixed Logit
+ nCores=11,
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+source("scripts/mixlogitbody.R")
+
+mixlog_small_tomato = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_small_tomato, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+### Big tomato's
+
+database<-database2[which(database2$q3_new==2),]
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_big_tomato",
+ modelDescr ="Simple mixedlogit model big tomato",
+ indivID ="RID", mixing = TRUE, # Mixed Logit
+ nCores=11,
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+source("scripts/mixlogitbody.R")
+
+mixlog_big_tomato = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_big_tomato, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+
+##### Estimate standard model in preference space #####
+
+database<-database2
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_pref_space",
+ modelDescr ="Simple mixedlogit model preference space",
+ indivID ="RID", mixing = TRUE, # Mixed Logit
+ nCores=11,
+ HB=FALSE,
+ outputDirectory = mixprefpath
+)
+
+
+source("scripts/mixlogitbody_pref_space.R")
+
+mixlog_pref_space = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_pref_space, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+
+##### Estimate standard model in preference space with fixed price
+
+
+database<-database2
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_fix_price",
+ modelDescr ="Simple mixedlogit model fix price",
+ indivID ="RID", mixing = TRUE, # Mixed Logit
+ nCores=11,
+ HB=FALSE,
+ outputDirectory = mixprefpath
+)
+
+
+source("scripts/mixlogitbody_fix_price.R")
+
+mixlog_fix_price = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_fix_price, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
+
+##### Estimate mxl model with interaction with table 4
+
+database<-database2 %>% filter(q14 < 4 & q15 < 4 & q16 < 4) #exlude don't knows
+
+modelOutput_settings = list(printPVal=T)
+
+### Set core controls
+apollo_control = list(
+ modelName ="mixlog_int_tab4",
+ modelDescr ="Simple mixedlogit model interactions table 4",
+ indivID ="RID", mixing = TRUE, # Mixed Logit
+ nCores=11,
+ HB=FALSE,
+ outputDirectory = mixwtppath
+)
+
+
+source("scripts/mixlogitbody_interaction_tab4.R")
+
+mixlog_int_tab4 = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+
+apollo_saveOutput(mixlog_int_tab4, saveOutput_settings = list(saveCov=FALSE, saveCorr=FALSE))
\ No newline at end of file
diff --git a/designfiles/seagri_designs.ngs b/designfiles/seagri_designs.ngs
index e18dd076dcae6687c6f3308ac58cb3a89fe17586..13b58947334e4573222f7e4732cc5c7c278e4680 100644
--- a/designfiles/seagri_designs.ngs
+++ b/designfiles/seagri_designs.ngs
@@ -1,457 +1,457 @@
-
-? Orthogonal design
-
-design
-;alts = alt1, alt2, alt3
-;rows = 24
-;block = 4
-
-;orth = seq
-
-
-;con
-;model:
-
-
-U(alt1) = b0 + b1 * Initiator[1,2,0] + b2 * Funding[1,2,0] + b3 * Damage [0,1] + b4 * Compensation[2,5,10,15,20,30] /
-U(alt2) = b0 + b1 * Initiator[1,2,0] + b2 * Funding[1,2,0] + b3 * Damage [0,1] + b4 * Compensation[2,5,10,15,20,30]
-
-;formatTitle = 'Scenario <scenarionumber>'
-;formatTableDimensions = 3, 6
-;formatTable:
-1,1 = '' /
-1,2 = 'Initiative to join the scheme' /
-1,3 = 'Source of funding for the compensation' /
-1,4 = 'Impact of forest damage on the carbon amount' /
-1,5 = 'Amount of carbon compensation ' /
-1,6 = 'Choice question&:' /
-2,1 = 'alt1' /
-2,2 = '<alt1.initiator>' /
-2,3 = '<alt1.funding>' /
-2,4 = '<alt1.damage>' /
-2,5 = '<alt1.compensation>' /
-2,6 = '' /
-3,1 = 'alt2' /
-3,2 = '<alt2.initiator>' /
-3,3 = '<alt2.funding>' /
-3,4 = '<alt2.damage>' /
-3,5 = '<alt2.compensation>' /
-3,6 = ''
-;formatTableStyle:
-1,1 = 'default' /
-1,2 = 'headingattribute' /
-1,3 = 'headingattribute' /
-1,4 = 'headingattribute' /
-1,5 = 'headingattribute' /
-1,6 = 'headingattribute' /
-2,1 = 'heading1' /
-2,2 = 'body1' /
-2,3 = 'body1' /
-2,4 = 'body1' /
-2,5 = 'body1' /
-2,6 = 'choice1' /
-3,1 = 'heading2' /
-3,2 = 'body2' /
-3,3 = 'body2' /
-3,4 = 'body2' /
-3,5 = 'body2' /
-3,6 = 'choice2'
-;formatStyleSheet = Default.css
-;formatAttributes:
-alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
-alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
-alt1.damage(0='not taken into account' , 1= 'taken into account') /
-alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
-alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
-alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
-alt2.damage(0='not taken into account' , 1= 'taken into account') /
-alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
-
-$
-
-
-
-
-
-
-? efficient design
-
-design
-;alts = alt1, alt2, alt3
-;rows = 36
-;block = 4
-
-;eff = (mnl,d)
-;rep = 1000
-;bseed = 2333344
-;rseed = 2333344
-
-;con
-;model:
-
-
-
-U(alt1) = b0[-0.4] + b1.dummy[0.1|-0.1] * Initiator[1,2,0] + b2.dummy[0.1|-0.1] * Funding[1,2,0] + b3[0.2] * Damage [0,1] + b4[0.015] * Compensation[2,5,10,15,20,30] /
-U(alt2) = b0 + b1 * Initiator + b2 * Funding + b3 * Damage + b4 * Compensation
-
-;formatTitle = 'Scenario <scenarionumber>'
-;formatTableDimensions = 3, 6
-;formatTable:
-1,1 = '' /
-1,2 = 'Initiative to join the scheme' /
-1,3 = 'Source of funding for the compensation' /
-1,4 = 'Impact of forest damage on the carbon amount' /
-1,5 = 'Amount of carbon compensation ' /
-1,6 = 'Choice question&:' /
-2,1 = 'alt1' /
-2,2 = '<alt1.initiator>' /
-2,3 = '<alt1.funding>' /
-2,4 = '<alt1.damage>' /
-2,5 = '<alt1.compensation>' /
-2,6 = '' /
-3,1 = 'alt2' /
-3,2 = '<alt2.initiator>' /
-3,3 = '<alt2.funding>' /
-3,4 = '<alt2.damage>' /
-3,5 = '<alt2.compensation>' /
-3,6 = ''
-;formatTableStyle:
-1,1 = 'default' /
-1,2 = 'headingattribute' /
-1,3 = 'headingattribute' /
-1,4 = 'headingattribute' /
-1,5 = 'headingattribute' /
-1,6 = 'headingattribute' /
-2,1 = 'heading1' /
-2,2 = 'body1' /
-2,3 = 'body1' /
-2,4 = 'body1' /
-2,5 = 'body1' /
-2,6 = 'choice1' /
-3,1 = 'heading2' /
-3,2 = 'body2' /
-3,3 = 'body2' /
-3,4 = 'body2' /
-3,5 = 'body2' /
-3,6 = 'choice2'
-;formatStyleSheet = Default.css
-;formatAttributes:
-alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
-alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
-alt1.damage(0='not taken into account' , 1= 'taken into account') /
-alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
-alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
-alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
-alt2.damage(0='not taken into account' , 1= 'taken into account') /
-alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
-
-
-
-
-$
-
-
-
-? Bayesian efficient design
-
-design
-;alts = alt1, alt2, alt3
-;rows = 36
-;block = 4
-
-;eff = (mnl,d,mean)
-;rep = 1000
-;bdraws = halton(1000)
-;bseed = 2333344
-;rseed = 2333344
-
-;con
-;model:
-
-
-
-U(alt1) = b0[(n,-0.4,0.3)] + b1.dummy[(n,0.1,0.2)|(n,0.1,0.2)] * Initiator[1,2,0] + b2.dummy[(n,0.1,0.2)|(n,0.1,0.2)] * Funding[1,2,0] + b3[(u,0,0.6)] * Damage [0,1] + b4[(u,0.05,0.2)] * Compensation[2,5,10,15,20,30] /
-U(alt2) = b0 + b1 * Initiator + b2 * Funding + b3 * Damage + b4 * Compensation
-
-;formatTitle = 'Scenario <scenarionumber>'
-;formatTableDimensions = 3, 6
-;formatTable:
-1,1 = '' /
-1,2 = 'Initiative to join the scheme' /
-1,3 = 'Source of funding for the compensation' /
-1,4 = 'Impact of forest damage on the carbon amount' /
-1,5 = 'Amount of carbon compensation ' /
-1,6 = 'Choice question&:' /
-2,1 = 'alt1' /
-2,2 = '<alt1.initiator>' /
-2,3 = '<alt1.funding>' /
-2,4 = '<alt1.damage>' /
-2,5 = '<alt1.compensation>' /
-2,6 = '' /
-3,1 = 'alt2' /
-3,2 = '<alt2.initiator>' /
-3,3 = '<alt2.funding>' /
-3,4 = '<alt2.damage>' /
-3,5 = '<alt2.compensation>' /
-3,6 = ''
-;formatTableStyle:
-1,1 = 'default' /
-1,2 = 'headingattribute' /
-1,3 = 'headingattribute' /
-1,4 = 'headingattribute' /
-1,5 = 'headingattribute' /
-1,6 = 'headingattribute' /
-2,1 = 'heading1' /
-2,2 = 'body1' /
-2,3 = 'body1' /
-2,4 = 'body1' /
-2,5 = 'body1' /
-2,6 = 'choice1' /
-3,1 = 'heading2' /
-3,2 = 'body2' /
-3,3 = 'body2' /
-3,4 = 'body2' /
-3,5 = 'body2' /
-3,6 = 'choice2'
-;formatStyleSheet = Default.css
-;formatAttributes:
-alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
-alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
-alt1.damage(0='not taken into account' , 1= 'taken into account') /
-alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
-alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
-alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
-alt2.damage(0='not taken into account' , 1= 'taken into account') /
-alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
-
-
-
-
-$
-
-
-
-
-
-
-
-
-U(alt1) = bm0 + b1 * Origin[0,1] + b2 * Eco[0,1] + bp1[0.02] * Pm[25:75:10]/
-U(alt2) = bv0 + b1 * Origin + b2 * Eco + bp2 * Pv[15:85:10]/
-U(alt3) = bb0 + b1 * Origin + b2 * Eco + bp3 * Pb[5:45:10]
-
-
-??? Designs aus anderen projekten als vorlage Baysian Efficient Design 1
-
-design
-;alts = alt1, alt2, alt3, alt4
-;rows = 24
-;block = 4
-;eff = (mnl,d,mean)
-;rep = 1000
-;bdraws = halton(1000)
-;con
-;model:
-U(alt1) = b1[(n,-1.2,0.1)] + b2[(n,0.1,0.02)] * B[0,1] + b3[(n,0.4,0.1)] * C[0,1] + b4[(n,0.3,0.1)] * D[0,1] + b5[(n,0.02,0.001)] * P[35:83:6]/
-U(alt2) = b6[(n,-1.4,0.1)] + b2 * B + b3 * C + b4 * D + b5 * P
-?U(alt3) =
-
-;formatTitle = 'Scenario <scenarionumber>'
-;formatTableDimensions = 3, 6
-;formatTable:
-1,1 = '' /
-1,2 = 'Results' /
-1,3 = 'Beratung' /
-1,4 = 'Partner' /
-1,5 = 'Kompensation' /
-1,6 = 'Choice question&:' /
-2,1 = 'alt1' /
-2,2 = '<alt1.b>' /
-2,3 = '<alt1.c>' /
-2,4 = '<alt1.d>' /
-2,5 = '<alt1.p>' /
-2,6 = '' /
-3,1 = 'alt2' /
-3,2 = '<alt2.b>' /
-3,3 = '<alt2.c>' /
-3,4 = '<alt2.d>' /
-3,5 = '<alt2.p>' /
-3,6 = ''
-;formatTableStyle:
-1,1 = 'default' /
-1,2 = 'headingattribute' /
-1,3 = 'headingattribute' /
-1,4 = 'headingattribute' /
-1,5 = 'headingattribute' /
-1,6 = 'headingattribute' /
-2,1 = 'heading1' /
-2,2 = 'body1' /
-2,3 = 'body1' /
-2,4 = 'body1' /
-2,5 = 'body1' /
-2,6 = 'choice1' /
-3,1 = 'heading2' /
-3,2 = 'body2' /
-3,3 = 'body2' /
-3,4 = 'body2' /
-3,5 = 'body2' /
-3,6 = 'choice2'
-;formatStyleSheet = Default.css
-;formatAttributes:
-alt1.b(0=Results, 1=Action) /
-alt1.c(0=Keine Beratung, 1=Mit Beratung) /
-alt1.d(0=Keine Partner, 1=Mit Partner) /
-alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
-alt2.b(0=Results, 1=Action) /
-alt2.c(0=Keine Beratung, 1=Mit Beratung) /
-alt2.d(0=Keine Partner, 1=Mit Partner) /
-alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
-$
-
-
-
-?Efficient Design
-
-
-design
-;alts = alt1*, alt2*, alt3
-;rows = 36
-;block = 3
-;eff = (mnl,d)
-;rep = 1000
-
-;con
-;model:
-U(alt1) = b1[-1.2] + b2[0.1] * B[0,1] + b3[0.4] * C[0,1] + b4[0.3] * D[0,1] + b5[0.02] * P[35:83:6]/
-U(alt2) = b6[-1.4] + b2 * B + b3 * C + b4 * D + b5 * P
-?U(alt3) =
-
-;formatTitle = 'Scenario <scenarionumber>'
-;formatTableDimensions = 3, 6
-;formatTable:
-1,1 = '' /
-1,2 = 'Results' /
-1,3 = 'Beratung' /
-1,4 = 'Partner' /
-1,5 = 'Kompensation' /
-1,6 = 'Choice question&:' /
-2,1 = 'alt1' /
-2,2 = '<alt1.b>' /
-2,3 = '<alt1.c>' /
-2,4 = '<alt1.d>' /
-2,5 = '<alt1.p>' /
-2,6 = '' /
-3,1 = 'alt2' /
-3,2 = '<alt2.b>' /
-3,3 = '<alt2.c>' /
-3,4 = '<alt2.d>' /
-3,5 = '<alt2.p>' /
-3,6 = ''
-;formatTableStyle:
-1,1 = 'default' /
-1,2 = 'headingattribute' /
-1,3 = 'headingattribute' /
-1,4 = 'headingattribute' /
-1,5 = 'headingattribute' /
-1,6 = 'headingattribute' /
-2,1 = 'heading1' /
-2,2 = 'body1' /
-2,3 = 'body1' /
-2,4 = 'body1' /
-2,5 = 'body1' /
-2,6 = 'choice1' /
-3,1 = 'heading2' /
-3,2 = 'body2' /
-3,3 = 'body2' /
-3,4 = 'body2' /
-3,5 = 'body2' /
-3,6 = 'choice2'
-;formatStyleSheet = Default.css
-;formatAttributes:
-alt1.b(0=Results, 1=Action) /
-alt1.c(0=Keine Beratung, 1=Mit Beratung) /
-alt1.d(0=Keine Partner, 1=Mit Partner) /
-alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
-alt2.b(0=Results, 1=Action) /
-alt2.c(0=Keine Beratung, 1=Mit Beratung) /
-alt2.d(0=Keine Partner, 1=Mit Partner) /
-alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
-$
-
-
-? Orthogonal Design
-
-
-
-
-design
-;alts = alt1*, alt2*, alt3
-;rows = 36
-;block = 3
-;orth = seq
-
-
-;con
-;model:
-U(alt1) = b1 + b2 * B[0,1] + b3 * C[0,1] + b4 * D[0,1] + b5 * P[35:83:6]/
-U(alt2) = b6[-1.4] + b2 * B + b3 * C + b4 * D + b5 * P
-
-
-;formatTitle = 'Scenario <scenarionumber>'
-;formatTableDimensions = 3, 6
-;formatTable:
-1,1 = '' /
-1,2 = 'Results' /
-1,3 = 'Beratung' /
-1,4 = 'Partner' /
-1,5 = 'Kompensation' /
-1,6 = 'Choice question&:' /
-2,1 = 'alt1' /
-2,2 = '<alt1.b>' /
-2,3 = '<alt1.c>' /
-2,4 = '<alt1.d>' /
-2,5 = '<alt1.p>' /
-2,6 = '' /
-3,1 = 'alt2' /
-3,2 = '<alt2.b>' /
-3,3 = '<alt2.c>' /
-3,4 = '<alt2.d>' /
-3,5 = '<alt2.p>' /
-3,6 = ''
-;formatTableStyle:
-1,1 = 'default' /
-1,2 = 'headingattribute' /
-1,3 = 'headingattribute' /
-1,4 = 'headingattribute' /
-1,5 = 'headingattribute' /
-1,6 = 'headingattribute' /
-2,1 = 'heading1' /
-2,2 = 'body1' /
-2,3 = 'body1' /
-2,4 = 'body1' /
-2,5 = 'body1' /
-2,6 = 'choice1' /
-3,1 = 'heading2' /
-3,2 = 'body2' /
-3,3 = 'body2' /
-3,4 = 'body2' /
-3,5 = 'body2' /
-3,6 = 'choice2'
-;formatStyleSheet = Default.css
-;formatAttributes:
-alt1.b(0=Results, 1=Action) /
-alt1.c(0=Keine Beratung, 1=Mit Beratung) /
-alt1.d(0=Keine Partner, 1=Mit Partner) /
-alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
-alt2.b(0=Results, 1=Action) /
-alt2.c(0=Keine Beratung, 1=Mit Beratung) /
-alt2.d(0=Keine Partner, 1=Mit Partner) /
-alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
-$
-
-
-
-
-
-
-
+
+? Orthogonal design
+
+design
+;alts = alt1, alt2, alt3
+;rows = 24
+;block = 4
+
+;orth = seq
+
+
+;con
+;model:
+
+
+U(alt1) = b0 + b1 * Initiator[1,2,0] + b2 * Funding[1,2,0] + b3 * Damage [0,1] + b4 * Compensation[2,5,10,15,20,30] /
+U(alt2) = b0 + b1 * Initiator[1,2,0] + b2 * Funding[1,2,0] + b3 * Damage [0,1] + b4 * Compensation[2,5,10,15,20,30]
+
+;formatTitle = 'Scenario <scenarionumber>'
+;formatTableDimensions = 3, 6
+;formatTable:
+1,1 = '' /
+1,2 = 'Initiative to join the scheme' /
+1,3 = 'Source of funding for the compensation' /
+1,4 = 'Impact of forest damage on the carbon amount' /
+1,5 = 'Amount of carbon compensation ' /
+1,6 = 'Choice question&:' /
+2,1 = 'alt1' /
+2,2 = '<alt1.initiator>' /
+2,3 = '<alt1.funding>' /
+2,4 = '<alt1.damage>' /
+2,5 = '<alt1.compensation>' /
+2,6 = '' /
+3,1 = 'alt2' /
+3,2 = '<alt2.initiator>' /
+3,3 = '<alt2.funding>' /
+3,4 = '<alt2.damage>' /
+3,5 = '<alt2.compensation>' /
+3,6 = ''
+;formatTableStyle:
+1,1 = 'default' /
+1,2 = 'headingattribute' /
+1,3 = 'headingattribute' /
+1,4 = 'headingattribute' /
+1,5 = 'headingattribute' /
+1,6 = 'headingattribute' /
+2,1 = 'heading1' /
+2,2 = 'body1' /
+2,3 = 'body1' /
+2,4 = 'body1' /
+2,5 = 'body1' /
+2,6 = 'choice1' /
+3,1 = 'heading2' /
+3,2 = 'body2' /
+3,3 = 'body2' /
+3,4 = 'body2' /
+3,5 = 'body2' /
+3,6 = 'choice2'
+;formatStyleSheet = Default.css
+;formatAttributes:
+alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
+alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
+alt1.damage(0='not taken into account' , 1= 'taken into account') /
+alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
+alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
+alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
+alt2.damage(0='not taken into account' , 1= 'taken into account') /
+alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
+
+$
+
+
+
+
+
+
+? efficient design
+
+design
+;alts = alt1, alt2, alt3
+;rows = 36
+;block = 4
+
+;eff = (mnl,d)
+;rep = 1000
+;bseed = 2333344
+;rseed = 2333344
+
+;con
+;model:
+
+
+
+U(alt1) = b0[-0.4] + b1.dummy[0.1|-0.1] * Initiator[1,2,0] + b2.dummy[0.1|-0.1] * Funding[1,2,0] + b3[0.2] * Damage [0,1] + b4[0.015] * Compensation[2,5,10,15,20,30] /
+U(alt2) = b0 + b1 * Initiator + b2 * Funding + b3 * Damage + b4 * Compensation
+
+;formatTitle = 'Scenario <scenarionumber>'
+;formatTableDimensions = 3, 6
+;formatTable:
+1,1 = '' /
+1,2 = 'Initiative to join the scheme' /
+1,3 = 'Source of funding for the compensation' /
+1,4 = 'Impact of forest damage on the carbon amount' /
+1,5 = 'Amount of carbon compensation ' /
+1,6 = 'Choice question&:' /
+2,1 = 'alt1' /
+2,2 = '<alt1.initiator>' /
+2,3 = '<alt1.funding>' /
+2,4 = '<alt1.damage>' /
+2,5 = '<alt1.compensation>' /
+2,6 = '' /
+3,1 = 'alt2' /
+3,2 = '<alt2.initiator>' /
+3,3 = '<alt2.funding>' /
+3,4 = '<alt2.damage>' /
+3,5 = '<alt2.compensation>' /
+3,6 = ''
+;formatTableStyle:
+1,1 = 'default' /
+1,2 = 'headingattribute' /
+1,3 = 'headingattribute' /
+1,4 = 'headingattribute' /
+1,5 = 'headingattribute' /
+1,6 = 'headingattribute' /
+2,1 = 'heading1' /
+2,2 = 'body1' /
+2,3 = 'body1' /
+2,4 = 'body1' /
+2,5 = 'body1' /
+2,6 = 'choice1' /
+3,1 = 'heading2' /
+3,2 = 'body2' /
+3,3 = 'body2' /
+3,4 = 'body2' /
+3,5 = 'body2' /
+3,6 = 'choice2'
+;formatStyleSheet = Default.css
+;formatAttributes:
+alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
+alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
+alt1.damage(0='not taken into account' , 1= 'taken into account') /
+alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
+alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
+alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
+alt2.damage(0='not taken into account' , 1= 'taken into account') /
+alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
+
+
+
+
+$
+
+
+
+? Bayesian efficient design
+
+design
+;alts = alt1, alt2, alt3
+;rows = 36
+;block = 4
+
+;eff = (mnl,d,mean)
+;rep = 1000
+;bdraws = halton(1000)
+;bseed = 2333344
+;rseed = 2333344
+
+;con
+;model:
+
+
+
+U(alt1) = b0[(n,-0.4,0.3)] + b1.dummy[(n,0.1,0.2)|(n,0.1,0.2)] * Initiator[1,2,0] + b2.dummy[(n,0.1,0.2)|(n,0.1,0.2)] * Funding[1,2,0] + b3[(u,0,0.6)] * Damage [0,1] + b4[(u,0.05,0.2)] * Compensation[2,5,10,15,20,30] /
+U(alt2) = b0 + b1 * Initiator + b2 * Funding + b3 * Damage + b4 * Compensation
+
+;formatTitle = 'Scenario <scenarionumber>'
+;formatTableDimensions = 3, 6
+;formatTable:
+1,1 = '' /
+1,2 = 'Initiative to join the scheme' /
+1,3 = 'Source of funding for the compensation' /
+1,4 = 'Impact of forest damage on the carbon amount' /
+1,5 = 'Amount of carbon compensation ' /
+1,6 = 'Choice question&:' /
+2,1 = 'alt1' /
+2,2 = '<alt1.initiator>' /
+2,3 = '<alt1.funding>' /
+2,4 = '<alt1.damage>' /
+2,5 = '<alt1.compensation>' /
+2,6 = '' /
+3,1 = 'alt2' /
+3,2 = '<alt2.initiator>' /
+3,3 = '<alt2.funding>' /
+3,4 = '<alt2.damage>' /
+3,5 = '<alt2.compensation>' /
+3,6 = ''
+;formatTableStyle:
+1,1 = 'default' /
+1,2 = 'headingattribute' /
+1,3 = 'headingattribute' /
+1,4 = 'headingattribute' /
+1,5 = 'headingattribute' /
+1,6 = 'headingattribute' /
+2,1 = 'heading1' /
+2,2 = 'body1' /
+2,3 = 'body1' /
+2,4 = 'body1' /
+2,5 = 'body1' /
+2,6 = 'choice1' /
+3,1 = 'heading2' /
+3,2 = 'body2' /
+3,3 = 'body2' /
+3,4 = 'body2' /
+3,5 = 'body2' /
+3,6 = 'choice2'
+;formatStyleSheet = Default.css
+;formatAttributes:
+alt1.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
+alt1.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
+alt1.damage(0='not taken into account' , 1= 'taken into account') /
+alt1.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR) /
+alt2.initiator(1='A familiar forestry professional ', 2='A forestry expert ', 0='My initiative') /
+alt2.funding(1='Emission offset payments paid by domestic companies', 2='Emission offset payments paid by foreign companies', 0='State tax resources') /
+alt2.damage(0='not taken into account' , 1= 'taken into account') /
+alt2.compensation(2=# EUR, 5=# EUR, 10=# EUR, 15=# EUR, 20=# EUR, 30=# EUR)
+
+
+
+
+$
+
+
+
+
+
+
+
+
+U(alt1) = bm0 + b1 * Origin[0,1] + b2 * Eco[0,1] + bp1[0.02] * Pm[25:75:10]/
+U(alt2) = bv0 + b1 * Origin + b2 * Eco + bp2 * Pv[15:85:10]/
+U(alt3) = bb0 + b1 * Origin + b2 * Eco + bp3 * Pb[5:45:10]
+
+
+??? Designs aus anderen projekten als vorlage Baysian Efficient Design 1
+
+design
+;alts = alt1, alt2, alt3, alt4
+;rows = 24
+;block = 4
+;eff = (mnl,d,mean)
+;rep = 1000
+;bdraws = halton(1000)
+;con
+;model:
+U(alt1) = b1[(n,-1.2,0.1)] + b2[(n,0.1,0.02)] * B[0,1] + b3[(n,0.4,0.1)] * C[0,1] + b4[(n,0.3,0.1)] * D[0,1] + b5[(n,0.02,0.001)] * P[35:83:6]/
+U(alt2) = b6[(n,-1.4,0.1)] + b2 * B + b3 * C + b4 * D + b5 * P
+?U(alt3) =
+
+;formatTitle = 'Scenario <scenarionumber>'
+;formatTableDimensions = 3, 6
+;formatTable:
+1,1 = '' /
+1,2 = 'Results' /
+1,3 = 'Beratung' /
+1,4 = 'Partner' /
+1,5 = 'Kompensation' /
+1,6 = 'Choice question&:' /
+2,1 = 'alt1' /
+2,2 = '<alt1.b>' /
+2,3 = '<alt1.c>' /
+2,4 = '<alt1.d>' /
+2,5 = '<alt1.p>' /
+2,6 = '' /
+3,1 = 'alt2' /
+3,2 = '<alt2.b>' /
+3,3 = '<alt2.c>' /
+3,4 = '<alt2.d>' /
+3,5 = '<alt2.p>' /
+3,6 = ''
+;formatTableStyle:
+1,1 = 'default' /
+1,2 = 'headingattribute' /
+1,3 = 'headingattribute' /
+1,4 = 'headingattribute' /
+1,5 = 'headingattribute' /
+1,6 = 'headingattribute' /
+2,1 = 'heading1' /
+2,2 = 'body1' /
+2,3 = 'body1' /
+2,4 = 'body1' /
+2,5 = 'body1' /
+2,6 = 'choice1' /
+3,1 = 'heading2' /
+3,2 = 'body2' /
+3,3 = 'body2' /
+3,4 = 'body2' /
+3,5 = 'body2' /
+3,6 = 'choice2'
+;formatStyleSheet = Default.css
+;formatAttributes:
+alt1.b(0=Results, 1=Action) /
+alt1.c(0=Keine Beratung, 1=Mit Beratung) /
+alt1.d(0=Keine Partner, 1=Mit Partner) /
+alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
+alt2.b(0=Results, 1=Action) /
+alt2.c(0=Keine Beratung, 1=Mit Beratung) /
+alt2.d(0=Keine Partner, 1=Mit Partner) /
+alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
+$
+
+
+
+?Efficient Design
+
+
+design
+;alts = alt1*, alt2*, alt3
+;rows = 36
+;block = 3
+;eff = (mnl,d)
+;rep = 1000
+
+;con
+;model:
+U(alt1) = b1[-1.2] + b2[0.1] * B[0,1] + b3[0.4] * C[0,1] + b4[0.3] * D[0,1] + b5[0.02] * P[35:83:6]/
+U(alt2) = b6[-1.4] + b2 * B + b3 * C + b4 * D + b5 * P
+?U(alt3) =
+
+;formatTitle = 'Scenario <scenarionumber>'
+;formatTableDimensions = 3, 6
+;formatTable:
+1,1 = '' /
+1,2 = 'Results' /
+1,3 = 'Beratung' /
+1,4 = 'Partner' /
+1,5 = 'Kompensation' /
+1,6 = 'Choice question&:' /
+2,1 = 'alt1' /
+2,2 = '<alt1.b>' /
+2,3 = '<alt1.c>' /
+2,4 = '<alt1.d>' /
+2,5 = '<alt1.p>' /
+2,6 = '' /
+3,1 = 'alt2' /
+3,2 = '<alt2.b>' /
+3,3 = '<alt2.c>' /
+3,4 = '<alt2.d>' /
+3,5 = '<alt2.p>' /
+3,6 = ''
+;formatTableStyle:
+1,1 = 'default' /
+1,2 = 'headingattribute' /
+1,3 = 'headingattribute' /
+1,4 = 'headingattribute' /
+1,5 = 'headingattribute' /
+1,6 = 'headingattribute' /
+2,1 = 'heading1' /
+2,2 = 'body1' /
+2,3 = 'body1' /
+2,4 = 'body1' /
+2,5 = 'body1' /
+2,6 = 'choice1' /
+3,1 = 'heading2' /
+3,2 = 'body2' /
+3,3 = 'body2' /
+3,4 = 'body2' /
+3,5 = 'body2' /
+3,6 = 'choice2'
+;formatStyleSheet = Default.css
+;formatAttributes:
+alt1.b(0=Results, 1=Action) /
+alt1.c(0=Keine Beratung, 1=Mit Beratung) /
+alt1.d(0=Keine Partner, 1=Mit Partner) /
+alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
+alt2.b(0=Results, 1=Action) /
+alt2.c(0=Keine Beratung, 1=Mit Beratung) /
+alt2.d(0=Keine Partner, 1=Mit Partner) /
+alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
+$
+
+
+? Orthogonal Design
+
+
+
+
+design
+;alts = alt1*, alt2*, alt3
+;rows = 36
+;block = 3
+;orth = seq
+
+
+;con
+;model:
+U(alt1) = b1 + b2 * B[0,1] + b3 * C[0,1] + b4 * D[0,1] + b5 * P[35:83:6]/
+U(alt2) = b6[-1.4] + b2 * B + b3 * C + b4 * D + b5 * P
+
+
+;formatTitle = 'Scenario <scenarionumber>'
+;formatTableDimensions = 3, 6
+;formatTable:
+1,1 = '' /
+1,2 = 'Results' /
+1,3 = 'Beratung' /
+1,4 = 'Partner' /
+1,5 = 'Kompensation' /
+1,6 = 'Choice question&:' /
+2,1 = 'alt1' /
+2,2 = '<alt1.b>' /
+2,3 = '<alt1.c>' /
+2,4 = '<alt1.d>' /
+2,5 = '<alt1.p>' /
+2,6 = '' /
+3,1 = 'alt2' /
+3,2 = '<alt2.b>' /
+3,3 = '<alt2.c>' /
+3,4 = '<alt2.d>' /
+3,5 = '<alt2.p>' /
+3,6 = ''
+;formatTableStyle:
+1,1 = 'default' /
+1,2 = 'headingattribute' /
+1,3 = 'headingattribute' /
+1,4 = 'headingattribute' /
+1,5 = 'headingattribute' /
+1,6 = 'headingattribute' /
+2,1 = 'heading1' /
+2,2 = 'body1' /
+2,3 = 'body1' /
+2,4 = 'body1' /
+2,5 = 'body1' /
+2,6 = 'choice1' /
+3,1 = 'heading2' /
+3,2 = 'body2' /
+3,3 = 'body2' /
+3,4 = 'body2' /
+3,5 = 'body2' /
+3,6 = 'choice2'
+;formatStyleSheet = Default.css
+;formatAttributes:
+alt1.b(0=Results, 1=Action) /
+alt1.c(0=Keine Beratung, 1=Mit Beratung) /
+alt1.d(0=Keine Partner, 1=Mit Partner) /
+alt1.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0) /
+alt2.b(0=Results, 1=Action) /
+alt2.c(0=Keine Beratung, 1=Mit Beratung) /
+alt2.d(0=Keine Partner, 1=Mit Partner) /
+alt2.p(35=#0, 41=#0, 47=#0, 53=#0, 59=#0, 65=#0, 71=#0, 77=#0, 83=#0)
+$
+
+
+
+
+
+
+
diff --git a/designs-seagri/bayeff3_newpriors_dummy.ngd b/designs-seagri/bayeff3_newpriors_dummy.ngd
index 49894f1a40f159b85823078d59acea0b820a9f04..c232f8f660117e9b9180e6b668cc0389b1510137 100644
--- a/designs-seagri/bayeff3_newpriors_dummy.ngd
+++ b/designs-seagri/bayeff3_newpriors_dummy.ngd
@@ -1,41 +1,41 @@
-Design Choice situation alt1.initiator alt1.funding alt1.damage alt1.compensation alt2.initiator alt2.funding alt2.damage alt2.compensation Block
-1 1 2 1 0 2 1 0 1 30 1
-1 2 2 1 0 5 1 2 1 10 4
-1 3 1 0 0 10 0 1 1 20 3
-1 4 1 1 0 20 0 2 1 30 4
-1 5 0 1 1 30 1 2 0 5 3
-1 6 1 0 0 5 0 2 1 15 2
-1 7 1 1 0 2 0 2 1 5 4
-1 8 0 1 0 15 1 0 1 2 2
-1 9 0 2 1 30 1 0 0 2 2
-1 10 1 2 1 30 2 0 0 20 4
-1 11 2 2 0 10 1 1 1 5 3
-1 12 2 2 0 30 0 1 1 10 1
-1 13 0 1 0 2 2 0 1 5 2
-1 14 2 0 1 10 0 2 0 30 2
-1 15 1 0 0 20 2 1 1 30 4
-1 16 1 1 1 10 2 2 0 2 2
-1 17 0 2 0 5 1 0 1 20 2
-1 18 0 0 1 30 1 2 0 15 3
-1 19 0 0 1 2 2 1 0 15 4
-1 20 0 1 1 20 2 2 0 10 1
-1 21 1 2 1 10 2 0 0 5 2
-1 22 0 0 1 15 2 1 1 30 2
-1 23 2 2 1 2 0 0 1 10 1
-1 24 1 2 0 5 2 1 1 2 3
-1 25 2 0 0 30 0 2 0 20 1
-1 26 1 2 1 20 2 1 0 10 1
-1 27 2 2 1 20 1 1 0 15 3
-1 28 1 1 1 5 2 0 0 15 4
-1 29 0 0 0 2 2 1 1 20 1
-1 30 0 0 0 15 1 1 0 30 1
-1 31 2 0 1 5 1 2 0 2 1
-1 32 2 2 0 20 0 0 1 2 3
-1 33 2 2 1 15 0 0 0 10 3
-1 34 1 1 1 15 0 2 0 20 3
-1 35 0 1 0 10 1 0 1 15 4
-1 36 2 0 1 15 0 1 0 5 4
-||||||||||
+Design Choice situation alt1.initiator alt1.funding alt1.damage alt1.compensation alt2.initiator alt2.funding alt2.damage alt2.compensation Block
+1 1 2 1 0 2 1 0 1 30 1
+1 2 2 1 0 5 1 2 1 10 4
+1 3 1 0 0 10 0 1 1 20 3
+1 4 1 1 0 20 0 2 1 30 4
+1 5 0 1 1 30 1 2 0 5 3
+1 6 1 0 0 5 0 2 1 15 2
+1 7 1 1 0 2 0 2 1 5 4
+1 8 0 1 0 15 1 0 1 2 2
+1 9 0 2 1 30 1 0 0 2 2
+1 10 1 2 1 30 2 0 0 20 4
+1 11 2 2 0 10 1 1 1 5 3
+1 12 2 2 0 30 0 1 1 10 1
+1 13 0 1 0 2 2 0 1 5 2
+1 14 2 0 1 10 0 2 0 30 2
+1 15 1 0 0 20 2 1 1 30 4
+1 16 1 1 1 10 2 2 0 2 2
+1 17 0 2 0 5 1 0 1 20 2
+1 18 0 0 1 30 1 2 0 15 3
+1 19 0 0 1 2 2 1 0 15 4
+1 20 0 1 1 20 2 2 0 10 1
+1 21 1 2 1 10 2 0 0 5 2
+1 22 0 0 1 15 2 1 1 30 2
+1 23 2 2 1 2 0 0 1 10 1
+1 24 1 2 0 5 2 1 1 2 3
+1 25 2 0 0 30 0 2 0 20 1
+1 26 1 2 1 20 2 1 0 10 1
+1 27 2 2 1 20 1 1 0 15 3
+1 28 1 1 1 5 2 0 0 15 4
+1 29 0 0 0 2 2 1 1 20 1
+1 30 0 0 0 15 1 1 0 30 1
+1 31 2 0 1 5 1 2 0 2 1
+1 32 2 2 0 20 0 0 1 2 3
+1 33 2 2 1 15 0 0 0 10 3
+1 34 1 1 1 15 0 2 0 20 3
+1 35 0 1 0 10 1 0 1 15 4
+1 36 2 0 1 15 0 1 0 5 4
+||||||||||
design
;alts = alt1, alt2, alt3
;rows = 36
diff --git a/designs-seagri/bayeff_newpriors1.ngd b/designs-seagri/bayeff_newpriors1.ngd
index bd3378431e31ef15c217a0497f5e41c3eaea8f6d..36a3e0f5e78c14c3a3514c36adc46ac443d370d4 100644
--- a/designs-seagri/bayeff_newpriors1.ngd
+++ b/designs-seagri/bayeff_newpriors1.ngd
@@ -1,41 +1,41 @@
-Design Choice situation alt1.initiator alt1.funding alt1.damage alt1.compensation alt2.initiator alt2.funding alt2.damage alt2.compensation Block
-1 1 1 0 0 30 0 1 1 30 4
-1 2 2 1 0 10 0 2 1 10 3
-1 3 1 2 1 10 0 0 0 10 3
-1 4 0 0 1 10 2 2 0 10 4
-1 5 1 1 1 2 2 0 0 2 4
-1 6 1 0 1 2 2 2 0 2 1
-1 7 2 1 1 20 0 0 0 20 2
-1 8 0 1 1 5 1 2 0 5 1
-1 9 2 1 0 15 1 2 1 15 1
-1 10 1 1 1 30 2 2 0 30 2
-1 11 0 2 1 5 1 1 0 10 2
-1 12 0 0 1 20 2 1 0 20 3
-1 13 2 2 1 2 1 0 0 5 1
-1 14 0 2 0 30 1 0 1 30 4
-1 15 1 1 1 20 0 2 0 20 3
-1 16 1 2 0 30 0 1 1 30 2
-1 17 0 1 0 2 2 0 1 2 4
-1 18 0 2 0 5 2 1 1 5 3
-1 19 0 0 0 30 2 2 1 30 2
-1 20 0 0 1 2 1 2 0 2 2
-1 21 1 1 0 5 0 2 1 5 4
-1 22 2 1 0 5 1 2 1 2 4
-1 23 2 2 1 10 1 1 0 10 4
-1 24 0 2 1 15 2 0 0 20 3
-1 25 1 2 0 15 0 0 1 15 1
-1 26 2 0 1 15 0 1 0 15 4
-1 27 2 2 1 20 1 1 0 20 2
-1 28 0 1 0 15 2 0 1 15 1
-1 29 1 2 0 30 2 0 1 30 3
-1 30 1 0 0 20 0 1 1 15 2
-1 31 2 1 1 2 0 0 0 5 3
-1 32 0 2 0 20 1 0 1 20 1
-1 33 2 0 0 5 0 1 1 2 1
-1 34 1 0 1 15 2 1 0 15 1
-1 35 2 0 0 10 1 1 1 5 3
-1 36 2 0 0 10 1 2 1 10 2
-||||||||||
+Design Choice situation alt1.initiator alt1.funding alt1.damage alt1.compensation alt2.initiator alt2.funding alt2.damage alt2.compensation Block
+1 1 1 0 0 30 0 1 1 30 4
+1 2 2 1 0 10 0 2 1 10 3
+1 3 1 2 1 10 0 0 0 10 3
+1 4 0 0 1 10 2 2 0 10 4
+1 5 1 1 1 2 2 0 0 2 4
+1 6 1 0 1 2 2 2 0 2 1
+1 7 2 1 1 20 0 0 0 20 2
+1 8 0 1 1 5 1 2 0 5 1
+1 9 2 1 0 15 1 2 1 15 1
+1 10 1 1 1 30 2 2 0 30 2
+1 11 0 2 1 5 1 1 0 10 2
+1 12 0 0 1 20 2 1 0 20 3
+1 13 2 2 1 2 1 0 0 5 1
+1 14 0 2 0 30 1 0 1 30 4
+1 15 1 1 1 20 0 2 0 20 3
+1 16 1 2 0 30 0 1 1 30 2
+1 17 0 1 0 2 2 0 1 2 4
+1 18 0 2 0 5 2 1 1 5 3
+1 19 0 0 0 30 2 2 1 30 2
+1 20 0 0 1 2 1 2 0 2 2
+1 21 1 1 0 5 0 2 1 5 4
+1 22 2 1 0 5 1 2 1 2 4
+1 23 2 2 1 10 1 1 0 10 4
+1 24 0 2 1 15 2 0 0 20 3
+1 25 1 2 0 15 0 0 1 15 1
+1 26 2 0 1 15 0 1 0 15 4
+1 27 2 2 1 20 1 1 0 20 2
+1 28 0 1 0 15 2 0 1 15 1
+1 29 1 2 0 30 2 0 1 30 3
+1 30 1 0 0 20 0 1 1 15 2
+1 31 2 1 1 2 0 0 0 5 3
+1 32 0 2 0 20 1 0 1 20 1
+1 33 2 0 0 5 0 1 1 2 1
+1 34 1 0 1 15 2 1 0 15 1
+1 35 2 0 0 10 1 1 1 5 3
+1 36 2 0 0 10 1 2 1 10 2
+||||||||||
design
;alts = alt1, alt2, alt3
;rows = 36
diff --git a/designs-seagri/bayrevised_newpriors1.ngd b/designs-seagri/bayrevised_newpriors1.ngd
index 96fb78a2befb3eaf680f68251929ade60e7cbe7f..fb784a6900a2f13f166ca103ac634461b75537c6 100644
--- a/designs-seagri/bayrevised_newpriors1.ngd
+++ b/designs-seagri/bayrevised_newpriors1.ngd
@@ -1,41 +1,41 @@
-Design Choice situation alt1.initiator alt1.funding alt1.damage alt1.compensation alt2.initiator alt2.funding alt2.damage alt2.compensation Block
-1 1 0 0 0 15 1 1 1 10 2
-1 2 2 2 0 20 0 0 1 20 4
-1 3 0 1 0 5 2 0 1 2 3
-1 4 2 1 1 15 1 2 0 15 1
-1 5 2 2 1 10 0 1 0 10 1
-1 6 1 2 1 10 0 1 0 15 2
-1 7 1 0 0 10 2 2 1 5 2
-1 8 1 0 0 30 0 2 1 5 4
-1 9 0 1 0 30 2 0 1 30 3
-1 10 2 2 1 15 1 0 0 15 1
-1 11 1 1 1 20 2 0 0 20 2
-1 12 2 0 1 30 1 2 0 30 3
-1 13 0 2 0 30 1 0 1 30 1
-1 14 2 2 0 2 1 0 1 5 1
-1 15 1 0 1 2 0 1 0 2 1
-1 16 1 1 1 2 2 0 0 2 4
-1 17 1 2 0 20 0 1 1 20 4
-1 18 0 0 1 5 1 1 0 5 1
-1 19 2 0 0 20 0 1 1 15 4
-1 20 2 1 1 5 0 2 0 5 4
-1 21 0 2 1 15 2 0 0 20 4
-1 22 1 1 0 5 0 0 1 2 3
-1 23 0 2 1 10 2 1 0 10 4
-1 24 0 2 1 2 2 0 0 15 3
-1 25 0 2 0 10 1 1 1 10 3
-1 26 2 1 1 5 1 2 0 2 4
-1 27 2 0 0 15 1 2 1 15 2
-1 28 1 0 0 15 0 2 1 10 3
-1 29 0 0 1 20 2 1 0 20 2
-1 30 1 2 0 10 0 1 1 10 1
-1 31 1 0 1 30 2 1 0 30 3
-1 32 2 1 0 30 0 0 0 30 2
-1 33 1 1 0 20 2 2 1 20 1
-1 34 0 1 1 2 2 2 1 5 2
-1 35 0 0 0 5 1 2 1 2 2
-1 36 2 1 1 2 1 2 0 30 3
-||||||||||
+Design Choice situation alt1.initiator alt1.funding alt1.damage alt1.compensation alt2.initiator alt2.funding alt2.damage alt2.compensation Block
+1 1 0 0 0 15 1 1 1 10 2
+1 2 2 2 0 20 0 0 1 20 4
+1 3 0 1 0 5 2 0 1 2 3
+1 4 2 1 1 15 1 2 0 15 1
+1 5 2 2 1 10 0 1 0 10 1
+1 6 1 2 1 10 0 1 0 15 2
+1 7 1 0 0 10 2 2 1 5 2
+1 8 1 0 0 30 0 2 1 5 4
+1 9 0 1 0 30 2 0 1 30 3
+1 10 2 2 1 15 1 0 0 15 1
+1 11 1 1 1 20 2 0 0 20 2
+1 12 2 0 1 30 1 2 0 30 3
+1 13 0 2 0 30 1 0 1 30 1
+1 14 2 2 0 2 1 0 1 5 1
+1 15 1 0 1 2 0 1 0 2 1
+1 16 1 1 1 2 2 0 0 2 4
+1 17 1 2 0 20 0 1 1 20 4
+1 18 0 0 1 5 1 1 0 5 1
+1 19 2 0 0 20 0 1 1 15 4
+1 20 2 1 1 5 0 2 0 5 4
+1 21 0 2 1 15 2 0 0 20 4
+1 22 1 1 0 5 0 0 1 2 3
+1 23 0 2 1 10 2 1 0 10 4
+1 24 0 2 1 2 2 0 0 15 3
+1 25 0 2 0 10 1 1 1 10 3
+1 26 2 1 1 5 1 2 0 2 4
+1 27 2 0 0 15 1 2 1 15 2
+1 28 1 0 0 15 0 2 1 10 3
+1 29 0 0 1 20 2 1 0 20 2
+1 30 1 2 0 10 0 1 1 10 1
+1 31 1 0 1 30 2 1 0 30 3
+1 32 2 1 0 30 0 0 0 30 2
+1 33 1 1 0 20 2 2 1 20 1
+1 34 0 1 1 2 2 2 1 5 2
+1 35 0 0 0 5 1 2 1 2 2
+1 36 2 1 1 2 1 2 0 30 3
+||||||||||
design
;alts = alt1, alt2, alt3
;rows = 36
diff --git a/functions.R b/functions.R
index fcd789ba6f434f7dbee8fa13473ce57e1af19e92..1ff2fc90c95987df907852ae38f6173060b1bba1 100644
--- a/functions.R
+++ b/functions.R
@@ -1,165 +1,165 @@
-sim_choice <- function(designfile, no_sim=10, respondents=330, mnl_U,utils=u ) {
-
-
- require("tictoc")
- require("readr")
- require("psych")
- require("dplyr")
- require("evd")
- require("tidyr")
- require("kableExtra")
- require("gridExtra")
- require("stringr")
- require("mixl")
- require("furrr")
- require("purrr")
- require("ggplot2")
- require("formula.tools")
-
-
- mnl_U <-paste(map_chr(u,as.character,keep.source.attr = TRUE),collapse = "",";") %>%
- str_replace_all( c( "priors\\[\"" = "" , "\"\\]" = "" , "~" = "=", "\\." = "_" , " b" = " @b" , "V."="U_", " alt"="$alt"))
-
-
-
-
-
- by_formula <- function(equation){ #used to take formulas as inputs in simulation utility function
- # //! cur_data_all may get deprecated in favor of pick
- # pick(everything()) %>%
- cur_data_all() %>%
- transmute(!!lhs(equation) := !!rhs(equation) )
- }
-
-
-
-
-
- simulate_choices <- function(data=database) { #the part in dataset that needs to be repeated in each run
-
-
- # browser()
-
- n=seq_along(1:length(utils))
-
- print("DANGER")
-
- data <- data %>%
- group_by(ID) %>%
- mutate(
- across(.cols=n,.fns = ~ rgumbel(setpp,loc=0, scale=1), .names = "{'e'}_{n}" ),
- across(starts_with("V_"), .names = "{'U'}_{n}") + across(starts_with("e_")) ) %>% ungroup() %>%
- mutate(CHOICE=max.col(.[,grep("U_",names(.))])
- ) %>%
- as.data.frame()
-
-
-
-
- return(data)
-
- }
-
- estimate_sim <- function(run=1) { #start loop
-
- cat(run)
-
- database <- simulate_choices()
-
-
-
-
- model<-mixl::estimate(model_spec,start_values = est, availabilities = availabilities, data= database,)
-
- return(model)
-
- }
-
- designs_all <- list()
-
- design <- read_delim(designfile,delim = "\t",
- escape_double = FALSE,
- trim_ws = TRUE ,
- col_select = c(-Design, -starts_with("...")) ,
- name_repair = "universal") %>%
- filter(!is.na(Choice.situation))
-
-
-
- nsets<-nrow(design)
- nblocks<-max(design$Block)
- setpp <- nsets/nblocks # Choice Sets per respondent; in this 'no blocks' design everyone sees all 24 sets
- #respondents <- replications*nblocks
- replications <- respondents/nblocks
- #browser()
-
- if(!exists("manipulations")) manipulations=list()
-
- database<- design %>%
- arrange(Block,Choice.situation) %>%
- slice(rep(row_number(), replications)) %>% ## replicate design according to number of replications
- mutate(RID = rep(1:respondents, each=setpp)) %>% # create Respondent ID.
- relocate(RID,`Choice.situation`) %>%
- mutate( !!! manipulations ,
- map_dfc(utils,by_formula) #our functions to create utility variables. They need to be entered as a formula list as an argument
-
- ) %>%
- rename(ID ="RID") %>%
- rename_with(~ stringr::str_replace(.,pattern = "\\.","_"), everything()) %>%
- as.data.frame()
-
-
-
-
-
- database <- simulate_choices()
-
- model_spec <- mixl::specify_model(mnl_U, database, disable_multicore=F)
-
- est=setNames(rep(0,length(model_spec$beta_names)), model_spec$beta_names)
-
-
- availabilities <- mixl::generate_default_availabilities(
- database, model_spec$num_utility_functions)
-
- plan(multisession, workers = 8)
-
- output<- 1:no_sim %>% map(estimate_sim)
-
- coefs<-map(1:length(output),~summary(output[[.]])[["coefTable"]][c(1,8)] %>%
- tibble::rownames_to_column() %>%
- pivot_wider(names_from = rowname, values_from = c(est, rob_pval0)) ) %>%
- bind_rows(.id = "run")
-
- output[["summary"]] <-describe(coefs[,-1], fast = TRUE)
-
- output[["coefs"]] <-coefs
-
- pvals <- output[["coefs"]] %>% select(starts_with("rob_pval0"))
-
- output[["power"]] <- 100*table(apply(pvals,1, function(x) all(x<0.05)))/nrow(pvals)
-
-
- output[["metainfo"]] <- c(Path = designfile, NoSim = no_sim, NoResp =respondents)
-
-
- print(kable(output[["summary"]],digits = 2, format = "rst"))
-
-
- print(output[["power"]])
-
-
- return(output)
-
-
-}
-
-
-plot_multi_histogram <- function(df, feature, label_column) { #function to create nice multi histograms, taken somewhere from the web
- plt <- ggplot(df, aes(x=eval(parse(text=feature)), fill=eval(parse(text=label_column)))) +
- #geom_histogram(alpha=0.7, position="identity", aes(y = ..density..), color="black") +
- geom_density(alpha=0.5) +
- geom_vline(aes(xintercept=mean(eval(parse(text=feature)))), color="black", linetype="dashed", linewidth=1) + ## this makes a vertical line of the mean
- labs(x=feature, y = "Density")
- plt + guides(fill=guide_legend(title=label_column))
+sim_choice <- function(designfile, no_sim=10, respondents=330, mnl_U,utils=u ) {
+
+
+ require("tictoc")
+ require("readr")
+ require("psych")
+ require("dplyr")
+ require("evd")
+ require("tidyr")
+ require("kableExtra")
+ require("gridExtra")
+ require("stringr")
+ require("mixl")
+ require("furrr")
+ require("purrr")
+ require("ggplot2")
+ require("formula.tools")
+
+
+ mnl_U <-paste(map_chr(u,as.character,keep.source.attr = TRUE),collapse = "",";") %>%
+ str_replace_all( c( "priors\\[\"" = "" , "\"\\]" = "" , "~" = "=", "\\." = "_" , " b" = " @b" , "V."="U_", " alt"="$alt"))
+
+
+
+
+
+ by_formula <- function(equation){ #used to take formulas as inputs in simulation utility function
+ # //! cur_data_all may get deprecated in favor of pick
+ # pick(everything()) %>%
+ cur_data_all() %>%
+ transmute(!!lhs(equation) := !!rhs(equation) )
+ }
+
+
+
+
+
+ simulate_choices <- function(data=database) { #the part in dataset that needs to be repeated in each run
+
+
+ # browser()
+
+ n=seq_along(1:length(utils))
+
+ print("DANGER")
+
+ data <- data %>%
+ group_by(ID) %>%
+ mutate(
+ across(.cols=n,.fns = ~ rgumbel(setpp,loc=0, scale=1), .names = "{'e'}_{n}" ),
+ across(starts_with("V_"), .names = "{'U'}_{n}") + across(starts_with("e_")) ) %>% ungroup() %>%
+ mutate(CHOICE=max.col(.[,grep("U_",names(.))])
+ ) %>%
+ as.data.frame()
+
+
+
+
+ return(data)
+
+ }
+
+ estimate_sim <- function(run=1) { #start loop
+
+ cat(run)
+
+ database <- simulate_choices()
+
+
+
+
+ model<-mixl::estimate(model_spec,start_values = est, availabilities = availabilities, data= database,)
+
+ return(model)
+
+ }
+
+ designs_all <- list()
+
+ design <- read_delim(designfile,delim = "\t",
+ escape_double = FALSE,
+ trim_ws = TRUE ,
+ col_select = c(-Design, -starts_with("...")) ,
+ name_repair = "universal") %>%
+ filter(!is.na(Choice.situation))
+
+
+
+ nsets<-nrow(design)
+ nblocks<-max(design$Block)
+ setpp <- nsets/nblocks # Choice Sets per respondent; in this 'no blocks' design everyone sees all 24 sets
+ #respondents <- replications*nblocks
+ replications <- respondents/nblocks
+ #browser()
+
+ if(!exists("manipulations")) manipulations=list()
+
+ database<- design %>%
+ arrange(Block,Choice.situation) %>%
+ slice(rep(row_number(), replications)) %>% ## replicate design according to number of replications
+ mutate(RID = rep(1:respondents, each=setpp)) %>% # create Respondent ID.
+ relocate(RID,`Choice.situation`) %>%
+ mutate( !!! manipulations ,
+ map_dfc(utils,by_formula) #our functions to create utility variables. They need to be entered as a formula list as an argument
+
+ ) %>%
+ rename(ID ="RID") %>%
+ rename_with(~ stringr::str_replace(.,pattern = "\\.","_"), everything()) %>%
+ as.data.frame()
+
+
+
+
+
+ database <- simulate_choices()
+
+ model_spec <- mixl::specify_model(mnl_U, database, disable_multicore=F)
+
+ est=setNames(rep(0,length(model_spec$beta_names)), model_spec$beta_names)
+
+
+ availabilities <- mixl::generate_default_availabilities(
+ database, model_spec$num_utility_functions)
+
+ plan(multisession, workers = 8)
+
+ output<- 1:no_sim %>% map(estimate_sim)
+
+ coefs<-map(1:length(output),~summary(output[[.]])[["coefTable"]][c(1,8)] %>%
+ tibble::rownames_to_column() %>%
+ pivot_wider(names_from = rowname, values_from = c(est, rob_pval0)) ) %>%
+ bind_rows(.id = "run")
+
+ output[["summary"]] <-describe(coefs[,-1], fast = TRUE)
+
+ output[["coefs"]] <-coefs
+
+ pvals <- output[["coefs"]] %>% select(starts_with("rob_pval0"))
+
+ output[["power"]] <- 100*table(apply(pvals,1, function(x) all(x<0.05)))/nrow(pvals)
+
+
+ output[["metainfo"]] <- c(Path = designfile, NoSim = no_sim, NoResp =respondents)
+
+
+ print(kable(output[["summary"]],digits = 2, format = "rst"))
+
+
+ print(output[["power"]])
+
+
+ return(output)
+
+
+}
+
+
+plot_multi_histogram <- function(df, feature, label_column) { #function to create nice multi histograms, taken somewhere from the web
+ plt <- ggplot(df, aes(x=eval(parse(text=feature)), fill=eval(parse(text=label_column)))) +
+ #geom_histogram(alpha=0.7, position="identity", aes(y = ..density..), color="black") +
+ geom_density(alpha=0.5) +
+ geom_vline(aes(xintercept=mean(eval(parse(text=feature)))), color="black", linetype="dashed", linewidth=1) + ## this makes a vertical line of the mean
+ labs(x=feature, y = "Density")
+ plt + guides(fill=guide_legend(title=label_column))
}
\ No newline at end of file
diff --git a/parameters_SE Design-Agri.R b/parameters_SE Design-Agri.R
index 13fafb8104ea9b43a8dee3088388ad640fd25385..565a8d1147675235876e1dfea6a44ad705630b9b 100644
--- a/parameters_SE Design-Agri.R
+++ b/parameters_SE Design-Agri.R
@@ -1,37 +1,37 @@
-
-
-designpath<- "designs-seagri/"
-
-resps =360 # number of respondents
-nosim=2 # number of simulations to run (about 500 is minimum)
-
-#betacoefficients should not include "-"
-basc = 4.2 ## very high asc
-bprof = 0.3
-bexp = 0.3
-bdomestic = 0.3
-bforeign = 0.3
-bdamage = 0.6
-bprice = 0.2
-
-manipulations = list(alt1.professional= expr(alt1.initiator==1),
- alt2.professional= expr(alt2.initiator==1),
- alt1.expert = expr(alt1.initiator==2),
- alt2.expert = expr(alt2.initiator==2),
- alt1.domestic = expr(alt1.funding==1),
- alt2.domestic = expr(alt2.funding==1),
- alt1.foreign = expr(alt1.funding==2),
- alt2.foreign = expr(alt2.funding==2))
-
-
-
-
-
-
-#place your utility functions here
-u<-list(
- v1 =V.1 ~ bprof*alt1.professional+ bexp * alt1.expert + bdomestic * alt1.domestic + bforeign * alt1.foreign + bdamage*alt1.damage + bprice * alt1.compensation,
- v2 =V.2 ~ bprof*alt2.professional + bexp * alt2.expert + bdomestic * alt2.domestic + bforeign * alt2.foreign + bdamage*alt2.damage + bprice * alt2.compensation,
- v3 =V.3 ~ basc)
-
+
+
+designpath<- "designs-seagri/"
+
+resps =360 # number of respondents
+nosim=2 # number of simulations to run (about 500 is minimum)
+
+#betacoefficients should not include "-"
+basc = 4.2 ## very high asc
+bprof = 0.3
+bexp = 0.3
+bdomestic = 0.3
+bforeign = 0.3
+bdamage = 0.6
+bprice = 0.2
+
+manipulations = list(alt1.professional= expr(alt1.initiator==1),
+ alt2.professional= expr(alt2.initiator==1),
+ alt1.expert = expr(alt1.initiator==2),
+ alt2.expert = expr(alt2.initiator==2),
+ alt1.domestic = expr(alt1.funding==1),
+ alt2.domestic = expr(alt2.funding==1),
+ alt1.foreign = expr(alt1.funding==2),
+ alt2.foreign = expr(alt2.funding==2))
+
+
+
+
+
+
+#place your utility functions here
+u<-list(
+ v1 =V.1 ~ bprof*alt1.professional+ bexp * alt1.expert + bdomestic * alt1.domestic + bforeign * alt1.foreign + bdamage*alt1.damage + bprice * alt1.compensation,
+ v2 =V.2 ~ bprof*alt2.professional + bexp * alt2.expert + bdomestic * alt2.domestic + bforeign * alt2.foreign + bdamage*alt2.damage + bprice * alt2.compensation,
+ v3 =V.3 ~ basc)
+
source("simulationcore_purrr.R")
\ No newline at end of file
diff --git a/parameters_SE_DRIVE.R b/parameters_SE_DRIVE.R
new file mode 100644
index 0000000000000000000000000000000000000000..0d4361213876f327b141f53cdd2c72c8f4ed679f
--- /dev/null
+++ b/parameters_SE_DRIVE.R
@@ -0,0 +1,22 @@
+
+
+source("prior_est/scripts/priordetermination_parks.R")
+
+designpath<- "Designsparks/"
+
+resps =360 # number of respondents
+nosim=2000 # number of simulations to run (about 500 is minimum)
+
+
+
+
+## If beta values come from other source
+for (ano in 1:length(priors)) {
+ assign(names(priors[ano]),priors[ano])
+}
+
+#place your utility functions here
+u<-list(
+ v1 =V.1~ basc +bgroesse*alt1.groesse+ bentfernung * alt1.entfernung + bgemeinschaft * alt1.gemeinschaft + bkultur * alt1.kultur + bumweltbildung*alt1.umweltbildung + btoiletten * alt1.toiletten+ bspiel*alt1.spiel + bpflegeint*alt1.pflegeint+bpflegeziele*alt1.pflegeziele+ bbeitrag*alt1.beitrag ,
+ v2 =V.2~ basc +bgroesse*alt2.groesse+ bentfernung * alt2.entfernung + bgemeinschaft * alt2.gemeinschaft + bkultur * alt2.kultur + bumweltbildung*alt2.umweltbildung + btoiletten * alt2.toiletten+ bspiel*alt2.spiel + bpflegeint*alt2.pflegeint+bpflegeziele*alt2.pflegeziele+ bbeitrag*alt2.beitrag,
+ v3 =V.3~ 0)
diff --git a/prior_est/04_CL_PS_F.R b/prior_est/04_CL_PS_F.R
index 8b3d40c69c8a98ad6d0b73d4cf93b7bf22abd0c2..71e045b455463bd975297e4edb7ebc0f52972d62 100644
--- a/prior_est/04_CL_PS_F.R
+++ b/prior_est/04_CL_PS_F.R
@@ -1,292 +1,292 @@
-######################################################################
-### Projekt : GartenLeistungen II ###
-### Beschreibung : Choice Model mit Apollo-Paket ###
-### Conditional Logit im Preference Space ###
-### Output : Flextable (Wordfile) ###
-### Datum : 11.07.2023 ###
-### R-Version : 4.3.1 ###
-### Apollo Version: 0.2.9 ###
-######################################################################
-
-
-### Environment loeschen
-rm(list=ls())
-
-### Arbeitspfad festlegen
-wd <- "S:/10 GIS/GartenLeistungen/Auswertung GartenLeistungen/R_GL2/pilot"
-setwd(wd)
-
-### Pakete laden (wenn nicht vorhanden, installieren)
-if(!require(pacman)){
- install.packages("pacman")
- library(pacman)
-}
-
-p_load(apollo, flextable, magrittr, officer, haven, dplyr, stringr, readr, sjlabelled)
-
-# ################################################################# #
-#### INITIALISE APOLLO AND LOAD DATA ####
-# ################################################################# #
-
-### initialise apollo and core settings
-apollo_initialise()
-apollo_control= list (
- modelName = "Clogit",
- modelDescr ="Conditional Logit in preference space",
- indivID = "ID",
- mixing = FALSE # True waere Mixed Logit
-)
-
-
-# Dataframe für Flextable erstellen
-dat <- data.frame(matrix(NA,nrow=30,ncol=2))
-
-dat[,1] <- c("asc\nEntstehung eines Referenzparks in der Nachbarschaft", "asc1",
- "Größe","1",
- "Entfernung","2",
- "Gemeinschaftsaktivitäten","Gemeinschaft1",
- "Kulturveranstaltungen","Kultur1",
- "Umweltbildungsangebote","Bildung1",
- "Pflegeintensität\nje Stufe besserer Pflege","3",
- "Pflegeziele\nmit Vielfalt im Vordergund","4",
- "Toiletten","toilet1",
- "Spiel- und Sportgeräte","Spielsport1",
- "Beitrag\npro 1 €","5",
- "Anzahl","LL(start)","LL(0)","LL(final)","Rho-square","Adj. Rho-square","AIC","BIC")
-colnames(dat) <- c("Attribute","Praeferenz")
-
-### Datensatz aus Aufbereitungs-Skript laden
-load("02_apollodataF.RData")
-# falls labelled data genutzt wird: (hier für Frankfurt aber nicht der Fall)
-# for (col in names(apollodata_F)) {
-# apollodata_F[[col]] <- as.numeric(val_labels(apollodata_F[[col]]))
-# }
-database <- as.data.frame(apollodata_F)
-database <- database[order(database$ID),]
-#database <- database[-which(is.na(database$choice)),]
-
-# ################################################################# #
-#### DEFINE apollo_beta() ####
-# ################################################################# #
-
-### Startwerte festlegen
-apollo_beta=c(asc = 0,
- b_groesse = 0,
- b_entfernung = 0,
- b_gemeinschaft = 0,
- b_kultur = 0,
- b_umweltbildung = 0,
- b_toiletten = 0,
- b_spiel = 0,
- b_pflegeint = 0,
- b_pflegeziele = 0,
- b_beitrag = 0)
-
-# ################################################################# #
-#### DEFINE MODEL AND LIKELIHOOD FUNCTION ####
-# ################################################################# #
-
-### keine Parameter fix halten
-apollo_fixed = c()
-
-### validieren
-apollo_inputs = apollo_validateInputs()
-
-apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
-
- ### Function initialisation: do not change the following three commands
- ### Attach inputs and detach after function exit
- apollo_attach(apollo_beta, apollo_inputs)
- on.exit(apollo_detach(apollo_beta, apollo_inputs))
-
- ### Create list of probabilities P
- P = list()
-
- ### List of utilities (later integrated in mnl_settings below)
- V = list()
- V[['alt1']] = (b_groesse * GROESSE.1 + b_entfernung * ENTFERNUNG.1 +
- b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 + b_kultur * KULTURVERANSTALTUNGEN.1 +
- b_umweltbildung * UMWELTBILDUNG.1 + b_toiletten * TOILETTEN.1 +
- b_spiel * SPIEL_SPORT.1 +
- b_pflegeint * PFLEGEINTENSITAET.1 + b_pflegeziele * PFLEGEZIELE.1 + b_beitrag * BEITRAG.1)
-
- V[['alt2']] = (b_groesse * GROESSE.2 + b_entfernung * ENTFERNUNG.2 +
- b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 + b_kultur * KULTURVERANSTALTUNGEN.2 +
- b_umweltbildung * UMWELTBILDUNG.2 + b_toiletten * TOILETTEN.2 +
- b_spiel * SPIEL_SPORT.2 +
- b_pflegeint * PFLEGEINTENSITAET.2 + b_pflegeziele * PFLEGEZIELE.2 + b_beitrag * BEITRAG.2)
-
- V[['alt3']] = (asc + b_groesse * GROESSE.3 + b_entfernung * ENTFERNUNG.3 +
- b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.3 + b_kultur * KULTURVERANSTALTUNGEN.3 +
- b_umweltbildung * UMWELTBILDUNG.3 + b_toiletten * TOILETTEN.3 +
- b_spiel * SPIEL_SPORT.3 +
- b_pflegeint * PFLEGEINTENSITAET.3 + b_pflegeziele * PFLEGEZIELE.3 + b_beitrag * BEITRAG.3)
-
- ### Define settings for MNL model component
- mnl_settings = list(
- alternatives = c(alt1=1, alt2=2, alt3=3),
- avail = 1, # all alternatives are available in every choice
- choiceVar = choice,
- V = V # tell function to use list vector defined above
- #rows = sample==i #not needed, für Frankfurt inkludieren wir alle
- )
-
- ### Compute probabilities using MNL model
- P[['model']] = apollo_mnl(mnl_settings, functionality)
-
- ### Take product across observation for same individual
- P = apollo_panelProd(P, apollo_inputs, functionality)
-
- ### Average across inter-individual draws - nur bei Mixed Logit!
- #P = apollo_avgInterDraws(P, apollo_inputs, functionality)
-
- ### Prepare and return outputs of function
- P = apollo_prepareProb(P, apollo_inputs, functionality)
- return(P)
-}
-
-
-# ################################################################# #
-#### MODEL ESTIMATION ####
-# ################################################################# #
-
-model = apollo_estimate(apollo_beta, apollo_fixed,
- apollo_probabilities, apollo_inputs)
-
-### model estmates speichern?
-ps_model <- model
-save(ps_model, file = "04_CL_PS_F_estimates.RData")
-
-# ################################################################# #
-#### MODEL OUTPUTS ####
-# ################################################################# #
-
-
-### relevante variablen aus Model-Output extrahieren
-z <- length(model$estimate)
-est <- c(model$estimate[1:z-1]*10,model$estimate[z]/10)
-se <- c(model$se[1:z-1]*10,model$se[z]/10)
-t1 <- est-1.64*se
-t2 <- est+1.64*se
-n <- model$nIndivs
-## stattdessen: output in datei speichern, und danach löschen:
-apollo_saveOutput(model, saveOutput_settings = list(printPVal = 1))
-s <- read_csv("Clogit_estimates.csv", show_col_types = FALSE)
-file.remove(c("Clogit_estimates.csv", "Clogit_model.rds", "Clogit_output.txt", "Clogit_iterations.csv"))
-p <- s$`Rob.p-val(0)`/2 # divide by 2 to get the one sided robust p value
-
-
-nParams <- length(model$apollo_beta)
-nFreeParams <- nParams
-if(!is.null(model$apollo_fixed)) nFreeParams <- nFreeParams - length(model$apollo_fixed)
-
-# Tabelle erstellen (Umweg über df nicht unbedingt nötig, aber so ähnlich wie bei LBS)
-df <- as.data.frame(matrix(data=NA,nrow=z*2+8,ncol=2))
-rownames(df) <- dat[,1]
-
-# Schleife fuer Signifikanz-Sterne
-p_wert <- function(p){
- if(p < 0.01){print("***")}
- else{if(p < 0.05){print("**")}
- else{if(p < 0.1){print("*")}
- else{print("")}}}
-}
-
-
-# Tabelle auffuellen, alles auf zwei Nachkommastellen runden
-for(j in 1:z){
- df[j*2-1,1] <- paste0(round(est[j], 2)," [",round(t1[j],2),";",round(t2[j],2),"]",p_wert(p[j]))
- df[j*2,1] <- paste0("(",round(se[j],2),")")
- if(j == z){
- x <-as.numeric(paste0(format(est[j], digits=2, nsmall = 2))) # Koeffizient des Beitrages
- y <- match(TRUE,round(x, 1:20) == x) # Nachkommastellen des Beitragskoeffizienten
- df[j*2-1,1] <- paste0(x," [",format(round(t1[j], digits=y), scientific = FALSE),";",format(round(t2[j], digits=y), scientific = FALSE),"]",p_wert(p[j]))
- df[j*2,1] <- paste0("(",format(round(se[j], digits=y), scientific = FALSE),")")
- }
-}
-rm(j)
-
-
-# Model measures fuer Fusszeile in Tabelle ergaenzen
-df[z*2+1,1] <- n
-df[z*2+2,1] <- round(model$LLStart,4)
-df[z*2+3,1] <- round(model$LL0,4)
-df[z*2+4,1] <- round(model$LLout,4)
-df[z*2+5,1] <- round(1-(model$maximum/model$LL0),4)
-df[z*2+6,1] <- round(1-((model$maximum-nFreeParams)/model$LL0),4)
-df[z*2+7,1] <- round(-2*model$maximum + 2*nFreeParams,2)
-df[z*2+8,1] <- round(-2*model$maximum + nFreeParams*log(model$nObs),2)
-
-dat[,2] <- df[,1]
-
-# parameter speichern?
-# save(apollo_control,database,model,apollo_beta,apollo_fixed,apollo_probabilities,apollo_inputs,
-# est,se,n,file=paste("04_clogit_parameter_sample",i,".RData",sep=""))
-
-
-### Dezimalpunkte in Kommas umwandeln
-dat <- dat %>%
- mutate(across(.cols= c(2:ncol(dat)),.fns = ~str_replace_all(., "\\.", "\\,")))
-
-
-
-# ################################################################# #
-#### Flextable ####
-# ################################################################# #
-
-### Einstellungen fuer Tabellenkopf und Fusszeile
-my_header <- data.frame(col_keys=colnames(dat),
- line1 = c("Model Results - Conditional logit"),
- line2 = c("Attribute","Praeferenz"),
- stringsAsFactors=FALSE)
-
-my_footer <- data.frame(col_keys=colnames(dat),
- line1 = c("*** 0,01 ; ** 0,05 ; * 0,1"),
- line2 = c("Referenzpark: 0 m Entfernung, keine Gemeinschaftsaktivitäten,
- keine Kulturveranstaltungen, keine Umweltbildungsangebote,
- keine Toiletten, keine Spiel- und Sportgeräte, Basispflege,
- Vielfalt und Nutzung sind gleich gewichtet"),
- stringsAsFactors=FALSE)
-
-### graphische Einstellungen fuer Flextable
-flex <- flextable(dat) %>%
- theme_booktabs() %>%
- set_header_df(mapping = my_header, key="col_keys") %>%
- set_footer_df(mapping = my_footer, key="col_keys") %>%
- border(i=2,border.bottom=fp_border(color="black",width=1),part="header") %>%
- fontsize(size=12,i=1,part="header") %>%
- align(align="center",part="header") %>%
- align(align="right",part="footer") %>%
- border(border.top=fp_border(color="black",width=1),part="footer") %>%
- border(i=23,border.top=fp_border(width=1)) %>%
- bold(j=1,part="body") %>%
- bold(i=1:2,part="header") %>%
- font(fontname="Calibri",part="body") %>%
- font(fontname="Calibri",part="header") %>%
- merge_h(part="footer") %>%
- merge_at(i =1:2,j=1,part="body") %>%
- merge_at(i =3:4,j=1,part="body") %>%
- merge_at(i =5:6,j=1,part="body") %>%
- merge_at(i =7:8,j=1,part="body") %>%
- merge_at(i =9:10,j=1,part="body") %>%
- merge_at(i =11:12,j=1,part="body") %>%
- merge_at(i =13:14,j=1,part="body") %>%
- merge_at(i =15:16,j=1,part="body") %>%
- merge_at(i =17:18,j=1,part="body") %>%
- merge_at(i =19:20,j=1,part="body") %>%
- merge_at(i =21:22,j=1,part="body") %>%
- merge_at(i=1,part="header") %>%
- width(j=2,width=2.5) %>%
- width(j=1,width=4)
-
-#----------------------------------------------
-# Tabelle exportieren
-#----------------------------------------------
-
-### Vorschau in R:
-print(flex)
-
-### Oeffnen und Speichern in Word:
-#print(flex,preview="docx")
-#set_prop <- prop_section(page_size = page_size(orient = "landscape"))
-save_as_docx(flex, path = "04_clogit_F_Konfidenzintervall_preference_space.docx")#, pr_section = set_prop)
+######################################################################
+### Projekt : GartenLeistungen II ###
+### Beschreibung : Choice Model mit Apollo-Paket ###
+### Conditional Logit im Preference Space ###
+### Output : Flextable (Wordfile) ###
+### Datum : 11.07.2023 ###
+### R-Version : 4.3.1 ###
+### Apollo Version: 0.2.9 ###
+######################################################################
+
+
+### Environment loeschen
+rm(list=ls())
+
+### Arbeitspfad festlegen
+wd <- "S:/10 GIS/GartenLeistungen/Auswertung GartenLeistungen/R_GL2/pilot"
+setwd(wd)
+
+### Pakete laden (wenn nicht vorhanden, installieren)
+if(!require(pacman)){
+ install.packages("pacman")
+ library(pacman)
+}
+
+p_load(apollo, flextable, magrittr, officer, haven, dplyr, stringr, readr, sjlabelled)
+
+# ################################################################# #
+#### INITIALISE APOLLO AND LOAD DATA ####
+# ################################################################# #
+
+### initialise apollo and core settings
+apollo_initialise()
+apollo_control= list (
+ modelName = "Clogit",
+ modelDescr ="Conditional Logit in preference space",
+ indivID = "ID",
+ mixing = FALSE # True waere Mixed Logit
+)
+
+
+# Dataframe für Flextable erstellen
+dat <- data.frame(matrix(NA,nrow=30,ncol=2))
+
+dat[,1] <- c("asc\nEntstehung eines Referenzparks in der Nachbarschaft", "asc1",
+ "Größe","1",
+ "Entfernung","2",
+ "Gemeinschaftsaktivitäten","Gemeinschaft1",
+ "Kulturveranstaltungen","Kultur1",
+ "Umweltbildungsangebote","Bildung1",
+ "Pflegeintensität\nje Stufe besserer Pflege","3",
+ "Pflegeziele\nmit Vielfalt im Vordergund","4",
+ "Toiletten","toilet1",
+ "Spiel- und Sportgeräte","Spielsport1",
+ "Beitrag\npro 1 €","5",
+ "Anzahl","LL(start)","LL(0)","LL(final)","Rho-square","Adj. Rho-square","AIC","BIC")
+colnames(dat) <- c("Attribute","Praeferenz")
+
+### Datensatz aus Aufbereitungs-Skript laden
+load("02_apollodataF.RData")
+# falls labelled data genutzt wird: (hier für Frankfurt aber nicht der Fall)
+# for (col in names(apollodata_F)) {
+# apollodata_F[[col]] <- as.numeric(val_labels(apollodata_F[[col]]))
+# }
+database <- as.data.frame(apollodata_F)
+database <- database[order(database$ID),]
+#database <- database[-which(is.na(database$choice)),]
+
+# ################################################################# #
+#### DEFINE apollo_beta() ####
+# ################################################################# #
+
+### Startwerte festlegen
+apollo_beta=c(asc = 0,
+ b_groesse = 0,
+ b_entfernung = 0,
+ b_gemeinschaft = 0,
+ b_kultur = 0,
+ b_umweltbildung = 0,
+ b_toiletten = 0,
+ b_spiel = 0,
+ b_pflegeint = 0,
+ b_pflegeziele = 0,
+ b_beitrag = 0)
+
+# ################################################################# #
+#### DEFINE MODEL AND LIKELIHOOD FUNCTION ####
+# ################################################################# #
+
+### keine Parameter fix halten
+apollo_fixed = c()
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+ V[['alt1']] = (b_groesse * GROESSE.1 + b_entfernung * ENTFERNUNG.1 +
+ b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 + b_kultur * KULTURVERANSTALTUNGEN.1 +
+ b_umweltbildung * UMWELTBILDUNG.1 + b_toiletten * TOILETTEN.1 +
+ b_spiel * SPIEL_SPORT.1 +
+ b_pflegeint * PFLEGEINTENSITAET.1 + b_pflegeziele * PFLEGEZIELE.1 + b_beitrag * BEITRAG.1)
+
+ V[['alt2']] = (b_groesse * GROESSE.2 + b_entfernung * ENTFERNUNG.2 +
+ b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 + b_kultur * KULTURVERANSTALTUNGEN.2 +
+ b_umweltbildung * UMWELTBILDUNG.2 + b_toiletten * TOILETTEN.2 +
+ b_spiel * SPIEL_SPORT.2 +
+ b_pflegeint * PFLEGEINTENSITAET.2 + b_pflegeziele * PFLEGEZIELE.2 + b_beitrag * BEITRAG.2)
+
+ V[['alt3']] = (asc + b_groesse * GROESSE.3 + b_entfernung * ENTFERNUNG.3 +
+ b_gemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.3 + b_kultur * KULTURVERANSTALTUNGEN.3 +
+ b_umweltbildung * UMWELTBILDUNG.3 + b_toiletten * TOILETTEN.3 +
+ b_spiel * SPIEL_SPORT.3 +
+ b_pflegeint * PFLEGEINTENSITAET.3 + b_pflegeziele * PFLEGEZIELE.3 + b_beitrag * BEITRAG.3)
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3),
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = choice,
+ V = V # tell function to use list vector defined above
+ #rows = sample==i #not needed, für Frankfurt inkludieren wir alle
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+ ### Average across inter-individual draws - nur bei Mixed Logit!
+ #P = apollo_avgInterDraws(P, apollo_inputs, functionality)
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+}
+
+
+# ################################################################# #
+#### MODEL ESTIMATION ####
+# ################################################################# #
+
+model = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs)
+
+### model estmates speichern?
+ps_model <- model
+save(ps_model, file = "04_CL_PS_F_estimates.RData")
+
+# ################################################################# #
+#### MODEL OUTPUTS ####
+# ################################################################# #
+
+
+### relevante variablen aus Model-Output extrahieren
+z <- length(model$estimate)
+est <- c(model$estimate[1:z-1]*10,model$estimate[z]/10)
+se <- c(model$se[1:z-1]*10,model$se[z]/10)
+t1 <- est-1.64*se
+t2 <- est+1.64*se
+n <- model$nIndivs
+## stattdessen: output in datei speichern, und danach löschen:
+apollo_saveOutput(model, saveOutput_settings = list(printPVal = 1))
+s <- read_csv("Clogit_estimates.csv", show_col_types = FALSE)
+file.remove(c("Clogit_estimates.csv", "Clogit_model.rds", "Clogit_output.txt", "Clogit_iterations.csv"))
+p <- s$`Rob.p-val(0)`/2 # divide by 2 to get the one sided robust p value
+
+
+nParams <- length(model$apollo_beta)
+nFreeParams <- nParams
+if(!is.null(model$apollo_fixed)) nFreeParams <- nFreeParams - length(model$apollo_fixed)
+
+# Tabelle erstellen (Umweg über df nicht unbedingt nötig, aber so ähnlich wie bei LBS)
+df <- as.data.frame(matrix(data=NA,nrow=z*2+8,ncol=2))
+rownames(df) <- dat[,1]
+
+# Schleife fuer Signifikanz-Sterne
+p_wert <- function(p){
+ if(p < 0.01){print("***")}
+ else{if(p < 0.05){print("**")}
+ else{if(p < 0.1){print("*")}
+ else{print("")}}}
+}
+
+
+# Tabelle auffuellen, alles auf zwei Nachkommastellen runden
+for(j in 1:z){
+ df[j*2-1,1] <- paste0(round(est[j], 2)," [",round(t1[j],2),";",round(t2[j],2),"]",p_wert(p[j]))
+ df[j*2,1] <- paste0("(",round(se[j],2),")")
+ if(j == z){
+ x <-as.numeric(paste0(format(est[j], digits=2, nsmall = 2))) # Koeffizient des Beitrages
+ y <- match(TRUE,round(x, 1:20) == x) # Nachkommastellen des Beitragskoeffizienten
+ df[j*2-1,1] <- paste0(x," [",format(round(t1[j], digits=y), scientific = FALSE),";",format(round(t2[j], digits=y), scientific = FALSE),"]",p_wert(p[j]))
+ df[j*2,1] <- paste0("(",format(round(se[j], digits=y), scientific = FALSE),")")
+ }
+}
+rm(j)
+
+
+# Model measures fuer Fusszeile in Tabelle ergaenzen
+df[z*2+1,1] <- n
+df[z*2+2,1] <- round(model$LLStart,4)
+df[z*2+3,1] <- round(model$LL0,4)
+df[z*2+4,1] <- round(model$LLout,4)
+df[z*2+5,1] <- round(1-(model$maximum/model$LL0),4)
+df[z*2+6,1] <- round(1-((model$maximum-nFreeParams)/model$LL0),4)
+df[z*2+7,1] <- round(-2*model$maximum + 2*nFreeParams,2)
+df[z*2+8,1] <- round(-2*model$maximum + nFreeParams*log(model$nObs),2)
+
+dat[,2] <- df[,1]
+
+# parameter speichern?
+# save(apollo_control,database,model,apollo_beta,apollo_fixed,apollo_probabilities,apollo_inputs,
+# est,se,n,file=paste("04_clogit_parameter_sample",i,".RData",sep=""))
+
+
+### Dezimalpunkte in Kommas umwandeln
+dat <- dat %>%
+ mutate(across(.cols= c(2:ncol(dat)),.fns = ~str_replace_all(., "\\.", "\\,")))
+
+
+
+# ################################################################# #
+#### Flextable ####
+# ################################################################# #
+
+### Einstellungen fuer Tabellenkopf und Fusszeile
+my_header <- data.frame(col_keys=colnames(dat),
+ line1 = c("Model Results - Conditional logit"),
+ line2 = c("Attribute","Praeferenz"),
+ stringsAsFactors=FALSE)
+
+my_footer <- data.frame(col_keys=colnames(dat),
+ line1 = c("*** 0,01 ; ** 0,05 ; * 0,1"),
+ line2 = c("Referenzpark: 0 m Entfernung, keine Gemeinschaftsaktivitäten,
+ keine Kulturveranstaltungen, keine Umweltbildungsangebote,
+ keine Toiletten, keine Spiel- und Sportgeräte, Basispflege,
+ Vielfalt und Nutzung sind gleich gewichtet"),
+ stringsAsFactors=FALSE)
+
+### graphische Einstellungen fuer Flextable
+flex <- flextable(dat) %>%
+ theme_booktabs() %>%
+ set_header_df(mapping = my_header, key="col_keys") %>%
+ set_footer_df(mapping = my_footer, key="col_keys") %>%
+ border(i=2,border.bottom=fp_border(color="black",width=1),part="header") %>%
+ fontsize(size=12,i=1,part="header") %>%
+ align(align="center",part="header") %>%
+ align(align="right",part="footer") %>%
+ border(border.top=fp_border(color="black",width=1),part="footer") %>%
+ border(i=23,border.top=fp_border(width=1)) %>%
+ bold(j=1,part="body") %>%
+ bold(i=1:2,part="header") %>%
+ font(fontname="Calibri",part="body") %>%
+ font(fontname="Calibri",part="header") %>%
+ merge_h(part="footer") %>%
+ merge_at(i =1:2,j=1,part="body") %>%
+ merge_at(i =3:4,j=1,part="body") %>%
+ merge_at(i =5:6,j=1,part="body") %>%
+ merge_at(i =7:8,j=1,part="body") %>%
+ merge_at(i =9:10,j=1,part="body") %>%
+ merge_at(i =11:12,j=1,part="body") %>%
+ merge_at(i =13:14,j=1,part="body") %>%
+ merge_at(i =15:16,j=1,part="body") %>%
+ merge_at(i =17:18,j=1,part="body") %>%
+ merge_at(i =19:20,j=1,part="body") %>%
+ merge_at(i =21:22,j=1,part="body") %>%
+ merge_at(i=1,part="header") %>%
+ width(j=2,width=2.5) %>%
+ width(j=1,width=4)
+
+#----------------------------------------------
+# Tabelle exportieren
+#----------------------------------------------
+
+### Vorschau in R:
+print(flex)
+
+### Oeffnen und Speichern in Word:
+#print(flex,preview="docx")
+#set_prop <- prop_section(page_size = page_size(orient = "landscape"))
+save_as_docx(flex, path = "04_clogit_F_Konfidenzintervall_preference_space.docx")#, pr_section = set_prop)
diff --git a/prior_est/functions/apollo_help_functions.R b/prior_est/functions/apollo_help_functions.R
index 81ad31e04c1b93b5863b90c16d05347c412c1423..c92478aa08c2ef77f773f0b4246e727f593f11fd 100644
--- a/prior_est/functions/apollo_help_functions.R
+++ b/prior_est/functions/apollo_help_functions.R
@@ -1,248 +1,248 @@
-## functions related to apollo
-
-##### LR test modified function ####
-
-apollo_lrTest_mod <-function (model1, model2){
- modelNames = list()
- LL = list()
- obs = list()
- k = list()
- inputs = list(model1, model2)
- for (i in 1:2) {
- modeluse = inputs[[i]]
- if (is.character(modeluse)) {
- filename = paste(paste(modeluse, "_output.txt", sep = ""))
- if (!file.exists(filename))
- stop("File ", filename, " not found!")
- lines = tryCatch(readLines(filename), warning = function(w) x = FALSE,
- error = function(e) x = FALSE)
- if (is.logical(lines) && lines == FALSE)
- stop("Could not open file ", filename)
- id <- grepl(paste0("Model name"), lines)
- value = lines[which(id)]
- position = gregexpr(pattern = ":", value)[[1]][1]
- modelNames[[i]] = (substr(value, position + 2, nchar(value)))
- id <- grepl(paste0("Number of modelled outcomes"),
- lines)
- if (any(id)) {
- value = lines[which(id)]
- }
- else {
- stop("Number of observations not found in ",
- filename)
- }
- position = gregexpr(pattern = ":", value)[[1]][1]
- obs[[i]] = as.double(substr(value, position + 1,
- nchar(value)))
- id1 <- grepl("LL\\(final, whole model)", lines)
- id2 <- grepl("LL\\(final)", lines)
- if (any(id1)) {
- value = lines[which(id1)]
- }
- else if (any(id2)) {
- value = lines[which(id2)]
- }
- else {
- stop("No final LL found in ", filename)
- }
- position = gregexpr(pattern = ":", value)[[1]][1]
- LL[[i]] = as.double(substr(value, position + 1, nchar(value)))
- id <- grepl("Estimated parameters", lines)
- if (any(id)) {
- value = lines[which(id)]
- }
- else {
- stop("Number of estimated parameters not found in ",
- filename)
- }
- position = gregexpr(pattern = ":", value)[[1]][1]
- k[[i]] = as.double(substr(value, position + 1, nchar(value)))
- }
- else {
- modelNames[[i]] = modeluse$apollo_control$modelName
- if (is.null(modeluse$maximum)) {
- stop("No LL found in ", paste0(modeluse))
- }
- else {
- LL[[i]] = modeluse$maximum
- }
- if (is.null(modeluse$nObs) || anyNA(modeluse$nObs[1])) {
- stop("Number of observations not found in ",
- paste0(modeluse))
- }
- else {
- obs[[i]] = modeluse$nObs[1]
- }
- nParams <- length(modeluse$apollo_beta)
- nFreeParams <- nParams
- if (!is.null(modeluse$apollo_fixed))
- nFreeParams <- nFreeParams - length(modeluse$apollo_fixed)
- k[[i]] = nFreeParams
- }
- }
- if (obs[[1]] != obs[[2]])
- stop("The two models to be compared were not estimated on the same number of observations. A likelihood ratio test cannot be used!")
- if (k[[1]] == k[[2]])
- stop("The two models to be compared have the same number of parameters. A likelihood ratio test cannot be used!")
- if (((k[[1]] - k[[2]]) * (LL[[1]] - LL[[2]])) < 0)
- stop("The model with more parameters does not have a better log-likelihood. A likelihood ratio test cannot be used!")
- if (LL[[2]] < LL[[1]]) {
- apollo_print(paste0("The order of your two models will be reversed in the output as model 1 has better fit than model 2."))
- apollo_print("\n")
- LL = LL[c(2, 1)]
- k = k[c(2, 1)]
- inputs = inputs[c(2, 1)]
- modelNames = modelNames[c(2, 1)]
- }
- output = matrix(0, nrow = 3, ncol = 2)
- output[1:2, 1] = round(unlist(LL), 2)
- output[1:2, 2] = round(unlist(k), 2)
- output[3, ] = output[2, ] - output[1, ]
- colnames(output) = c("LL", "par")
- rownames(output) = c(unlist(modelNames), "Difference")
- LR_test_value = 2 * output[3, 1]
- df = output[3, 2]
- p = stats::pchisq(LR_test_value, df, lower.tail = FALSE)
- print(output)
- cat("\nLikelihood ratio test-value: ", round(LR_test_value,
- 2), "\n")
- cat("Degrees of freedom: ", df, "\n")
- cat("Likelihood ratio test p-value: ", formatC(p), "\n")
- return(results = data.frame(LR = LR_test_value , df = as.character(as.integer(df)) , pvalue =p))
-}
-
-
-
-###### Regression Tables from Apollo Output with kable ####
-
-# make.apollo.table.kable <- function(modelname, cols , colnames)
-#
-# model <- modelname
-#
-# cols <- length(colnames) + 3
-#
-# # Dataframe fuer Flextable erstellen
-# dat <- data.frame(matrix(NA,nrow=29,ncol=cols))
-# dat[,1] <- c("asc Gemeinschaftsgarten", "asc1", "asc Kleingarten", "asc3","Größe\npro 1000 m²","1",
-# "Entfernung\npro 1000 m","2","Gemeinschaftsaktivitäten","Gemeinschaft1",
-# "Kulturveranstaltungen","Kultur1","Umweltbildungsangebote","Bildung1",
-# "Zugang\nje zusätzlich geöffnetem Tag","3", "Gestaltung\nje Stufe naturnäher","4","Beitrag\npro 1 €","5",
-# "LL(start)","LL(final)","Rho-square","Adj. Rho-square","AIC","BIC",
-# "Anzahl Beobachtungen","Anzahl Befragte","Anzahl Parameter")
-# colnames(dat) <- c("","main effects (mean)","main effects (standard deviations)","TIME")
-#
-# ### relevante variablen aus Model-Output extrahieren
-# z <- length(model$estimate)
-# est <- model$estimate
-#
-# est[grepl("sd_b",names(est))] <- abs(est[grepl("sd_b",names(est))]) # make sure all standard deviations are positive
-#
-# names(est[c(10,20)])
-#
-# timesten <- names(est[grepl("groesse",names(est))])
-# timeshundred<-names(est[grepl("gemeinschaft|klein|entfernung|kultur|umwelt|zugang|gestaltung",names(est))])
-#
-#
-#
-# est[timeshundred] <- est[timeshundred]*100
-# est[timesten] <- est[timesten]*10 # groesse nur mit 10 skalieren
-#
-#
-# se <- model$se
-# se[timeshundred] <- se[timeshundred]*100
-# se[c(3,13,23,32,41,50,59)] <- se[c(3,13,23,32,41,50,59)]*10
-# est[c("mean_b_groesse","sd_b_groesse")] <- est[c("mean_b_groesse","sd_b_groesse")]*10 ## NEU! Groesse bei main effects noch mal mit 10 (also 100) skalieren
-#
-# # yearly fee Koeffizienten anpassen
-# est[c("mean_b_beitrag" , "sd_b_beitrag")] <- -exp(est[c("mean_b_beitrag" , "sd_b_beitrag")])/100 ## NEU! Beitrag wieder zurückskalieren
-# se[c("mean_b_beitrag" , "sd_b_beitrag")] <- exp(se[c("mean_b_beitrag" , "sd_b_beitrag")])/100
-# se <- abs(se) # alle standard deviations positiv
-# s <- summary(model)
-# p <- s$estimate[,4]
-# n <- model$nIndivs
-#
-# nParams <- length(model$apollo_beta)
-# nFreeParams <- nParams
-# if(!is.null(model$apollo_fixed)) nFreeParams <- nFreeParams - length(model$apollo_fixed)
-#
-# # Schleife fuer Signifikanz-Sterne
-# p_wert <- function(p){
-# if(p < 0.01){print("***")}
-# else{if(p < 0.05){print("**")}
-# else{if(p < 0.1){print("*")}
-# else{print("")}}}}
-#
-# # Tabelle erstellen
-# df <- as.data.frame(matrix(data=NA,nrow=29,ncol=3))
-# rownames(df) <- dat[,1]
-#
-#
-# for (column in cols-1){
-#
-# # Tabelle auffuellen
-# for(j in 1:10){# Anzahl der Parameter
-# df[j*2-1,1] <- paste(round(est[j],2),p_wert(p[j]))
-# df[j*2,1] <- paste0("(",round(se[j],2),")")
-# df[j*2-1,2] <- paste(round(est[j+10],2),p_wert(p[j+10]))
-# df[j*2,2] <- paste0("(",round(se[j+10],2),")")
-# }
-# for(j in 1:9){
-# df[j*2-1,3] <- paste(round(est[j+20],2),p_wert(p[j+20]))
-# df[j*2,3] <- paste0("(",round(se[j+20],2),")")
-#
-# }
-#
-#
-# }
-#
-# # Model measures fuer Fusszeile in Tabelle ergaenzen
-# df[21,1] <- round(model$LLStart,3)
-# #df[23,1] <- round(model$LL0,3)
-# df[22,1] <- round(model$LLout,3)
-# df[23,1] <- round(1-(model$maximum/model$LL0),3)
-# df[24,1] <- round(1-((model$maximum-nFreeParams)/model$LL0),3)
-# df[25,1] <- round(-2*model$maximum + 2*nFreeParams,3)
-# df[26,1] <- round(-2*model$maximum + nFreeParams*log(model$nObs),3)
-# df[27,1] <- model$nObs
-# df[28,1] <- model$nIndivs
-# df[29,1] <- nFreeParams
-#
-# dat[,2:4] <- df[,1:3]
-#
-# # rownames
-# rownames(dat) <- NULL
-# dat[,1] <- c("ASC community garden", "", "ASC allotment garden", "","size (per 1,000 m²)","",
-# "distance (per 1,000 m)","","community activities","",
-# "cultural events","","environmental education","",
-# "access (per open day)","", " near-natural garden layout","",
-# "yearly fee (per 1 Euro)","",
-# "Log-likelihood at convergence", "Log-likelihood at constant(s)","Rho-square",
-# "Adj. Rho-square","AIC","BIC","Number of observations", "Number of respondents", "Number of parameters")
-#
-# # colnames
-# colnames(dat) <- c("","main effects (mean)","main effects (standard deviations)",# !!! NEU: Spalte drei neu eingefügt
-# "TIME")
-#
-# # replace NA
-# dat[is.na(dat)] <- ""
-#
-#
-# return(dat)
-#}
-
-
-###### Regression Tables from Apollo Output with texreg ####
-
-## this function can be used to make a publication like table from models estimated with gmnl
-
-make.apollo.table <- function(model) {
- l_table <- apollo_modelOutput(model,modelOutput_settings = list(printPVal=T) )
-
- createTexreg( coef = l_table[,"Estimate"] ,
- coef.names = rownames(l_table) ,
- se = l_table[,"Rob.s.e."],
- pvalues = l_table[,7] ,
- gof.names = c( "Log-likelihood(NULL)" , "Log-likelihood at convergence" , "Number of observations" , "Number of respondents") , gof.decimal = c(TRUE,TRUE,FALSE,FALSE) ,
- gof = c(model$LL0,model$LLout,model$nObs,model$nIndivs)
- )
+## functions related to apollo
+
+##### LR test modified function ####
+
+apollo_lrTest_mod <-function (model1, model2){
+ modelNames = list()
+ LL = list()
+ obs = list()
+ k = list()
+ inputs = list(model1, model2)
+ for (i in 1:2) {
+ modeluse = inputs[[i]]
+ if (is.character(modeluse)) {
+ filename = paste(paste(modeluse, "_output.txt", sep = ""))
+ if (!file.exists(filename))
+ stop("File ", filename, " not found!")
+ lines = tryCatch(readLines(filename), warning = function(w) x = FALSE,
+ error = function(e) x = FALSE)
+ if (is.logical(lines) && lines == FALSE)
+ stop("Could not open file ", filename)
+ id <- grepl(paste0("Model name"), lines)
+ value = lines[which(id)]
+ position = gregexpr(pattern = ":", value)[[1]][1]
+ modelNames[[i]] = (substr(value, position + 2, nchar(value)))
+ id <- grepl(paste0("Number of modelled outcomes"),
+ lines)
+ if (any(id)) {
+ value = lines[which(id)]
+ }
+ else {
+ stop("Number of observations not found in ",
+ filename)
+ }
+ position = gregexpr(pattern = ":", value)[[1]][1]
+ obs[[i]] = as.double(substr(value, position + 1,
+ nchar(value)))
+ id1 <- grepl("LL\\(final, whole model)", lines)
+ id2 <- grepl("LL\\(final)", lines)
+ if (any(id1)) {
+ value = lines[which(id1)]
+ }
+ else if (any(id2)) {
+ value = lines[which(id2)]
+ }
+ else {
+ stop("No final LL found in ", filename)
+ }
+ position = gregexpr(pattern = ":", value)[[1]][1]
+ LL[[i]] = as.double(substr(value, position + 1, nchar(value)))
+ id <- grepl("Estimated parameters", lines)
+ if (any(id)) {
+ value = lines[which(id)]
+ }
+ else {
+ stop("Number of estimated parameters not found in ",
+ filename)
+ }
+ position = gregexpr(pattern = ":", value)[[1]][1]
+ k[[i]] = as.double(substr(value, position + 1, nchar(value)))
+ }
+ else {
+ modelNames[[i]] = modeluse$apollo_control$modelName
+ if (is.null(modeluse$maximum)) {
+ stop("No LL found in ", paste0(modeluse))
+ }
+ else {
+ LL[[i]] = modeluse$maximum
+ }
+ if (is.null(modeluse$nObs) || anyNA(modeluse$nObs[1])) {
+ stop("Number of observations not found in ",
+ paste0(modeluse))
+ }
+ else {
+ obs[[i]] = modeluse$nObs[1]
+ }
+ nParams <- length(modeluse$apollo_beta)
+ nFreeParams <- nParams
+ if (!is.null(modeluse$apollo_fixed))
+ nFreeParams <- nFreeParams - length(modeluse$apollo_fixed)
+ k[[i]] = nFreeParams
+ }
+ }
+ if (obs[[1]] != obs[[2]])
+ stop("The two models to be compared were not estimated on the same number of observations. A likelihood ratio test cannot be used!")
+ if (k[[1]] == k[[2]])
+ stop("The two models to be compared have the same number of parameters. A likelihood ratio test cannot be used!")
+ if (((k[[1]] - k[[2]]) * (LL[[1]] - LL[[2]])) < 0)
+ stop("The model with more parameters does not have a better log-likelihood. A likelihood ratio test cannot be used!")
+ if (LL[[2]] < LL[[1]]) {
+ apollo_print(paste0("The order of your two models will be reversed in the output as model 1 has better fit than model 2."))
+ apollo_print("\n")
+ LL = LL[c(2, 1)]
+ k = k[c(2, 1)]
+ inputs = inputs[c(2, 1)]
+ modelNames = modelNames[c(2, 1)]
+ }
+ output = matrix(0, nrow = 3, ncol = 2)
+ output[1:2, 1] = round(unlist(LL), 2)
+ output[1:2, 2] = round(unlist(k), 2)
+ output[3, ] = output[2, ] - output[1, ]
+ colnames(output) = c("LL", "par")
+ rownames(output) = c(unlist(modelNames), "Difference")
+ LR_test_value = 2 * output[3, 1]
+ df = output[3, 2]
+ p = stats::pchisq(LR_test_value, df, lower.tail = FALSE)
+ print(output)
+ cat("\nLikelihood ratio test-value: ", round(LR_test_value,
+ 2), "\n")
+ cat("Degrees of freedom: ", df, "\n")
+ cat("Likelihood ratio test p-value: ", formatC(p), "\n")
+ return(results = data.frame(LR = LR_test_value , df = as.character(as.integer(df)) , pvalue =p))
+}
+
+
+
+###### Regression Tables from Apollo Output with kable ####
+
+# make.apollo.table.kable <- function(modelname, cols , colnames)
+#
+# model <- modelname
+#
+# cols <- length(colnames) + 3
+#
+# # Dataframe fuer Flextable erstellen
+# dat <- data.frame(matrix(NA,nrow=29,ncol=cols))
+# dat[,1] <- c("asc Gemeinschaftsgarten", "asc1", "asc Kleingarten", "asc3","Größe\npro 1000 m²","1",
+# "Entfernung\npro 1000 m","2","Gemeinschaftsaktivitäten","Gemeinschaft1",
+# "Kulturveranstaltungen","Kultur1","Umweltbildungsangebote","Bildung1",
+# "Zugang\nje zusätzlich geöffnetem Tag","3", "Gestaltung\nje Stufe naturnäher","4","Beitrag\npro 1 €","5",
+# "LL(start)","LL(final)","Rho-square","Adj. Rho-square","AIC","BIC",
+# "Anzahl Beobachtungen","Anzahl Befragte","Anzahl Parameter")
+# colnames(dat) <- c("","main effects (mean)","main effects (standard deviations)","TIME")
+#
+# ### relevante variablen aus Model-Output extrahieren
+# z <- length(model$estimate)
+# est <- model$estimate
+#
+# est[grepl("sd_b",names(est))] <- abs(est[grepl("sd_b",names(est))]) # make sure all standard deviations are positive
+#
+# names(est[c(10,20)])
+#
+# timesten <- names(est[grepl("groesse",names(est))])
+# timeshundred<-names(est[grepl("gemeinschaft|klein|entfernung|kultur|umwelt|zugang|gestaltung",names(est))])
+#
+#
+#
+# est[timeshundred] <- est[timeshundred]*100
+# est[timesten] <- est[timesten]*10 # groesse nur mit 10 skalieren
+#
+#
+# se <- model$se
+# se[timeshundred] <- se[timeshundred]*100
+# se[c(3,13,23,32,41,50,59)] <- se[c(3,13,23,32,41,50,59)]*10
+# est[c("mean_b_groesse","sd_b_groesse")] <- est[c("mean_b_groesse","sd_b_groesse")]*10 ## NEU! Groesse bei main effects noch mal mit 10 (also 100) skalieren
+#
+# # yearly fee Koeffizienten anpassen
+# est[c("mean_b_beitrag" , "sd_b_beitrag")] <- -exp(est[c("mean_b_beitrag" , "sd_b_beitrag")])/100 ## NEU! Beitrag wieder zurückskalieren
+# se[c("mean_b_beitrag" , "sd_b_beitrag")] <- exp(se[c("mean_b_beitrag" , "sd_b_beitrag")])/100
+# se <- abs(se) # alle standard deviations positiv
+# s <- summary(model)
+# p <- s$estimate[,4]
+# n <- model$nIndivs
+#
+# nParams <- length(model$apollo_beta)
+# nFreeParams <- nParams
+# if(!is.null(model$apollo_fixed)) nFreeParams <- nFreeParams - length(model$apollo_fixed)
+#
+# # Schleife fuer Signifikanz-Sterne
+# p_wert <- function(p){
+# if(p < 0.01){print("***")}
+# else{if(p < 0.05){print("**")}
+# else{if(p < 0.1){print("*")}
+# else{print("")}}}}
+#
+# # Tabelle erstellen
+# df <- as.data.frame(matrix(data=NA,nrow=29,ncol=3))
+# rownames(df) <- dat[,1]
+#
+#
+# for (column in cols-1){
+#
+# # Tabelle auffuellen
+# for(j in 1:10){# Anzahl der Parameter
+# df[j*2-1,1] <- paste(round(est[j],2),p_wert(p[j]))
+# df[j*2,1] <- paste0("(",round(se[j],2),")")
+# df[j*2-1,2] <- paste(round(est[j+10],2),p_wert(p[j+10]))
+# df[j*2,2] <- paste0("(",round(se[j+10],2),")")
+# }
+# for(j in 1:9){
+# df[j*2-1,3] <- paste(round(est[j+20],2),p_wert(p[j+20]))
+# df[j*2,3] <- paste0("(",round(se[j+20],2),")")
+#
+# }
+#
+#
+# }
+#
+# # Model measures fuer Fusszeile in Tabelle ergaenzen
+# df[21,1] <- round(model$LLStart,3)
+# #df[23,1] <- round(model$LL0,3)
+# df[22,1] <- round(model$LLout,3)
+# df[23,1] <- round(1-(model$maximum/model$LL0),3)
+# df[24,1] <- round(1-((model$maximum-nFreeParams)/model$LL0),3)
+# df[25,1] <- round(-2*model$maximum + 2*nFreeParams,3)
+# df[26,1] <- round(-2*model$maximum + nFreeParams*log(model$nObs),3)
+# df[27,1] <- model$nObs
+# df[28,1] <- model$nIndivs
+# df[29,1] <- nFreeParams
+#
+# dat[,2:4] <- df[,1:3]
+#
+# # rownames
+# rownames(dat) <- NULL
+# dat[,1] <- c("ASC community garden", "", "ASC allotment garden", "","size (per 1,000 m²)","",
+# "distance (per 1,000 m)","","community activities","",
+# "cultural events","","environmental education","",
+# "access (per open day)","", " near-natural garden layout","",
+# "yearly fee (per 1 Euro)","",
+# "Log-likelihood at convergence", "Log-likelihood at constant(s)","Rho-square",
+# "Adj. Rho-square","AIC","BIC","Number of observations", "Number of respondents", "Number of parameters")
+#
+# # colnames
+# colnames(dat) <- c("","main effects (mean)","main effects (standard deviations)",# !!! NEU: Spalte drei neu eingefügt
+# "TIME")
+#
+# # replace NA
+# dat[is.na(dat)] <- ""
+#
+#
+# return(dat)
+#}
+
+
+###### Regression Tables from Apollo Output with texreg ####
+
+## this function can be used to make a publication like table from models estimated with gmnl
+
+make.apollo.table <- function(model) {
+ l_table <- apollo_modelOutput(model,modelOutput_settings = list(printPVal=T) )
+
+ createTexreg( coef = l_table[,"Estimate"] ,
+ coef.names = rownames(l_table) ,
+ se = l_table[,"Rob.s.e."],
+ pvalues = l_table[,7] ,
+ gof.names = c( "Log-likelihood(NULL)" , "Log-likelihood at convergence" , "Number of observations" , "Number of respondents") , gof.decimal = c(TRUE,TRUE,FALSE,FALSE) ,
+ gof = c(model$LL0,model$LLout,model$nObs,model$nIndivs)
+ )
}
\ No newline at end of file
diff --git a/prior_est/scripts/data_cleaning.R b/prior_est/scripts/data_cleaning.R
index d45c478dc7ea4ce181a2e721652dd12ae8236ea4..bc9fbb6940eb2ba307e14c31d820399659f0bb39 100644
--- a/prior_est/scripts/data_cleaning.R
+++ b/prior_est/scripts/data_cleaning.R
@@ -1,86 +1,86 @@
-######################################################################
-### Project : Information processing in stated preference surveys
-### Description : Data cleaning and preparation
-### Date : 12.08.2021
-### Version : 2
-
-######################################################################
-
-
-
-#====================================
-# Prepare data
-#====================================
-
-
-data <- read_sav("prior_est/data/data_raw.sav" ) %>% ### import data
- filter(compl==1,sample!=3) %>% ## sample 3 is another choice exepriment not relevant here
- mutate(stuttgart=if_else(q2_1<=70629&q2_1>70000,1,0)) %>% ## postal codes to city
-select(-c('userid', 'studNumb', 'Title', 'Client', 'compl', 'interrupt', 'dDay', 'dMonth', 'dYear', 'startTime', 'startTimel', 'endTime', 'city', 'device_str', 'osName_str', 'osVersion_str', 'agentName_str', 'agentVersion_str', 'devManu_str', 'devName_str', 'vpWidth_str', 'vpHeight_str', 'flashVersion_str', 'useragent_str'))%>%
- mutate(across(matches("^q6_[0-9]$") , ~ replace_na(., 3) ), ## NA in to 3
- across(c("q7_7_a","q7_7_c","q7_7_d","q7_7_e","q7_7_f","q7_7_i"), ~ recode(as.numeric(.), '1'=0,'2'=1,'3'=0, .missing = 0)) , #recode quiz
- across(c("q7_7_b","q7_7_g","q7_7_h") , ~ recode(as.numeric(.),'1'=1,'2'=0,'3'=0 , .missing = 0) ) #recode quiz
- ) %>%
- mutate(korrekt=rowSums(select(.,c("q7_7_a" , "q7_7_b" ,"q7_7_c" ,"q7_7_e", "q7_7_f", "q7_7_g", "q7_7_h", "q7_7_i")),na.rm=TRUE) # number of correct answers in quiz
- ) %>%
- {. ->> dataN } %>% #here is save
- select(c('serial', 'korrekt' , 'sample', 'sample2', 'q3_3_zeit', 'q6_blck', 'q6_set_1', 'q6_set_2', 'q6_set_3', 'q6_set_4', 'q6_set_5', 'q6_set_6', 'q6_set_7', 'q6_set_8', 'q6_1', 'q6_2', 'q6_3', 'q6_4', 'q6_5', 'q6_6', 'q6_7', 'q6_8')) %>%
- {. ->> widedata } %>% #here is save
-
-
-rename_with(function(x) c("ID", "korrekt" ,"sample","subsample","Zeit","block","set_1","set_2","set_3","set_4","set_5","set_6","set_7","set_8","choice_1","choice_2","choice_3","choice_4","choice_5","choice_6","choice_7","choice_8") ) %>% ### rename variables
- as.data.frame()%>%
-reshape(varying=7:22, timevar="choiceset", idvar="ID", direction="long", sep="_") %>% # to long format for apollo %>%
-arrange(ID) %>%
- {. ->> apollodata }
-
-#### Prepare design and merge with data
-
-design12<-read_excel("prior_est/data/design_hauptstudie.xlsx") %>%
- mutate(across(where(is.character) , ~ tidyr::replace_na(., "0") ) ,
- L_GROESSE=GROESSE, GROESSE = as.numeric(str_remove_all(GROESSE,"[^0-9]"))/1000 ,
- L_ENTFERNUNG = ENTFERNUNG , ENTFERNUNG=as.numeric(str_remove(ENTFERNUNG,"[^0-9].*?(\\((.*?)\\))")) , ENTFERNUNG=if_else(ENTFERNUNG>99,ENTFERNUNG/1000,ENTFERNUNG) ,
- L_GEMEINSCHAFTSAKTIVITAETEN=GEMEINSCHAFTSAKTIVITAETEN , GEMEINSCHAFTSAKTIVITAETEN=recode(GEMEINSCHAFTSAKTIVITAETEN, "Gemeinschaftsaktivitäten" = 1 , .default=0),
- L_KULTURVERANSTALTUNGEN=KULTURVERANSTALTUNGEN , KULTURVERANSTALTUNGEN=recode(KULTURVERANSTALTUNGEN, "Kulturveranstaltungen" = 1 , .default=0) ,
- L_UMWELTBILDUNG=UMWELTBILDUNG , UMWELTBILDUNG=recode(UMWELTBILDUNG, "Umweltbildung" = 1 , .default=0),
- L_GESTALTUNG=GESTALTUNG , GESTALTUNG=recode(GESTALTUNG, "eher geordnet" = 0 , "eher naturnah" = 1 , .default=999),
- L_ZUGANG=ZUGANG, ZUGANG = as.numeric(str_remove_all(ZUGANG,"[^0-9]"))-7,
- L_BEITRAG=BEITRAG, BEITRAG = as.numeric(str_remove_all(BEITRAG,"[^0-9]"))/10
- )%>% ### NAs in 0 umwandeln
- as.data.frame() %>%
- reshape(timevar = "alt", idvar = "choice_set", direction = "wide" ) %>% ## daten in wide format
- mutate(GROESSE.3=0, ENTFERNUNG.3=0,GEMEINSCHAFTSAKTIVITAETEN.3=0, KULTURVERANSTALTUNGEN.3=0, UMWELTBILDUNG.3=0, GESTALTUNG.3=0, ZUGANG.3=0, BEITRAG.3=0) %>%
- rename(set=choice_set) %>% {. ->> designforsim } %>%
-right_join(data, by="set") %>%
- arrange(ID,set) %>%
- mutate(choice=as.numeric(choice),
- Zeit=Zeit/10 #for time models
- ) %>%
- select(!matches("Block.[1-2]")) %>%
- relocate(ID,sample, subsample, block, Zeit, choiceset, set, choice) %>%
- relocate(starts_with("L_"), .after = last_col())
-
-
-#data for apollo
-
-
-## create means by treatment for time and knowledge questions, to be used later on in time and knowledge interacting choice models.
-
-means <- design12 %>%
- group_by(subsample) %>%
- summarize(Tmean_Zeit=mean(Zeit) , Tmean_knowl =mean(korrekt))
-
-
-database <- design12 %>%
- select(-starts_with("L_"))%>%
- left_join(means, by="subsample") %>%
- mutate(
- across(matches("^GROESSE.[1-2]") |matches("^BEITRAG.[1-2]") , ~./10 ) ,
- info=if_else(subsample>=4,1,0) ,
- wissen=if_else(subsample==2|subsample==5,1,0) ,
- selbst =if_else(subsample==3|subsample==6,1,0) ,
- across(everything(), as.numeric),
- Zeit_alt =Zeit, Zeit=Zeit- Tmean_Zeit ,
- knowledge_old=korrekt , korrekt = knowledge_old-Tmean_knowl
- ) %>%
- as.data.frame()
+######################################################################
+### Project : Information processing in stated preference surveys
+### Description : Data cleaning and preparation
+### Date : 12.08.2021
+### Version : 2
+
+######################################################################
+
+
+
+#====================================
+# Prepare data
+#====================================
+
+
+data <- read_sav("prior_est/data/data_raw.sav" ) %>% ### import data
+ filter(compl==1,sample!=3) %>% ## sample 3 is another choice exepriment not relevant here
+ mutate(stuttgart=if_else(q2_1<=70629&q2_1>70000,1,0)) %>% ## postal codes to city
+select(-c('userid', 'studNumb', 'Title', 'Client', 'compl', 'interrupt', 'dDay', 'dMonth', 'dYear', 'startTime', 'startTimel', 'endTime', 'city', 'device_str', 'osName_str', 'osVersion_str', 'agentName_str', 'agentVersion_str', 'devManu_str', 'devName_str', 'vpWidth_str', 'vpHeight_str', 'flashVersion_str', 'useragent_str'))%>%
+ mutate(across(matches("^q6_[0-9]$") , ~ replace_na(., 3) ), ## NA in to 3
+ across(c("q7_7_a","q7_7_c","q7_7_d","q7_7_e","q7_7_f","q7_7_i"), ~ recode(as.numeric(.), '1'=0,'2'=1,'3'=0, .missing = 0)) , #recode quiz
+ across(c("q7_7_b","q7_7_g","q7_7_h") , ~ recode(as.numeric(.),'1'=1,'2'=0,'3'=0 , .missing = 0) ) #recode quiz
+ ) %>%
+ mutate(korrekt=rowSums(select(.,c("q7_7_a" , "q7_7_b" ,"q7_7_c" ,"q7_7_e", "q7_7_f", "q7_7_g", "q7_7_h", "q7_7_i")),na.rm=TRUE) # number of correct answers in quiz
+ ) %>%
+ {. ->> dataN } %>% #here is save
+ select(c('serial', 'korrekt' , 'sample', 'sample2', 'q3_3_zeit', 'q6_blck', 'q6_set_1', 'q6_set_2', 'q6_set_3', 'q6_set_4', 'q6_set_5', 'q6_set_6', 'q6_set_7', 'q6_set_8', 'q6_1', 'q6_2', 'q6_3', 'q6_4', 'q6_5', 'q6_6', 'q6_7', 'q6_8')) %>%
+ {. ->> widedata } %>% #here is save
+
+
+rename_with(function(x) c("ID", "korrekt" ,"sample","subsample","Zeit","block","set_1","set_2","set_3","set_4","set_5","set_6","set_7","set_8","choice_1","choice_2","choice_3","choice_4","choice_5","choice_6","choice_7","choice_8") ) %>% ### rename variables
+ as.data.frame()%>%
+reshape(varying=7:22, timevar="choiceset", idvar="ID", direction="long", sep="_") %>% # to long format for apollo %>%
+arrange(ID) %>%
+ {. ->> apollodata }
+
+#### Prepare design and merge with data
+
+design12<-read_excel("prior_est/data/design_hauptstudie.xlsx") %>%
+ mutate(across(where(is.character) , ~ tidyr::replace_na(., "0") ) ,
+ L_GROESSE=GROESSE, GROESSE = as.numeric(str_remove_all(GROESSE,"[^0-9]"))/1000 ,
+ L_ENTFERNUNG = ENTFERNUNG , ENTFERNUNG=as.numeric(str_remove(ENTFERNUNG,"[^0-9].*?(\\((.*?)\\))")) , ENTFERNUNG=if_else(ENTFERNUNG>99,ENTFERNUNG/1000,ENTFERNUNG) ,
+ L_GEMEINSCHAFTSAKTIVITAETEN=GEMEINSCHAFTSAKTIVITAETEN , GEMEINSCHAFTSAKTIVITAETEN=recode(GEMEINSCHAFTSAKTIVITAETEN, "Gemeinschaftsaktivitäten" = 1 , .default=0),
+ L_KULTURVERANSTALTUNGEN=KULTURVERANSTALTUNGEN , KULTURVERANSTALTUNGEN=recode(KULTURVERANSTALTUNGEN, "Kulturveranstaltungen" = 1 , .default=0) ,
+ L_UMWELTBILDUNG=UMWELTBILDUNG , UMWELTBILDUNG=recode(UMWELTBILDUNG, "Umweltbildung" = 1 , .default=0),
+ L_GESTALTUNG=GESTALTUNG , GESTALTUNG=recode(GESTALTUNG, "eher geordnet" = 0 , "eher naturnah" = 1 , .default=999),
+ L_ZUGANG=ZUGANG, ZUGANG = as.numeric(str_remove_all(ZUGANG,"[^0-9]"))-7,
+ L_BEITRAG=BEITRAG, BEITRAG = as.numeric(str_remove_all(BEITRAG,"[^0-9]"))/10
+ )%>% ### NAs in 0 umwandeln
+ as.data.frame() %>%
+ reshape(timevar = "alt", idvar = "choice_set", direction = "wide" ) %>% ## daten in wide format
+ mutate(GROESSE.3=0, ENTFERNUNG.3=0,GEMEINSCHAFTSAKTIVITAETEN.3=0, KULTURVERANSTALTUNGEN.3=0, UMWELTBILDUNG.3=0, GESTALTUNG.3=0, ZUGANG.3=0, BEITRAG.3=0) %>%
+ rename(set=choice_set) %>% {. ->> designforsim } %>%
+right_join(data, by="set") %>%
+ arrange(ID,set) %>%
+ mutate(choice=as.numeric(choice),
+ Zeit=Zeit/10 #for time models
+ ) %>%
+ select(!matches("Block.[1-2]")) %>%
+ relocate(ID,sample, subsample, block, Zeit, choiceset, set, choice) %>%
+ relocate(starts_with("L_"), .after = last_col())
+
+
+#data for apollo
+
+
+## create means by treatment for time and knowledge questions, to be used later on in time and knowledge interacting choice models.
+
+means <- design12 %>%
+ group_by(subsample) %>%
+ summarize(Tmean_Zeit=mean(Zeit) , Tmean_knowl =mean(korrekt))
+
+
+database <- design12 %>%
+ select(-starts_with("L_"))%>%
+ left_join(means, by="subsample") %>%
+ mutate(
+ across(matches("^GROESSE.[1-2]") |matches("^BEITRAG.[1-2]") , ~./10 ) ,
+ info=if_else(subsample>=4,1,0) ,
+ wissen=if_else(subsample==2|subsample==5,1,0) ,
+ selbst =if_else(subsample==3|subsample==6,1,0) ,
+ across(everything(), as.numeric),
+ Zeit_alt =Zeit, Zeit=Zeit- Tmean_Zeit ,
+ knowledge_old=korrekt , korrekt = knowledge_old-Tmean_knowl
+ ) %>%
+ as.data.frame()
diff --git a/prior_est/scripts/modelclogitbase.R b/prior_est/scripts/modelclogitbase.R
index 5f8ed09eb00b7dcbf4c3e4a97f57b6955862bafd..1ca3102436b2b896cf329289f21929a6439e75f5 100644
--- a/prior_est/scripts/modelclogitbase.R
+++ b/prior_est/scripts/modelclogitbase.R
@@ -1,158 +1,158 @@
-#### Model 2: No interaction terms ####
-# ################################################################# #
-
-### initialise apollo and core settings
-apollo_initialise()
-apollo_control= list (
- modelName = "modelclogitbase",
- modelDescr ="Conditional Logit in preference space without Interaction Terms",
- indivID = "ID",
- mixing = FALSE, # Mixed Logit
- HB=FALSE,
- nCores=1,
- outputDirectory = "modeloutput/"
-)
-
-
-
-# ################################################################# #
-#### DEFINE MODEL PARAMETERS ##
-# ################################################################# #
-
-### Starting values
-apollo_beta=c(bascgemeinschaft = 0.61,
- bascklein = 0.43,
- bgroesse = 0.0,
- bentfernung = -0.17,
- bgemeinschaft = 0.06,
- bkultur = 0.06,
- bumweltbildung = 0.13,
- bzugang = 0.05,
- bgestaltung = 0.31,
- bbeitrag = -0.12
-
-)
-
-### keine Parameter fix halten
-apollo_fixed = c()
-
-
-
-
-
-
-
-
-# ################################################################# #
-#### DEFINE MODEL AND LIKELIHOOD FUNCTION ##
-# ################################################################# #
-
-
-
-### validieren
-apollo_inputs = apollo_validateInputs()
-apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
-
- ### Function initialisation: do not change the following three commands
- ### Attach inputs and detach after function exit
- apollo_attach(apollo_beta, apollo_inputs)
- on.exit(apollo_detach(apollo_beta, apollo_inputs))
-
- ### Create list of probabilities P
- P = list()
-
- ### List of utilities (later integrated in mnl_settings below)
- V = list()
-
- V[['alt1']] =
- bascgemeinschaft +
-
- bgroesse * GROESSE.1 +
-
- bentfernung * ENTFERNUNG.1 +
-
- bgemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 +
-
- bkultur * KULTURVERANSTALTUNGEN.1 +
-
- bumweltbildung * UMWELTBILDUNG.1 +
-
- bzugang * ZUGANG.1 +
-
- bgestaltung * GESTALTUNG.1 -
-
- bbeitrag*BEITRAG.1
-
- V[['alt2']] =
- bascklein +
-
- bgroesse * GROESSE.2 +
-
- bentfernung * ENTFERNUNG.2 +
-
- bgemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 +
-
- bkultur * KULTURVERANSTALTUNGEN.2 +
-
- bumweltbildung * UMWELTBILDUNG.2 +
-
- bzugang * ZUGANG.2 +
-
- bgestaltung * GESTALTUNG.2 -
-
- bbeitrag* BEITRAG.2
-
- V[['alt3']] = 0
-
- ### Define settings for MNL model component
- mnl_settings = list(
- alternatives = c(alt1=1, alt2=2, alt3=3),
- avail = 1, # all alternatives are available in every choice
- choiceVar = choice,
- V = V#, # tell function to use list vector defined above
-
- )
-
- ### Compute probabilities using MNL model
- P[['model']] = apollo_mnl(mnl_settings, functionality)
-
- ### Take product across observation for same individual
- P = apollo_panelProd(P, apollo_inputs, functionality)
-
-
-
- ### Prepare and return outputs of function
- P = apollo_prepareProb(P, apollo_inputs, functionality)
- return(P)
-}
-
-
-
-# ################################################################# #
-#### MODEL ESTIMATION ##
-# ################################################################# #
-
-modelclogitbase = apollo_estimate(apollo_beta, apollo_fixed,
- apollo_probabilities, apollo_inputs, estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
- hessianRoutine="analytic"))
-
-
-
-# ################################################################# #
-#### MODEL OUTPUTS ##
-# ################################################################# #
-
-
-
-apollo_saveOutput(modelclogitbase)
-
-
-detach(package:apollo, unload=TRUE)
-
-
-
-# ################################################################# #
-
-
-
-
+#### Model 2: No interaction terms ####
+# ################################################################# #
+
+### initialise apollo and core settings
+apollo_initialise()
+apollo_control= list (
+ modelName = "modelclogitbase",
+ modelDescr ="Conditional Logit in preference space without Interaction Terms",
+ indivID = "ID",
+ mixing = FALSE, # Mixed Logit
+ HB=FALSE,
+ nCores=1,
+ outputDirectory = "modeloutput/"
+)
+
+
+
+# ################################################################# #
+#### DEFINE MODEL PARAMETERS ##
+# ################################################################# #
+
+### Starting values
+apollo_beta=c(bascgemeinschaft = 0.61,
+ bascklein = 0.43,
+ bgroesse = 0.0,
+ bentfernung = -0.17,
+ bgemeinschaft = 0.06,
+ bkultur = 0.06,
+ bumweltbildung = 0.13,
+ bzugang = 0.05,
+ bgestaltung = 0.31,
+ bbeitrag = -0.12
+
+)
+
+### keine Parameter fix halten
+apollo_fixed = c()
+
+
+
+
+
+
+
+
+# ################################################################# #
+#### DEFINE MODEL AND LIKELIHOOD FUNCTION ##
+# ################################################################# #
+
+
+
+### validieren
+apollo_inputs = apollo_validateInputs()
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ ### List of utilities (later integrated in mnl_settings below)
+ V = list()
+
+ V[['alt1']] =
+ bascgemeinschaft +
+
+ bgroesse * GROESSE.1 +
+
+ bentfernung * ENTFERNUNG.1 +
+
+ bgemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.1 +
+
+ bkultur * KULTURVERANSTALTUNGEN.1 +
+
+ bumweltbildung * UMWELTBILDUNG.1 +
+
+ bzugang * ZUGANG.1 +
+
+ bgestaltung * GESTALTUNG.1 -
+
+ bbeitrag*BEITRAG.1
+
+ V[['alt2']] =
+ bascklein +
+
+ bgroesse * GROESSE.2 +
+
+ bentfernung * ENTFERNUNG.2 +
+
+ bgemeinschaft * GEMEINSCHAFTSAKTIVITAETEN.2 +
+
+ bkultur * KULTURVERANSTALTUNGEN.2 +
+
+ bumweltbildung * UMWELTBILDUNG.2 +
+
+ bzugang * ZUGANG.2 +
+
+ bgestaltung * GESTALTUNG.2 -
+
+ bbeitrag* BEITRAG.2
+
+ V[['alt3']] = 0
+
+ ### Define settings for MNL model component
+ mnl_settings = list(
+ alternatives = c(alt1=1, alt2=2, alt3=3),
+ avail = 1, # all alternatives are available in every choice
+ choiceVar = choice,
+ V = V#, # tell function to use list vector defined above
+
+ )
+
+ ### Compute probabilities using MNL model
+ P[['model']] = apollo_mnl(mnl_settings, functionality)
+
+ ### Take product across observation for same individual
+ P = apollo_panelProd(P, apollo_inputs, functionality)
+
+
+
+ ### Prepare and return outputs of function
+ P = apollo_prepareProb(P, apollo_inputs, functionality)
+ return(P)
+}
+
+
+
+# ################################################################# #
+#### MODEL ESTIMATION ##
+# ################################################################# #
+
+modelclogitbase = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs, estimate_settings=list(maxIterations=400, estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+
+
+
+apollo_saveOutput(modelclogitbase)
+
+
+detach(package:apollo, unload=TRUE)
+
+
+
+# ################################################################# #
+
+
+
+
diff --git a/prior_est/scripts/prepareolddesign.R b/prior_est/scripts/prepareolddesign.R
index ab83f7a0db0bf2b756892adb35a038dcc9a8db4d..216d2b11cf434618b811aa4eb94bea4cda2a9fb1 100644
--- a/prior_est/scripts/prepareolddesign.R
+++ b/prior_est/scripts/prepareolddesign.R
@@ -1,17 +1,17 @@
-
-
-designtosave <- designforsim %>%
- select(!starts_with("L_"), -block.2) %>%
- rename("Choice situation"=set, Block=block.1) %>%
- mutate(Design=1,
- across(matches("^GROESSE.[1-2]") |matches("^BEITRAG.[1-2]") , ~./10 )
- ) %>%
-rename_with(tolower, .cols = matches("[1-3]$")) %>%
- rename_with(~ paste0("alt",
- str_extract(.x,"[1-3]$"),
- ".",
- str_remove(.x,"\\.[1-3]$") ),
- .cols = matches("[1-3]$")) %>%
- rename_with(~ str_remove_all(.x,"saktivitaeten|veranstaltungen"),everything())
-
-write_delim(designtosave, "Designs/designjeem.ngd" ,delim = "\t")
+
+
+designtosave <- designforsim %>%
+ select(!starts_with("L_"), -block.2) %>%
+ rename("Choice situation"=set, Block=block.1) %>%
+ mutate(Design=1,
+ across(matches("^GROESSE.[1-2]") |matches("^BEITRAG.[1-2]") , ~./10 )
+ ) %>%
+rename_with(tolower, .cols = matches("[1-3]$")) %>%
+ rename_with(~ paste0("alt",
+ str_extract(.x,"[1-3]$"),
+ ".",
+ str_remove(.x,"\\.[1-3]$") ),
+ .cols = matches("[1-3]$")) %>%
+ rename_with(~ str_remove_all(.x,"saktivitaeten|veranstaltungen"),everything())
+
+write_delim(designtosave, "Designs/designjeem.ngd" ,delim = "\t")
diff --git a/prior_est/scripts/priordetermination.R b/prior_est/scripts/priordetermination.R
index a3c9cf3e981102fb34a4cc9a1292dbdaa3c6de39..5dea45ff1420ef4690b31338795a358f2ff8e298 100644
--- a/prior_est/scripts/priordetermination.R
+++ b/prior_est/scripts/priordetermination.R
@@ -1,42 +1,42 @@
-
-source("prior_est/scripts/preamble.R")
-source("prior_est/functions/apollo_help_functions.R")
-source("prior_est/scripts/data_cleaning.R")
-source("prior_est/scripts/prepareolddesign.R")
-source("prior_est/scripts/modelclogitbase.R")
-
-priors <- round(modelclogitbase$estimate, 2)
-priorsasc <- priors[grep("asc",names(priors))]
-
-priorsb <- priors[-grep("asc",names(priors))]
-length(priors)
-names(priors)
-
-
-
-
-
-attlevels <- database %>%
- select(ends_with(".1")) %>%
- rename_with(~str_remove(.,".1")) %>%
- relocate("ZUGANG", .before="GESTALTUNG") %>%
- as.list() %>% map(~paste0(sort(unique(.x)), collapse = ","))
-
-
-genU <-function(alts=3){
-require(purrr)
- paste0(map_chr(1:alts,
- ~paste0("U(alt",.,") = ",
- paste0(names(priorsasc),"[", priorsasc,"]", collapse = "+"),
- "+",
- paste0(names(priorsb),"[",priorsb,"]*",
- str_remove(names(priorsb),pattern = "b_") ,
- "[",attlevels,"]" ,
- collapse = "+"))), collapse = "/ \n")
-
-
-}
-
-
-t <-genU()
-cat(t)
+
+source("prior_est/scripts/preamble.R")
+source("prior_est/functions/apollo_help_functions.R")
+source("prior_est/scripts/data_cleaning.R")
+source("prior_est/scripts/prepareolddesign.R")
+source("prior_est/scripts/modelclogitbase.R")
+
+priors <- round(modelclogitbase$estimate, 2)
+priorsasc <- priors[grep("asc",names(priors))]
+
+priorsb <- priors[-grep("asc",names(priors))]
+length(priors)
+names(priors)
+
+
+
+
+
+attlevels <- database %>%
+ select(ends_with(".1")) %>%
+ rename_with(~str_remove(.,".1")) %>%
+ relocate("ZUGANG", .before="GESTALTUNG") %>%
+ as.list() %>% map(~paste0(sort(unique(.x)), collapse = ","))
+
+
+genU <-function(alts=3){
+require(purrr)
+ paste0(map_chr(1:alts,
+ ~paste0("U(alt",.,") = ",
+ paste0(names(priorsasc),"[", priorsasc,"]", collapse = "+"),
+ "+",
+ paste0(names(priorsb),"[",priorsb,"]*",
+ str_remove(names(priorsb),pattern = "b_") ,
+ "[",attlevels,"]" ,
+ collapse = "+"))), collapse = "/ \n")
+
+
+}
+
+
+t <-genU()
+cat(t)
diff --git a/prior_est/scripts/priordetermination_parks.R b/prior_est/scripts/priordetermination_parks.R
index 2d1eac157f39e5b4cef204e47b48d3bf8a6a95f7..14ed906b17d6f8458e005a8f1d6a02e7a4e0b9f8 100644
--- a/prior_est/scripts/priordetermination_parks.R
+++ b/prior_est/scripts/priordetermination_parks.R
@@ -1,50 +1,50 @@
-
-source("prior_est/scripts/preamble.R")
-
-load("prior_est/04_CL_PS_F_estimates.RData")
-load("prior_est/data/02_apollodataF.RData")
-
-
-
-priors <- round(ps_model$estimate, 2)
-
-names(priors)<- str_remove(names(priors),"_")
-
-
-priorsasc <- priors[grep("asc",names(priors))]
-names(priors)<- str_replace(names(priors),"asc","basc")
-
-
-
-priorsb <- priors[-grep("asc",names(priors))]
-length(priors)
-names(priors)
-
-
-
-
-
-attlevels <- apollodata_F %>%
- select(ends_with(".1")) %>%
- rename_with(~str_remove(.,".1")) %>%
- #relocate("ZUGANG", .before="GESTALTUNG") %>%
- as.list() %>% map(~paste0(sort(unique(.x)), collapse = ","))
-
-
-genU <-function(alts=3){
-require(purrr)
- paste0(map_chr(1:alts,
- ~paste0("U(alt",.,") = ",
- paste0(names(priorsasc),"[", priorsasc,"]", collapse = "+"),
- "+",
- paste0(names(priorsb),"[",priorsb,"]*",
- str_remove(names(priorsb),pattern = "^b") ,
- "[",attlevels,"]" ,
- collapse = "+"))), collapse = "/ \n")
-
-
-}
-
-
-t <-genU(2)
-cat(t)
+
+source("prior_est/scripts/preamble.R")
+
+load("prior_est/04_CL_PS_F_estimates.RData")
+load("prior_est/data/02_apollodataF.RData")
+
+
+
+priors <- round(ps_model$estimate, 2)
+
+names(priors)<- str_remove(names(priors),"_")
+
+
+priorsasc <- priors[grep("asc",names(priors))]
+names(priors)<- str_replace(names(priors),"asc","basc")
+
+
+
+priorsb <- priors[-grep("asc",names(priors))]
+length(priors)
+names(priors)
+
+
+
+
+
+attlevels <- apollodata_F %>%
+ select(ends_with(".1")) %>%
+ rename_with(~str_remove(.,".1")) %>%
+ #relocate("ZUGANG", .before="GESTALTUNG") %>%
+ as.list() %>% map(~paste0(sort(unique(.x)), collapse = ","))
+
+
+genU <-function(alts=3){
+require(purrr)
+ paste0(map_chr(1:alts,
+ ~paste0("U(alt",.,") = ",
+ paste0(names(priorsasc),"[", priorsasc,"]", collapse = "+"),
+ "+",
+ paste0(names(priorsb),"[",priorsb,"]*",
+ str_remove(names(priorsb),pattern = "^b") ,
+ "[",attlevels,"]" ,
+ collapse = "+"))), collapse = "/ \n")
+
+
+}
+
+
+t <-genU(2)
+cat(t)
diff --git a/simulation_output.html b/simulation_output.html
index 88976050123740f0e1b4de7322f82af91de60dce..1bd9bdd24473b494643c7a975b8c278d0fe8ce9a 100644
--- a/simulation_output.html
+++ b/simulation_output.html
@@ -1,4059 +1,4059 @@
-<!DOCTYPE html>
-<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
-
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-<meta name="generator" content="quarto-1.3.353">
-
-<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
-
-<meta name="author" content="Julian Sagebiel">
-<meta name="dcterms.date" content="2023-01-05">
-
-<title>Simulation experimental design</title>
-<style>
-code{white-space: pre-wrap;}
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- *
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- convertToMenu();
- isVisible = false;
- }
- }
- }
- }
- };
- };
-
- const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
- for (const tabEl of tabEls) {
- const id = tabEl.getAttribute("data-bs-target");
- if (id) {
- const columnEl = document.querySelector(
- `${id} .column-margin, .tabset-margin-content`
- );
- if (columnEl)
- tabEl.addEventListener("shown.bs.tab", function (event) {
- const el = event.srcElement;
- if (el) {
- const visibleCls = `${el.id}-margin-content`;
- // walk up until we find a parent tabset
- let panelTabsetEl = el.parentElement;
- while (panelTabsetEl) {
- if (panelTabsetEl.classList.contains("panel-tabset")) {
- break;
- }
- panelTabsetEl = panelTabsetEl.parentElement;
- }
-
- if (panelTabsetEl) {
- const prevSib = panelTabsetEl.previousElementSibling;
- if (
- prevSib &&
- prevSib.classList.contains("tabset-margin-container")
- ) {
- const childNodes = prevSib.querySelectorAll(
- ".tabset-margin-content"
- );
- for (const childEl of childNodes) {
- if (childEl.classList.contains(visibleCls)) {
- childEl.classList.remove("collapse");
- } else {
- childEl.classList.add("collapse");
- }
- }
- }
- }
- }
-
- layoutMarginEls();
- });
- }
- }
-
- // Manage the visibility of the toc and the sidebar
- const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
- id: "quarto-toc-toggle",
- titleSelector: "#toc-title",
- dismissOnClick: true,
- });
- const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
- id: "quarto-sidebarnav-toggle",
- titleSelector: ".title",
- dismissOnClick: false,
- });
- let tocLeftScrollVisibility;
- if (leftTocEl) {
- tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
- id: "quarto-lefttoc-toggle",
- titleSelector: "#toc-title",
- dismissOnClick: true,
- });
- }
-
- // Find the first element that uses formatting in special columns
- const conflictingEls = window.document.body.querySelectorAll(
- '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
- );
-
- // Filter all the possibly conflicting elements into ones
- // the do conflict on the left or ride side
- const arrConflictingEls = Array.from(conflictingEls);
- const leftSideConflictEls = arrConflictingEls.filter((el) => {
- if (el.tagName === "ASIDE") {
- return false;
- }
- return Array.from(el.classList).find((className) => {
- return (
- className !== "column-body" &&
- className.startsWith("column-") &&
- !className.endsWith("right") &&
- !className.endsWith("container") &&
- className !== "column-margin"
- );
- });
- });
- const rightSideConflictEls = arrConflictingEls.filter((el) => {
- if (el.tagName === "ASIDE") {
- return true;
- }
-
- const hasMarginCaption = Array.from(el.classList).find((className) => {
- return className == "margin-caption";
- });
- if (hasMarginCaption) {
- return true;
- }
-
- return Array.from(el.classList).find((className) => {
- return (
- className !== "column-body" &&
- !className.endsWith("container") &&
- className.startsWith("column-") &&
- !className.endsWith("left")
- );
- });
- });
-
- const kOverlapPaddingSize = 10;
- function toRegions(els) {
- return els.map((el) => {
- const boundRect = el.getBoundingClientRect();
- const top =
- boundRect.top +
- document.documentElement.scrollTop -
- kOverlapPaddingSize;
- return {
- top,
- bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
- };
- });
- }
-
- let hasObserved = false;
- const visibleItemObserver = (els) => {
- let visibleElements = [...els];
- const intersectionObserver = new IntersectionObserver(
- (entries, _observer) => {
- entries.forEach((entry) => {
- if (entry.isIntersecting) {
- if (visibleElements.indexOf(entry.target) === -1) {
- visibleElements.push(entry.target);
- }
- } else {
- visibleElements = visibleElements.filter((visibleEntry) => {
- return visibleEntry !== entry;
- });
- }
- });
-
- if (!hasObserved) {
- hideOverlappedSidebars();
- }
- hasObserved = true;
- },
- {}
- );
- els.forEach((el) => {
- intersectionObserver.observe(el);
- });
-
- return {
- getVisibleEntries: () => {
- return visibleElements;
- },
- };
- };
-
- const rightElementObserver = visibleItemObserver(rightSideConflictEls);
- const leftElementObserver = visibleItemObserver(leftSideConflictEls);
-
- const hideOverlappedSidebars = () => {
- marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
- sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
- if (tocLeftScrollVisibility) {
- tocLeftScrollVisibility(
- toRegions(leftElementObserver.getVisibleEntries())
- );
- }
- };
-
- window.quartoToggleReader = () => {
- // Applies a slow class (or removes it)
- // to update the transition speed
- const slowTransition = (slow) => {
- const manageTransition = (id, slow) => {
- const el = document.getElementById(id);
- if (el) {
- if (slow) {
- el.classList.add("slow");
- } else {
- el.classList.remove("slow");
- }
- }
- };
-
- manageTransition("TOC", slow);
- manageTransition("quarto-sidebar", slow);
- };
- const readerMode = !isReaderMode();
- setReaderModeValue(readerMode);
-
- // If we're entering reader mode, slow the transition
- if (readerMode) {
- slowTransition(readerMode);
- }
- highlightReaderToggle(readerMode);
- hideOverlappedSidebars();
-
- // If we're exiting reader mode, restore the non-slow transition
- if (!readerMode) {
- slowTransition(!readerMode);
- }
- };
-
- const highlightReaderToggle = (readerMode) => {
- const els = document.querySelectorAll(".quarto-reader-toggle");
- if (els) {
- els.forEach((el) => {
- if (readerMode) {
- el.classList.add("reader");
- } else {
- el.classList.remove("reader");
- }
- });
- }
- };
-
- const setReaderModeValue = (val) => {
- if (window.location.protocol !== "file:") {
- window.localStorage.setItem("quarto-reader-mode", val);
- } else {
- localReaderMode = val;
- }
- };
-
- const isReaderMode = () => {
- if (window.location.protocol !== "file:") {
- return window.localStorage.getItem("quarto-reader-mode") === "true";
- } else {
- return localReaderMode;
- }
- };
- let localReaderMode = null;
-
- const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
- const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
-
- // Walk the TOC and collapse/expand nodes
- // Nodes are expanded if:
- // - they are top level
- // - they have children that are 'active' links
- // - they are directly below an link that is 'active'
- const walk = (el, depth) => {
- // Tick depth when we enter a UL
- if (el.tagName === "UL") {
- depth = depth + 1;
- }
-
- // It this is active link
- let isActiveNode = false;
- if (el.tagName === "A" && el.classList.contains("active")) {
- isActiveNode = true;
- }
-
- // See if there is an active child to this element
- let hasActiveChild = false;
- for (child of el.children) {
- hasActiveChild = walk(child, depth) || hasActiveChild;
- }
-
- // Process the collapse state if this is an UL
- if (el.tagName === "UL") {
- if (tocOpenDepth === -1 && depth > 1) {
- el.classList.add("collapse");
- } else if (
- depth <= tocOpenDepth ||
- hasActiveChild ||
- prevSiblingIsActiveLink(el)
- ) {
- el.classList.remove("collapse");
- } else {
- el.classList.add("collapse");
- }
-
- // untick depth when we leave a UL
- depth = depth - 1;
- }
- return hasActiveChild || isActiveNode;
- };
-
- // walk the TOC and expand / collapse any items that should be shown
-
- if (tocEl) {
- walk(tocEl, 0);
- updateActiveLink();
- }
-
- // Throttle the scroll event and walk peridiocally
- window.document.addEventListener(
- "scroll",
- throttle(() => {
- if (tocEl) {
- updateActiveLink();
- walk(tocEl, 0);
- }
- if (!isReaderMode()) {
- hideOverlappedSidebars();
- }
- }, 5)
- );
- window.addEventListener(
- "resize",
- throttle(() => {
- if (!isReaderMode()) {
- hideOverlappedSidebars();
- }
- }, 10)
- );
- hideOverlappedSidebars();
- highlightReaderToggle(isReaderMode());
-});
-
-// grouped tabsets
-window.addEventListener("pageshow", (_event) => {
- function getTabSettings() {
- const data = localStorage.getItem("quarto-persistent-tabsets-data");
- if (!data) {
- localStorage.setItem("quarto-persistent-tabsets-data", "{}");
- return {};
- }
- if (data) {
- return JSON.parse(data);
- }
- }
-
- function setTabSettings(data) {
- localStorage.setItem(
- "quarto-persistent-tabsets-data",
- JSON.stringify(data)
- );
- }
-
- function setTabState(groupName, groupValue) {
- const data = getTabSettings();
- data[groupName] = groupValue;
- setTabSettings(data);
- }
-
- function toggleTab(tab, active) {
- const tabPanelId = tab.getAttribute("aria-controls");
- const tabPanel = document.getElementById(tabPanelId);
- if (active) {
- tab.classList.add("active");
- tabPanel.classList.add("active");
- } else {
- tab.classList.remove("active");
- tabPanel.classList.remove("active");
- }
- }
-
- function toggleAll(selectedGroup, selectorsToSync) {
- for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
- const active = selectedGroup === thisGroup;
- for (const tab of tabs) {
- toggleTab(tab, active);
- }
- }
- }
-
- function findSelectorsToSyncByLanguage() {
- const result = {};
- const tabs = Array.from(
- document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
- );
- for (const item of tabs) {
- const div = item.parentElement.parentElement.parentElement;
- const group = div.getAttribute("data-group");
- if (!result[group]) {
- result[group] = {};
- }
- const selectorsToSync = result[group];
- const value = item.innerHTML;
- if (!selectorsToSync[value]) {
- selectorsToSync[value] = [];
- }
- selectorsToSync[value].push(item);
- }
- return result;
- }
-
- function setupSelectorSync() {
- const selectorsToSync = findSelectorsToSyncByLanguage();
- Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
- Object.entries(tabSetsByValue).forEach(([value, items]) => {
- items.forEach((item) => {
- item.addEventListener("click", (_event) => {
- setTabState(group, value);
- toggleAll(value, selectorsToSync[group]);
- });
- });
- });
- });
- return selectorsToSync;
- }
-
- const selectorsToSync = setupSelectorSync();
- for (const [group, selectedName] of Object.entries(getTabSettings())) {
- const selectors = selectorsToSync[group];
- // it's possible that stale state gives us empty selections, so we explicitly check here.
- if (selectors) {
- toggleAll(selectedName, selectors);
- }
- }
-});
-
-function throttle(func, wait) {
- let waiting = false;
- return function () {
- if (!waiting) {
- func.apply(this, arguments);
- waiting = true;
- setTimeout(function () {
- waiting = false;
- }, wait);
- }
- };
-}
-
-function nexttick(func) {
- return setTimeout(func, 0);
-}
-</script>
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Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove(Tn),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add(Tn),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),u(t),t.classList.contains(On)&&t.classList.add(Cn);let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&V.find(".dropdown-toggle",e).forEach((t=>t.classList.add(Tn))),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=xn.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this)||xn.getOrCreateInstance(this).show()})),g(xn);const Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
-//# sourceMappingURL=bootstrap.bundle.min.js.map</script>
-<style type="text/css">@font-face {
-font-display: block;
-font-family: "bootstrap-icons";
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) 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-.bi::before,
-[class^="bi-"]::before,
-[class*=" bi-"]::before {
-display: inline-block;
-font-family: bootstrap-icons !important;
-font-style: normal;
-font-weight: normal !important;
-font-variant: normal;
-text-transform: none;
-line-height: 1;
-vertical-align: -.125em;
--webkit-font-smoothing: antialiased;
--moz-osx-font-smoothing: grayscale;
-}
-.bi-123::before { content: "\f67f"; }
-.bi-alarm-fill::before { content: "\f101"; }
-.bi-alarm::before { content: "\f102"; }
-.bi-align-bottom::before { content: "\f103"; }
-.bi-align-center::before { content: "\f104"; }
-.bi-align-end::before { content: "\f105"; }
-.bi-align-middle::before { content: "\f106"; }
-.bi-align-start::before { content: "\f107"; }
-.bi-align-top::before { content: "\f108"; }
-.bi-alt::before { content: "\f109"; }
-.bi-app-indicator::before { content: "\f10a"; }
-.bi-app::before { content: "\f10b"; }
-.bi-archive-fill::before { content: "\f10c"; }
-.bi-archive::before { content: "\f10d"; }
-.bi-arrow-90deg-down::before { content: "\f10e"; }
-.bi-arrow-90deg-left::before { content: "\f10f"; }
-.bi-arrow-90deg-right::before { content: "\f110"; }
-.bi-arrow-90deg-up::before { content: "\f111"; }
-.bi-arrow-bar-down::before { content: "\f112"; }
-.bi-arrow-bar-left::before { content: "\f113"; }
-.bi-arrow-bar-right::before { content: "\f114"; }
-.bi-arrow-bar-up::before { content: "\f115"; }
-.bi-arrow-clockwise::before { content: "\f116"; }
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-.bi-arrow-down-circle-fill::before { content: "\f118"; }
-.bi-arrow-down-circle::before { content: "\f119"; }
-.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
-.bi-arrow-down-left-circle::before { content: "\f11b"; }
-.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
-.bi-arrow-down-left-square::before { content: "\f11d"; }
-.bi-arrow-down-left::before { content: "\f11e"; }
-.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
-.bi-arrow-down-right-circle::before { content: "\f120"; }
-.bi-arrow-down-right-square-fill::before { content: "\f121"; }
-.bi-arrow-down-right-square::before { content: "\f122"; }
-.bi-arrow-down-right::before { content: "\f123"; }
-.bi-arrow-down-short::before { content: "\f124"; }
-.bi-arrow-down-square-fill::before { content: "\f125"; }
-.bi-arrow-down-square::before { content: "\f126"; }
-.bi-arrow-down-up::before { content: "\f127"; }
-.bi-arrow-down::before { content: "\f128"; }
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-.bi-arrow-left-square::before { content: "\f12e"; }
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-.bi-arrow-right-circle::before { content: "\f134"; }
-.bi-arrow-right-short::before { content: "\f135"; }
-.bi-arrow-right-square-fill::before { content: "\f136"; }
-.bi-arrow-right-square::before { content: "\f137"; }
-.bi-arrow-right::before { content: "\f138"; }
-.bi-arrow-up-circle-fill::before { content: "\f139"; }
-.bi-arrow-up-circle::before { content: "\f13a"; }
-.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
-.bi-arrow-up-left-circle::before { content: "\f13c"; }
-.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
-.bi-arrow-up-left-square::before { content: "\f13e"; }
-.bi-arrow-up-left::before { content: "\f13f"; }
-.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
-.bi-arrow-up-right-circle::before { content: "\f141"; }
-.bi-arrow-up-right-square-fill::before { content: "\f142"; }
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-.bi-train-front::before { content: "\f81d"; }
-.bi-train-lightrail-front-fill::before { content: "\f81e"; }
-.bi-train-lightrail-front::before { content: "\f81f"; }
-.bi-truck-front-fill::before { content: "\f820"; }
-.bi-truck-front::before { content: "\f821"; }
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-.bi-ev-station::before { content: "\f83a"; }
-.bi-fuel-pump-diesel-fill::before { content: "\f83b"; }
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-.bi-fuel-pump-fill::before { content: "\f83d"; }
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-.bi-rocket-takeoff-fill::before { content: "\f844"; }
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-.bi-sign-do-not-enter::before { content: "\f852"; }
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-.bi-sign-no-parking-fill::before { content: "\f861"; }
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-.bi-sign-no-right-turn-fill::before { content: "\f863"; }
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-.bi-sign-railroad-fill::before { content: "\f865"; }
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-.bi-building-check::before { content: "\f868"; }
-.bi-building-dash::before { content: "\f869"; }
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-.bi-building-exclamation::before { content: "\f86b"; }
-.bi-building-fill-add::before { content: "\f86c"; }
-.bi-building-fill-check::before { content: "\f86d"; }
-.bi-building-fill-dash::before { content: "\f86e"; }
-.bi-building-fill-down::before { content: "\f86f"; }
-.bi-building-fill-exclamation::before { content: "\f870"; }
-.bi-building-fill-gear::before { content: "\f871"; }
-.bi-building-fill-lock::before { content: "\f872"; }
-.bi-building-fill-slash::before { content: "\f873"; }
-.bi-building-fill-up::before { content: "\f874"; }
-.bi-building-fill-x::before { content: "\f875"; }
-.bi-building-fill::before { content: "\f876"; }
-.bi-building-gear::before { content: "\f877"; }
-.bi-building-lock::before { content: "\f878"; }
-.bi-building-slash::before { content: "\f879"; }
-.bi-building-up::before { content: "\f87a"; }
-.bi-building-x::before { content: "\f87b"; }
-.bi-buildings-fill::before { content: "\f87c"; }
-.bi-buildings::before { content: "\f87d"; }
-.bi-bus-front-fill::before { content: "\f87e"; }
-.bi-bus-front::before { content: "\f87f"; }
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-.bi-globe-central-south-asia::before { content: "\f884"; }
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-.bi-house-add::before { content: "\f887"; }
-.bi-house-check-fill::before { content: "\f888"; }
-.bi-house-check::before { content: "\f889"; }
-.bi-house-dash-fill::before { content: "\f88a"; }
-.bi-house-dash::before { content: "\f88b"; }
-.bi-house-down-fill::before { content: "\f88c"; }
-.bi-house-down::before { content: "\f88d"; }
-.bi-house-exclamation-fill::before { content: "\f88e"; }
-.bi-house-exclamation::before { content: "\f88f"; }
-.bi-house-gear-fill::before { content: "\f890"; }
-.bi-house-gear::before { content: "\f891"; }
-.bi-house-lock-fill::before { content: "\f892"; }
-.bi-house-lock::before { content: "\f893"; }
-.bi-house-slash-fill::before { content: "\f894"; }
-.bi-house-slash::before { content: "\f895"; }
-.bi-house-up-fill::before { content: "\f896"; }
-.bi-house-up::before { content: "\f897"; }
-.bi-house-x-fill::before { content: "\f898"; }
-.bi-house-x::before { content: "\f899"; }
-.bi-person-add::before { content: "\f89a"; }
-.bi-person-down::before { content: "\f89b"; }
-.bi-person-exclamation::before { content: "\f89c"; }
-.bi-person-fill-add::before { content: "\f89d"; }
-.bi-person-fill-check::before { content: "\f89e"; }
-.bi-person-fill-dash::before { content: "\f89f"; }
-.bi-person-fill-down::before { content: "\f8a0"; }
-.bi-person-fill-exclamation::before { content: "\f8a1"; }
-.bi-person-fill-gear::before { content: "\f8a2"; }
-.bi-person-fill-lock::before { content: "\f8a3"; }
-.bi-person-fill-slash::before { content: "\f8a4"; }
-.bi-person-fill-up::before { content: "\f8a5"; }
-.bi-person-fill-x::before { content: "\f8a6"; }
-.bi-person-gear::before { content: "\f8a7"; }
-.bi-person-lock::before { content: "\f8a8"; }
-.bi-person-slash::before { content: "\f8a9"; }
-.bi-person-up::before { content: "\f8aa"; }
-.bi-scooter::before { content: "\f8ab"; }
-.bi-taxi-front-fill::before { content: "\f8ac"; }
-.bi-taxi-front::before { content: "\f8ad"; }
-.bi-amd::before { content: "\f8ae"; }
-.bi-database-add::before { content: "\f8af"; }
-.bi-database-check::before { content: "\f8b0"; }
-.bi-database-dash::before { content: "\f8b1"; }
-.bi-database-down::before { content: "\f8b2"; }
-.bi-database-exclamation::before { content: "\f8b3"; }
-.bi-database-fill-add::before { content: "\f8b4"; }
-.bi-database-fill-check::before { content: "\f8b5"; }
-.bi-database-fill-dash::before { content: "\f8b6"; }
-.bi-database-fill-down::before { content: "\f8b7"; }
-.bi-database-fill-exclamation::before { content: "\f8b8"; }
-.bi-database-fill-gear::before { content: "\f8b9"; }
-.bi-database-fill-lock::before { content: "\f8ba"; }
-.bi-database-fill-slash::before { content: "\f8bb"; }
-.bi-database-fill-up::before { content: "\f8bc"; }
-.bi-database-fill-x::before { content: "\f8bd"; }
-.bi-database-fill::before { content: "\f8be"; }
-.bi-database-gear::before { content: "\f8bf"; }
-.bi-database-lock::before { content: "\f8c0"; }
-.bi-database-slash::before { content: "\f8c1"; }
-.bi-database-up::before { content: "\f8c2"; }
-.bi-database-x::before { content: "\f8c3"; }
-.bi-database::before { content: "\f8c4"; }
-.bi-houses-fill::before { content: "\f8c5"; }
-.bi-houses::before { content: "\f8c6"; }
-.bi-nvidia::before { content: "\f8c7"; }
-.bi-person-vcard-fill::before { content: "\f8c8"; }
-.bi-person-vcard::before { content: "\f8c9"; }
-.bi-sina-weibo::before { content: "\f8ca"; }
-.bi-tencent-qq::before { content: "\f8cb"; }
-.bi-wikipedia::before { content: "\f8cc"; }
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-</head>
-
-<body class="fullcontent">
-
-<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
-
-<main class="content" id="quarto-document-content">
-
-<header id="title-block-header" class="quarto-title-block default">
-<div class="quarto-title">
-<h1 class="title">Simulation experimental design</h1>
-</div>
-
-
-
-<div class="quarto-title-meta">
-
- <div>
- <div class="quarto-title-meta-heading">Author</div>
- <div class="quarto-title-meta-contents">
- <p>Julian Sagebiel </p>
- </div>
- </div>
-
- <div>
- <div class="quarto-title-meta-heading">Published</div>
- <div class="quarto-title-meta-contents">
- <p class="date">January 5, 2023</p>
- </div>
- </div>
-
-
- </div>
-
-
-</header>
-
-<p>The simulation has 360 respondents and 2 runs.</p>
-<p>the parameters used for the simulation are:</p>
-<div class="cell">
-<div class="cell-output cell-output-stdout">
-<pre><code>$b_beitrag
-b_beitrag
- 1.15
-
-$b_entfernung
-b_entfernung
- -0.23
-
-$b_gemeinschaft
-b_gemeinschaft
- 0.13
-
-$b_gestaltung
-b_gestaltung
- 0.39
-
-$b_groesse
-b_groesse
- 0.07
-
-$b_kultur
-b_kultur
- 0.16
-
-$b_umweltbildung
-b_umweltbildung
- 0.14
-
-$b_zugang
-b_zugang
- 0.07 </code></pre>
-</div>
-</div>
-<p>The average frequencies of choices for each alternative are</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>gr<span class="ot"><-</span><span class="fu">map_dfr</span>(<span class="fu">seq_along</span>(<span class="dv">1</span><span class="sc">:</span>nosim), <span class="sc">~</span> <span class="fu">table</span>(all_designs[[<span class="st">"bayeff"</span>]][[.]][[<span class="st">"data"</span>]][[<span class="st">"CHOICE"</span>]])<span class="sc">/</span><span class="fu">length</span>(all_designs[[<span class="st">"bayeff"</span>]][[<span class="dv">1</span>]][[<span class="st">"data"</span>]][[<span class="st">"CHOICE"</span>]])) <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">everything</span>(),as.numeric)) </span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(<span class="fu">summary</span>(gr)[<span class="dv">4</span>,])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</div>
-<section id="statistics-and-power" class="level1">
-<h1>Statistics and power</h1>
-<p>Here you see the statistics of your parameters for the 2 runs.</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(summaryall ,<span class="at">digits =</span> <span class="dv">3</span>) <span class="sc">%>%</span> <span class="fu">kable_styling</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output-display">
-<table class="table table-sm table-striped small" data-quarto-postprocess="true">
-<thead>
-<tr class="header">
-<th style="text-align: left;" data-quarto-table-cell-role="th"></th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.n</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.n</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.mean</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.mean</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.sd</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.sd</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.min</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.min</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.max</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.max</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.range</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.range</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.se</th>
-<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.se</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td style="text-align: left;">est_asc_gemeinschaft</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.604</td>
-<td style="text-align: right;">0.648</td>
-<td style="text-align: right;">0.065</td>
-<td style="text-align: right;">0.242</td>
-<td style="text-align: right;">0.558</td>
-<td style="text-align: right;">0.477</td>
-<td style="text-align: right;">0.650</td>
-<td style="text-align: right;">0.819</td>
-<td style="text-align: right;">0.092</td>
-<td style="text-align: right;">0.342</td>
-<td style="text-align: right;">0.046</td>
-<td style="text-align: right;">0.171</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">est_b_groesse</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.074</td>
-<td style="text-align: right;">0.065</td>
-<td style="text-align: right;">0.023</td>
-<td style="text-align: right;">0.032</td>
-<td style="text-align: right;">0.058</td>
-<td style="text-align: right;">0.043</td>
-<td style="text-align: right;">0.091</td>
-<td style="text-align: right;">0.088</td>
-<td style="text-align: right;">0.033</td>
-<td style="text-align: right;">0.045</td>
-<td style="text-align: right;">0.016</td>
-<td style="text-align: right;">0.022</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">est_b_entfernung</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">-0.222</td>
-<td style="text-align: right;">-0.223</td>
-<td style="text-align: right;">0.006</td>
-<td style="text-align: right;">0.003</td>
-<td style="text-align: right;">-0.227</td>
-<td style="text-align: right;">-0.225</td>
-<td style="text-align: right;">-0.218</td>
-<td style="text-align: right;">-0.221</td>
-<td style="text-align: right;">0.009</td>
-<td style="text-align: right;">0.004</td>
-<td style="text-align: right;">0.005</td>
-<td style="text-align: right;">0.002</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">est_b_gemeinschaft</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.101</td>
-<td style="text-align: right;">0.124</td>
-<td style="text-align: right;">0.005</td>
-<td style="text-align: right;">0.042</td>
-<td style="text-align: right;">0.098</td>
-<td style="text-align: right;">0.094</td>
-<td style="text-align: right;">0.105</td>
-<td style="text-align: right;">0.154</td>
-<td style="text-align: right;">0.006</td>
-<td style="text-align: right;">0.060</td>
-<td style="text-align: right;">0.003</td>
-<td style="text-align: right;">0.030</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">est_b_kultur</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.145</td>
-<td style="text-align: right;">0.160</td>
-<td style="text-align: right;">0.035</td>
-<td style="text-align: right;">0.038</td>
-<td style="text-align: right;">0.121</td>
-<td style="text-align: right;">0.133</td>
-<td style="text-align: right;">0.170</td>
-<td style="text-align: right;">0.187</td>
-<td style="text-align: right;">0.049</td>
-<td style="text-align: right;">0.054</td>
-<td style="text-align: right;">0.025</td>
-<td style="text-align: right;">0.027</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">est_b_umweltbildung</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.119</td>
-<td style="text-align: right;">0.154</td>
-<td style="text-align: right;">0.049</td>
-<td style="text-align: right;">0.059</td>
-<td style="text-align: right;">0.085</td>
-<td style="text-align: right;">0.112</td>
-<td style="text-align: right;">0.153</td>
-<td style="text-align: right;">0.196</td>
-<td style="text-align: right;">0.069</td>
-<td style="text-align: right;">0.083</td>
-<td style="text-align: right;">0.034</td>
-<td style="text-align: right;">0.042</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">est_b_zugang</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.058</td>
-<td style="text-align: right;">0.060</td>
-<td style="text-align: right;">0.004</td>
-<td style="text-align: right;">0.005</td>
-<td style="text-align: right;">0.055</td>
-<td style="text-align: right;">0.056</td>
-<td style="text-align: right;">0.060</td>
-<td style="text-align: right;">0.063</td>
-<td style="text-align: right;">0.006</td>
-<td style="text-align: right;">0.007</td>
-<td style="text-align: right;">0.003</td>
-<td style="text-align: right;">0.003</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">est_b_gestaltung</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.384</td>
-<td style="text-align: right;">0.336</td>
-<td style="text-align: right;">0.025</td>
-<td style="text-align: right;">0.066</td>
-<td style="text-align: right;">0.366</td>
-<td style="text-align: right;">0.289</td>
-<td style="text-align: right;">0.401</td>
-<td style="text-align: right;">0.382</td>
-<td style="text-align: right;">0.035</td>
-<td style="text-align: right;">0.094</td>
-<td style="text-align: right;">0.018</td>
-<td style="text-align: right;">0.047</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">est_b_beitrag</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">1.136</td>
-<td style="text-align: right;">1.067</td>
-<td style="text-align: right;">0.107</td>
-<td style="text-align: right;">0.092</td>
-<td style="text-align: right;">1.060</td>
-<td style="text-align: right;">1.002</td>
-<td style="text-align: right;">1.211</td>
-<td style="text-align: right;">1.132</td>
-<td style="text-align: right;">0.151</td>
-<td style="text-align: right;">0.131</td>
-<td style="text-align: right;">0.076</td>
-<td style="text-align: right;">0.065</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">est_asc_klein</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.378</td>
-<td style="text-align: right;">0.485</td>
-<td style="text-align: right;">0.124</td>
-<td style="text-align: right;">0.257</td>
-<td style="text-align: right;">0.291</td>
-<td style="text-align: right;">0.303</td>
-<td style="text-align: right;">0.466</td>
-<td style="text-align: right;">0.667</td>
-<td style="text-align: right;">0.175</td>
-<td style="text-align: right;">0.363</td>
-<td style="text-align: right;">0.087</td>
-<td style="text-align: right;">0.182</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">rob_pval0_asc_gemeinschaft</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">rob_pval0_b_groesse</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.030</td>
-<td style="text-align: right;">0.075</td>
-<td style="text-align: right;">0.042</td>
-<td style="text-align: right;">0.106</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.060</td>
-<td style="text-align: right;">0.150</td>
-<td style="text-align: right;">0.060</td>
-<td style="text-align: right;">0.150</td>
-<td style="text-align: right;">0.030</td>
-<td style="text-align: right;">0.075</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">rob_pval0_b_entfernung</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">rob_pval0_b_gemeinschaft</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.010</td>
-<td style="text-align: right;">0.010</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.014</td>
-<td style="text-align: right;">0.010</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.010</td>
-<td style="text-align: right;">0.020</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.020</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.010</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">rob_pval0_b_kultur</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">rob_pval0_b_umweltbildung</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.020</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.028</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.040</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.040</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.020</td>
-<td style="text-align: right;">0.000</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">rob_pval0_b_zugang</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">rob_pval0_b_gestaltung</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;">rob_pval0_b_beitrag</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;">rob_pval0_asc_klein</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">2</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-<td style="text-align: right;">0.000</td>
-</tr>
-</tbody>
-</table>
-
-
-</div>
-</div>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>powa</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>$bayefficient
-
-FALSE TRUE
- 50 50
-
-$efficient
-
-FALSE TRUE
- 50 50 </code></pre>
-</div>
-</div>
-<section id="illustration-of-simulated-parameter-values" class="level2">
-<h2 class="anchored" data-anchor-id="illustration-of-simulated-parameter-values">Illustration of simulated parameter values</h2>
-<p>To facilitate interpretation and judgement of the different designs, you can plot the densities of simulated parameter values from the different experimental designs.</p>
-<div class="cell">
-<div class="cell-output-display">
-<p><img src="data:image/png;base64,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" 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V1ZP3dUUCegkXIBqNB+ckDLHYACqDIAGtpbBDRWUAAV2k8NaEE/AVQZAA3tTQIaKSiACu0nB9R04SRO13gANLQDqLBxdNtmZsMBqFQ//EbJA1AAVQZAQ7uufnZZA+jklgNQqX7wdVE/AVQZAA3tSkBnl2rJdX9UDqBCql44s/WHX5b1E0CVAdDQrqqfX9eVAhonKIAK7QDabgA0tOsAnV+qRRf+sFzRHqMXgArtANpuADS0a+ojlnW1gMZcTQ+gQjuAthsADe2K+phVXTGg83wBqNB+SkAL+wmgygBoaNcAGrNUy678Qbmqfd4vABXaTwhoaT8BVBkADe359VHLumJA50/iAVRoPyWghdbLLgCqC4CG9uz6uMOCqgGdAwxAhfbTAVr8ABRAlQHQ0J4PaNxSLb32D8qV7QCqaD8hoGVWy14AVBcADe259ZGHBXUDOiNYpZs+tt+2/WSAlj8ABVBlADS0ZwMauVRLr/2Dcm37jKCVbvrYftv20wFaZK0cBEB1AdDQnlkfe1gAoNPtACrU7/5mcAAKoMoAaGjPq49e1QA63Q6gQv32LxZ+AqgyABraAVTYOFnbJjYAKtVv/2LhJ4AqA6ChvWlAZwStctPH99u2nwZQkwNQAFUGQEN724DKgla56eP7bdtPAqiNnwCqDICG9qz6+HVdO6DitaA1bvqEftv2UwBq5CeAKgOgoT0P0PilWnThD8tLACoIWuOmT+i3bQfQdgOgoT2nPmFdVw+oJGiFmz6l37YdQNsNgIb2LEATlmrJdX9UXqQdQLPaAbTdAGhoz6hPWdYeAJ0UtL5Nn9Rv2w6g7QZAQ3t6fdKqdgDo9CFodZs+rd+2/QSAWvkJoMoAaGjPADRpqZZb9SPlhQCdErS6TZ/Wb9tuD6iZnwCqDICG9uT6tGXtAdDJQ9DaNn1iv237CQAtc++OBEB1AdDQng5o2lIttujHyksBOiFobZs+sd+23RxQuwNQAFUGQEN7an3isnYB6JSglW361H7bdmtADf0EUGUANLQn1qcuax+ATgha16ZP7rdtNwe01H07EgDVBUBDO4CuA6DJ7cb1lgegAKoMgIZ2AF0HQJPbrQEtddeOBUB1AdDQDqDrnB5QPlROjOkBKIAqA6ChHUDXAdDkdtN6Wz8BVBkADe1p9ckL2wmg44ICqNAOoO0GQEN7Un36uvYC6KigACq0A2i7sQP0wrP46HJz4kXRAXra/0lHWZx6pZ37P9hXOAIN7Sn1GccFbo5Axw5BOQIV2m03jum64QhUGQAN7UmAZizVEut9srxoO4AmtZtuHNt1A6DKAGhoT6jPeWDKE6BHngGo0G5YvwDQugOgoT0F0JylWmC5T5eXbQfQlHZLQI3XDYAqA6ChPb4+65lRV4AOQQNQod2ufgGglQdAQ3sCoFlLVb/ahfLC7UPRAFRoN6vv/qEG0JoDoKE9uj7v0jxXgA4FBVCh3Q7Q7q4tfM8eBkB1AdDQHg9o3lJVL3apHECF1LJwErMA0OoDoKE9tj7ztSEAOrk9AXQ0/ToD0JoDoKE9GtDMpapd62I5gAqpZOEkZgGg9QdAQ3tkfe6LkwF0coMC6Fg26wxAaw6Ahva4+uw3d/AF6EBQABXabeoXAOogABraAfQgB6gBqNBuUr9bZwBacwA0tAPoYQA0st2iPiwzAK05ABraAfQw+6oBqNAOoO0GQEM7gA5yIkD5ULmRAKiPAGhoj6rPf4Nwh4AGQWvY9Ip+23YAbTcAGtrjAM1fqqqFPldu0g6gUe0A2m4ANLTH1Cs+ocYjoDtBK9j0mn7bdoP6vXUGoDUHQEN7FKCKpapZ57PlNu07QSvY9Jp+2/by9fv/TgNozQHQ0B5Rr/mIRI+AfgigEe0GgO7ftTb37LYdQFUB0NAeA6hmqSp+d77cCtBFLZte1W/bXrx+AaBeAqChfb5e9RndLgH9EEDn28sDenDXGt2zm3YAVQVAQ3sEoKqlqvnl2XIzQBd1bHpdv2176foFgLoJgIb22XrVAahTQDeCnnvTK/tt24sDenjXWt2zfTuAqgKgoX0eUN1SVf32XDmASv227YXrFwDqJwAa2ufqdQegAKrY9Np+2/bSgA7uWqt7tm8HUFUANLTP1Cv9BND8Ta/ut20vWz9cZwBacwA0tAPoWAB0tr1o/dEyA9CaA6ChHUBH0wnqiaCRftt2AG03ABraAXQ8a0E9ETTSb9sOoO0GQEM7gE4EQOV2AG03ABra5Xqtn54BXbgiaKTftr1k/fEyA9CaA6ChfQZQ9VLVFojlpu0AKrYXBfT4rrW8ZwFUGQAN7WK9+gDUNaALRwSN9du2F6wfWWYAWnMANLTLgOqXqrpBKrfdzUwB5UPlQkaWGYDWHAAN7VK9/gDUN6CWwAHoLmPLDEBrDoCGdhHQAktVXyGU2+5mHwKo0F4Q0LG71vSOBVBdADS0C/UFDkCdA2ooHIBuM7rMALTmAGholwAtsVQLdEyXGwP6kSFxALrN6DID0JoDoKF9ur7EASiATm9dAO0yvswAtOYAaGgXAC2yVEuUTJYDqBAfgE78Mw2gNQdAQ/tkfZEDUACd3rwAus7EKgPQmgOgoX0a0DJLtUjLVLk1oIZPIwFol6l/pgG05gBoaJ+qL3MA6h1QuyuZALTL1CoD0JoDoKF9EtBCS7VMzUT5CQC1Qg5A15n8ZxpAaw6AhvaJ+kIHoO4BNTsEBdA/SqsMQGsOgIb28fpSfl4AoEbKAegfAdRrADS0A6jQv/5/ADreDqDtBkBDO4AK/ev/Z8QcgP4RQL0GQEM7gAr93f8H0NF2AG03ABraAVTo7/6/jXMAKq4yAK05ABraR+uL+XkJgNoICqDipXIAWnMANLSPA1puqRZrGis/DaAmJ/EAKv4zDaA1B0BD+1h9uQPQCwHUQDoAFf+ZBtCaA6ChfaS+oJ+XAajN80itf6icuMwAtOYAaGgHUKF/+xeTY8XGAZVXGYDWHAAN7cf1CwDd9u/+ZiFo64BGbnqTOxZAVQHQ0D4GaMmlWrDruBxApX7bdmX9zCoD0JoDoKEdQIX+3d8A9KhdC2jspje5YwFUFQAN7QAq9O/+BqBH7br6uUUGoDUHQEP7UX1RPwE0ZdMX7rdtV9XPLjIArTkAGtqPAS27VIu2DctPBqiFoAAat+kt7lgAVQVAQ/uwvuwB6MUAanApKIBGbvryAVBdADS0D+oL+3lBgBYXFEAjN335AKguABraAVTo3/8CQA/bAbTdAGhoB1Chf/+L4oegDQM6v8oAtOYAaGgHUKH/4KvSgrYMaOKmLx0A1QVAQ/thfdGXcXZLtWzdoPykgJY+iW8X0IhFBqA1B0BD+xDQ0ku1cN9h+YkBLStow4Amb/rCAVBdADS0H9QXPwC9KEALH4I2C2jMIgPQmgOgoX0AaPGlWrrwoPzUgBYVtFVAoxYZgNYcAA3tACr0D79RVNBmAc3a9EUDoLoAaGgHUKF/+A0ADe259XFrDEBrDoCG9v368n4CaOSmN0itgOZt+qIBUF0ANLQfAGqwVMtX7pU7BrTVD5WL/EcaQGsOgIb2vXqDA1AAnUqzgGZu+qIBUF0ANLTvA2qxVA06Q/mpAS0oaKOAxv4jDaA1B0BDe6i3OAC9NEDLXQraKqD5m75gAFQXAA3te4CaLFWL0l35GQAthV6bgEb/Iw2gNQdAQ/uu3uQA9OIALXYI2iSg8WsMQGsOgIb2bb2NnxcIaCH1ADR505cLgOpSCaCLYcbvrLT7NjEAKvWPfbMQewCavulH7ovJyO0AqkqtgI7uVKfZD8q/jchmqZq0bsvPAWihk3gAzdj0e3dCXKbaAVSVSgD9u0HGGT0VoLFLOy2XCGgR9wA0Y9Nvtn/InanIigKoLpUCOlB0e2el3beJAVCpf/zbAPrHPEATFtnUv13zdI4zOmgHUFVyAf38jW9s/7Lscuv+4BZJd8MooIeM/vFE+4HVGfxlAloCvgYBTVljY69hSLLzWNH9dgBVJRfQe8sNoJ/eNgZ0z9ATARq/tpNyiYAWOQRtEVDFps+zcwpRANUlD9Bn95ZbQB9v/zJM0t0gAzo4EDUKgEr9E98vcYe0B2jSGht8IKoGzxFDAVSXLED/8NPlDtB7y++N3yjpbpgH1N5QAJX6p34AoDmAZm36zPN2GVEA1SUH0PeXy+/+6wbQZ2++9PPxWyXdDVGA/t33t4im3cfRS6H7A0BH+6d+UODOaA7QtCX20XZDl9PzwNDh/17qf3zbyQL06/+4O3P//I2X/2V1PPq37xzdKuluiAY0HIim3c9RS2H9/838vFBACwjaHqDJm744nvuIDv73Uv/j207uk0hbQLfPIS1/tv3J1zZJqvt+UrbPKVlkdU5DUqK/H+zuzDqzSF1j/XJ/3SbbM/ldzr15fEUL6OPV6fwHV//+1nJ3Jn8CQLeIZs4uJXlxNx8ATU3iErPUMwgahjr35vEVLaDvb/48fi4p6UQg5RR+/zmltNONuZMRyxN4TuEn09gpfOIaMzp33+b114dn8qn/8W1HC2j4+uUPDm+RdDfkAFr6wVAAlfonf1LiDmjpQ+VSlpjZQ59DQPcJTf2PbzulAP309q37h7dIuhuyAC38nPxq5JHnJMvlQgEtISiAjm/Y/uz9JIDe2T0jn/of33bKAXr6I9CAaNp9PrkUTA9ALxbQApeCAujIVt0efJ4I0N1lTan/8W1HCeizNzdPvx+/ICnpblAA+neljkIBVOoXfqbf+gA6slG35+4nA3RLaOJ/fNvRHoHeG0AaknQ3aAAtRSiASv3SDwE0IVFrbP+hzxMC2iGa+h/fdgpcB/rdD66e/PToOaRTAlqG0PX1NIW4Gc0FA6rf9MqCuX7b9pT6mDV2+NTRiQG9k/of33bUj4G+v3kzpqOXIiXdDVpASzwUutoYpbgZzeUCqha0HUAj/Bw+8w6gNUf/JNKTv18uX/ru8Pjz5IDqCTU+AL1kQLUn8Q0BOruljy5cAtCaU/M70icCqj2PN/bzsgHVCdoMoLNrbOS6TwCtOZcEqJJQAJX6Z34OoFGZWWKj180DaM25LEBV5/EAKvXP/Fz56EkjgMpLbOJlRwBacy4NUAWhACr1z91AJWgzgIobcOJVmwBacy4P0OzzeACV+uduAKDzkVbY9IveAbTmXCCguQehi3MbpCo/9/AAOh/Zz0niALTiXCSgHaFp62B9eHBug1Tl5x4eQGcjHIBK77kEoDXnQgHNOAgd+XitsgHQyTQC6PS2k96zDkBrzqUCmizo2OcTls1lA6oStAlApw5A597zE0BrzsUCmkjoanWf3yBN+fmHB1A5gp8ycWZ4du0AqsoFA5r0dDyAzvTP30RxCNoCoOMHoBHvOA+gNeeSAU04CF0A6Ex/xG0AVEi2nwBadS4b0Oin49fLuwaD8ssrGD73ELSJD5UbBTTqA48AtOZcOqCRggLoXH/MjbJfAtYmoJEfGAegNefiAY07jQfQuf6oWwHoZI4Ajf7ATQCtOZcPaIyg3equw6Dc8iqGz3OwBUCPD0Aj+QTQutMAoBGn8d3qrsOg3PI6hgfQiYz4GU1cWTGH7QCqShOAzh2ELgB0tj/uZpnv4nLxgA4PQKMPP+8AaN1pA1D5ktDN6q7EoMzySoYH0NEc+hn98GdPXFEwj9oBVJVGABVP4wE0oj/ydllvg3XxgB4egCbxCaB1pxlA14ROrW4Ane+PvSGAjmToZxpxxbAcbQdQVRoCdOogdHt4UI1BWeW1DJ9B4cUDenAAmuongFadxgAd208BNKY/+pY578R66YAebJ5EPwG06rQE6Pgx6AJAY/qjbwmgw+wfgKb7CaBVpy1Axw5Cd8u7HoNyyqsZHkAH2fMz8emjDXFFoJxsB1BVGgN05BgUQKP6o28JoIcJfqZdvRSIK+HkdDuAqtIaoMdPxgNoVH/8TZMxbATQPD4BtO60B+jgIHQBoFH9CbcF0P1s11cmnwBad9oEdG93DWdYNRmUXl7R8Mkf59cCoNl+AmjVaRDQg2PQBYDG9afcGED30i+wfD8BtOo0CejeQejeU6RVGZRcXtPwiRxeNKBqPwG06rQJaBAUQCP7k24NoLus11fu00cb4jQ+zrcDqCqNAro7jQfQyP6kW6d6eLkfKrdeXzo/AbTqNAtoL+j+i0TqMii1vK7hAXSThe70vSNO9duz7QCqSruAdoLuv0q5MoMSy+saPvEQ9GIB7Q9AlcTpfn2uHUBVaRjQ7oHQfSPqMiixvLLh0wS9VEBL+AmgVadlQAeC1mZQWnltwycJerGAqs/f7wBo3Wka0BsHglZnUFJ5bcMD6ObyAgC96LQM6I0bN+5wIX1kf+ovAGgPaAHi9BVSO4Cq0jagq+UCoHH9qb8AoIX8BNCq0zygd3gzkaj+1F8A0EJ+AmjVaRjQGz2gu7P46gxKKq9u+BRBLxHQQnwCaN1pGtB+wWyfSarPoJTy+oZvHtBSxBXqmWgHUFUA9M72GLRCgxLK6xs+4RD0AgFdLG4MZcolrlDPRDuAqgKg63SCVmhQQnmFwzcM6KKcn7/FXWQAACAASURBVABadQC0y1rQGg2KL69w+PhD0EsDdAGgraRlQPdXzXrJV2hQfHmNw0cLemGArtbSwT/PSuJKFY23A6gq7QI6WOEAKvdn/VabgK79BNBG0jCgw4Vz8M4iBmkR0EhBLwrQwn4CaNVpFtDjFf66saANAhp7CHphgI7986wgrlzVWDuAqtIqoCNHCK8vbAVtEtA4QS8J0N5PAG0kALq3lBamhLYIaOQh6OUA2p2/Fz0ABdCq0yigN0YBvWMqaJuARgh6QR8qt1gUPwAF0KrTLKATS8lQ0CYBjRH0cgDdHn+W9BNAqw6ADpYSgI73Z//mvI0XA+jGz6In8ABadwB0uJTMBAXQiVwKoDs/AbSdtAno6BLfLiUrQQF0IhcC6NbPwgegAFp1GgVUWkpWD4MC6EQuA9DgJ4A2lCYBHV/iu6Vk9Fw8gE7kIgA18xNAq06LgE4s8bCUbARtFNB5QS8B0J2fANpWAHR0KVkI2iqgs4JeAKDBTwBtKw0COnYR/dFSMhC0WUDnXo7kH9A9PwG0rTQJaMxSKi9ow4DKPLoH1NRPAK06ADq1lAD0oF/125cN6L6fpa9hugOgdac9QKfO4I+WUmlBWwZU9NE5oId+AmhbaRHQ2KVUWNCGAZUPQd0Durcuy/sJoFUHQKeXUuGHQZsGVALSN6ArP78/v7g0xJWv3G8HUFUAVFhKZQVtGVBRUM+Ars/fA6AWfgJo1WkO0MmHQEeWUllBmwZUEtQxoIsBoKV569alRWloB1BVGgQ0fimVfU0SgAoK+QS0f/5oB6jJASiAVp3WAJ0+AB0FtOQxKIBOM+QT0M3z7wHQorTt1qVJ664dQFVpD9CEpdSlmKAAOu2QS0C31y9tAbU5AAXQqgOgwlLqU0rQtgEVn0Zy+aFyu+s/d4AWU+1wXdrUbtsBVJXGABXO4AVAywjaOKDStaAeAQ3Xz28ANToABdCq0xygKUtpk0KCNg/otKA+Ad0ty+9b+gmgVactQKUD0GlACwnaOqDCIahDQPdev9kBauYngFad1gBNWkq7FBEUQCcF9Qfo/uvfAbThAKiwlEJKCNo8oNOCugN0AaDbTdt4AFRYSnspICiATp7EewP0wE8AbTlNASovchHQAoIC6OQhqDNAD/3sALXzE0CrTluAJi6lgwBogUwI6gvQgZ89oDrGxHVpV30HQLVpCdCZo4QZQNWCAuiHUyfxrgAd+rkG1PAAFECrTkOAzi3yOUC1ggLoh1OHoM4AHS7L71segAJo1QFQYSkNonwYFEDXcQ/okZ8rQC0PQAG06tgBmpTvnyA3bryuzErQj4gui0pWXG7Wb0BffmXVlHNvYV/hCFT4t/goqmNQjkC7jJ3E+zkCPXoAdL0sLY8/OQKtOwAqLKXjaAQF0C6uAR3z0/QZpDsAWndaAjR5KY1EISiAdvEM6ISfxsTZtgOoKu0AOnucEAWo4ql4AO3iF9DFlJ8A2m4aAjR9KY0mW1AA7TMiqAtAx/0E0LbTDKDzD1TF7ge5ggLoJseCegB0nE8AbTytABrxQH88oHmCAug2TgGdWlkA2nAAVFhKE8kUFEC3GRyCuvhQuWk/4xdOXgC05jQCqPhW9JNLaSp5ggLoLodeegBU8BNAW04zgOYspclkCQqgId4AnXoAtHtqEkAbDoAKS2k6OYICaMgBmPUDOu0ngDaeNgCNOYNP2w8W6YQC6F72xawe0Ek/+2vjALThtAJo1lKSAqC67JFZO6CCnwBq5oeTNAFo1AFo8n6QKiiA7scPoNN+bl6cAaANpxFA85bSTBIFBdD9eAJ0Zl0BaMMBUGEpzSTxLB5A9+MG0Fk/ATQjDxZf+M38re4unnuY+T9wqgCosJTmkiYogO7HC6DTfu7eXQFA0wOgs0m6G0wBjXy/xoz9IElQAD1IMLNmQKUHQAH0jwDaBKC5S2k+KYIC6GF2aFYMaIyfAJqROEAdpAFAY98wPGs/SBAUQAepH9AoPwE0IwA6m6S7wRDQ6A9cyNsPADQ7WzWrBVTwE0C3mzYvADqbpLvBL6DxggLoMBs2awVU8hNAt5s2Me/95WJx7YWHO0A/+eHN1Te++uvNj7svF1/5cf/V7jHQg9/67JXFi09/+fxi8cVvnf8RUgAVllJcYgUF0KNUDajoJ4BuN21Snv6gu7MXX/jhBtAHi01e6H7+7vbLL3U/3QA6+K0VoP/rN/dvds4AqLCU4hL7MCiAHqV3s05Ao/0E0PjcXUn58Orp22sN1/at/FwffP7p7V7Qj28unlt9+fQXi8X1/uYdoIPfWgG6uPafHl59snL1xeJuJQZAhaUUGQDNTgdnrYDGLioAjc0KyF68Bz2Fq6+vX22+ce0ne4+M3u3/0gM6/K01oK+tv14dmZ79MqcGAM1fSrGJExRAR1ItoKKfh4sKQGNzdwvkysD138JTSSsLr28Z3b/9GsgHg99a/bFx8+75n4q6eECjD0A1+0GUoAA6krWcNQI64yeA7jZtQnolu3T07X3dW/lo/XzSP4df6L45/K01oLvjVgDtYwioYinFJ0ZQAB1LR2d1HyoX/wCocuFE5HIADfL19K3PxUPWFr7d/e2L3/p9f6sO0PWT7vu/tfcNAN3GDND4A1AloPOCAuhoagRU9nP4jzKARmZI4TGg3fVK3dPr3YVMABobK0AT/NTtBxGCAuho1oeglQE65yeA7m3ahIwcgV4/vtXvfvT8mtD100RbQA9+C0BH4h7QCEEBdDzVAZroJ4DGZuQx0PFn0dfXMa1/MvkYaO2AfvYXL/xe25x0NxgBGvdW9JNLKSkAmpnVIWhVgM74efyoOoDGZgfeCsX13+7unnXvLD2icvJZ+OoBfUX/Oqmku8EMUNVSSsucoAA6kcWiMkAT1xSAxmZ33We4DnRzCPqoO2ffXca0OTadvA60dkCfvr15JFdhaNLdcAmAzgkKoFOpCtAZP0cu6wDQ6PSvPDp4JdL66aLtKfuKxmvfXv25fo3R7jHQ7laDVyLVDugqv+ufDtu+rD89SXeDDaBJZ/AlABUFBdCprA5B0/ba1CTUz/oJoIebNi1vb55k/y/D18L3R6If39x+3RG5fS383YPf8gHo1e6Sgt07paQl6W6wAlS3lFIjCwqgkzG+jj4B0NkHQEeWFIAm5HdH78b0/P5x2tNffnl9IejmKZjpd2Pqb105oKv/nPe6dz259q2Mp5SS7gYTQNMOQEvsB6KgADrdXhGgM2vKZuFIuSxAVanwIz5mL2P6U/cGfYuvJB+GJt0NRoAql1J6JEEBdLq9lpdyZpzAA6h17u49dz9y3eh5M3ME+svndy8UeCGxOeluuBRAJUEBdLrdWNDYxZjlJ4Aa58HmzZfC0/EVRQB0q+f6ufj1G/Ylzp50N1gAmngGX2g/ANCc9o9sHwaNXIxzfk68sQKA2mbl5rVvrzx69+b5H/I8yhSgm0c/w9Wgj1Iffki6G2wA1S6lrEwKCqDT7VUAOvcE0tSKAlDjPNo+N/+lvOezLTNxHWj/Hvr7Tx599kqi/kl3wwUBOikogE631wBorp8Aap4/vd0/VV/bM0hX0iuRrh1evvTZKxyBxmXqYVAAnW5fA2ooaMxizPYTQFvOFKD7b2va5ek/JV7KlHQ3GACa+hBouf1gQlAAnW7/6ENTQeMAzV1PANpwLvfdmFL9LLgfjAsKoNPt6/rzAprvJ4C2nInHQH/01+F8/eO/+l9yHntIuhvKA5p8AFpyPxgVFECn27t6O0HnF+P8E/DTywlAG87UKfzeM0bJTx/1SbobLADVL6X8jAkKoNPtfb2ZoLOLcfYBUACd2rSNJwLQj28CaHJGBAXQiXRXfPR/S9t54/fymZ/P+wmgU5u28RwBevg5JfvvlJKYpLvh4gAdERRAJ7IHqJGgM4tR5yeAtpzjI9BHx4C+ltOcdDcUBzTdz9L7AYDGJgBqJai8GJV+AmjLOQb06f/96qt/dfPan726zd8Mr2iKS9LdUB7QEktJl6GgADqRPUCNBBUXo9ZPAG05EY+BZibpbigNaMYBaPn9YCAogE7kEFALQWcAnV9NADq5aRtPxGVMmUm6GwoDmuOnBaAHggLoRPYBtRFUWozzfs4tJwBtOJd5IX0dgA4EBdCJHABqchIvLEa9nwAam39LihVMZTME9OmPXl0dfK7+/36yDkeT7oaygKZfRD++lNQ5EBRAJ3JOQNUPgNosnIN62/ZTAnr8/PR0nALaf/Ty4Fomb9eBZvlpsh/sywCgExkCWl7QycUY4ef885EAGosNgCYk6W64VED3BQXQiRwCaiHo1GKM8hNAxU2bgs3i/4uNW0DLJeluuFhA7wDobAaAGgg6sRjL+AmgsQHQlCTdDUUBzfPTaj/Y2QCgExkCWl7Q8cUY5yeAyps2IQCakqS7oSygpZZSmWxxANCJHAFa/Imk0cUY42fUP8YAGpkGAX36P371T5nNSXdDSUAzD0ANAV2YG3RhgJYWdGwxFvMTQKOxaQnQT/6Ph1dXn60/We5LP8lqTrobCgKa66fdfrDhAUAnMgJoYUHHAS21lgA0FpuGAH3QPfV+N/9JeAAN6X0A0ImMAVr2ne1GFmM5PwE0Gpt2AH3UsfnxzcVzDz95Je/T7JPuhssGtBcUQCcyDmhJQY8XY5yfADq/aVOwaQfQu917gD5aXPtJxifC90m6Gy4c0E5QAJ3IKKBFBT1ajKWegDdfOHcAtO5MfS78Ws4No84+0qNKQNeCAuhEpgAtJ+hwMRb1E0CjsWkG0N7Mz15ZXL/yB2i5pVQyY0YUzOUBWlLQwWIs9wT8CRYOgFYdCdCPb3bvRe8L0OwDUOv9AECF9tH6coIeLsbCfgJoNDbNANqfwj/oHgJ19hhotp/W+4GtoJcIaDlBDxZjaT8BNBqbZgC9uru4vn76fS2nr2fh8w9AzQF93VLQiwS0mKADQGOWEYBGbtoUbDSA3k3/aLb/fnOxtqv748FQsaNvHOTpL1+I+9+Yvoxpndeunv5w0R+HJifpbigFqMLPUwBqJ+hlAlpK0P3FGONn2joC0MicGNAOsRc3fyQC+qh7+ici0xfSr3K9eyLpWtaHcgLoUb/lE0kXCmghQfcWY3k/ATQ2Jwb0weo0OvyRFjWgV5/86K9/vPrjs7/46q+T/9e7JN0NhQDNeyv6yaVUNq9bPhV/qYCWuRw0LMbiD4DeAdDonBzQF/f+SIseUHWS7oZigBZdSmWzrjcT9GIBLSLobjFa+AmgsdEC+t7zi2vf6o8m3/vL1V35xW89DM9xP+jI++SHNxeLr6yP+fpHIa9v/tgy+t43V7/27f723TfCL6z/F959frFYHTE+/cHmBDzivyllAyQl6W5oBVAzQS8X0BKCbhdjpJ8AmrBpE6IE9NXFjrW3N5+VcX13leXKvNe6Ky/XWT/qOArog/7X+r+u///eL6z+F768ee+PQoD+z21+n7KVtkm6G5oB1OpiposGVC3oZjHa+AmgsVECunju11fvdtemP1pcWz+++G73/PbdcLX6Z68sXnh49fQX/fsfHZzCd/9/xeWfP1z92vrH3TcOfmH1v3D94e5/QXkK/8kPFyFeLqRX+XkiQI0EvWBACwjaL0YjPwE0NkpANyquztjv9jT2R529dd3/f7Bhb8/Mgy82P+/I3f9G/0X/yvW+XAvo4afKuQG08FIqm229iaCXDKhe0G4xWvkJoLFRAvpiL9NGoz/9j//zm+sLLTffWaO4eQuP9ZHmmsIjQHtwN+m/sf8Ld3s0O1S1gD5YLL70N7/a5p98vBJJdwB6QkANBL1oQNWCrhejmZ8AGpsSz8L35n3yzcX2SvX+J52i/UOXu2O+I0A/e2XvkvaNqHu/cHd7ez2gq+LI359O0t1QAlClnycD1ETQywZUK2gPqJGfABqbYoCun/q59pW/+ef+iHKN3aPNReszgO6dS/ffMAL0gOrMJN0NTQFqIeiFA6p8Kv7K0k8AjU0pQNfHdw+vto+BrrR67uHd/mT+gK35I9DDXygKaN7HeOwn6W5oC1ADQS8dUJ2gV5Z+AmhsSjwG+vHNL/xm697qALJD9e61/+sHa7AOHuOMewx0/xeKnsJzBFo8+/XFBb14QFWCXsU8AJr9MjYAjYwS0Oc2L8zcHTg+6q/oXP35Zzc3z//0F9X3F9dPPgv/oH/G/sXBLxQENO/lT4dJuhtaA7S4oC0Ami+oqZ8AGhvtdaDrqzS7dzjqTuGf/mJzSXz3UOb2L8/9+mpzKefkdaDv3dxdxnTwC4eARr6H5wSgq0PQb6dsmpEk3Q1FAC2+lMrmsL6woJcPaPfG9Wk79za2fgJobIq8Emn9NnObN4t77hf9ceIK1M0Z86P+hUXdjUYA3b4S6fruG/u/sA/o+mmq5x5+fHP2THziFP5Hf7VYXPuzVzf5aw+XMWkPQE8MaOHLQRsANFtQYz8BNDbaJ5FWB4qb47rNS9rD6+C3x4v9S9t/fNV/9wjQqdfC/7j/XwiArt9EVAHo4XX0YxfSf/7GNzZ/e/bW7eXyO+8c3SLpbigBaPmlVDbD+qKCtgBo5mn86pdcv40XgM6mwCOOuckF9N5yA+jnbyzXuXV/eIuku0EPqPoA9OSAFhXUM6DdGou/adoO3l3ANPsKjJrfRAFA51LiOe/c5L0b07N7yy2g95Yvv3P15M3lyx8MbpN0N6gB1ft5DkDLCdoIoBnHoOvz97lnH6t+EwUAncu7+pf9ZCcL0D/8dLkF9NPbt+5frY9DX/r54EZJd0OTgJYUtBVAky9n6h7/nHnwvO7LNwBUzt3cDx0qEhHQp+PvY/f+cvndf90A+v7uz+8NbpV0N2gBVe8Eo0upbEbqywnaDqBpB6GLWUD1SwdAY7ExAfQXC/UVQ4pMA7p+z+cv/Oazv/jW0VPw73/9H68eb+C8t/xZ9+f265ATA2qxlMpmrL6YoG0BGi3o5gl48dSl9gfPAbTmTAH69O3+2aPPXll8aexVnRswn725OXX/9Pb2QdCvbZI0xveVuXHjdZ9ZbeSPWk8HaOIvRK7uxWJ24XhdOWZJ2nGT0hKgdxeLL/3Hm1/4zdP/fTF6TT6AFgqApgPa/UbM4p7z8wZ+Hidpx01KQ4A+Wiy+vXlLkc3rogY5BvTW/cNbnPIUvsQZ/FlO4e+UupipoVP47a/Enr9PLpwb2qfft3csp/CR2LQD6N3+haKbN9EfuUhg+gh0m5MCarOUymaqvoigjQEaQ+jeC5DGFk4pPgE0HptmAO2vTN0Aun4DqaNbVAVomR3hfIAWELQ5QPvfivNzbOEU4xNA47FJiWtAezo3gI6+OWhVz8K7OJCY3hFKCNoeoDOE7vs5snDK8Qmg8dgA6C6PB9d/nvM6UB9nYsKOUEDQFgGVCD3wc7hwyp2993csgMbl35KS0ny+TH4m0u7j7sbfGu9xRa9EKrQvnA/QO9mC7NImoJMPhR76ebhwbhT2E0Bj0w6g/RNHPaDjHzC3BfTZm8uvn/m18KV2hjMCqhe0VUDHCR34ebBwCut5B0Cj82/NnMKv30/0hYcdoJ98c/SVprvHPJ+c+92YvDwXMLMjKAVtF9CR8/ihn3sLpzyfABqPTTuAdm/d/OWb1/7s+cVi9L32wpNGT95a+fmd4fEngB73yz8GUF1BMPTIz83CuVH85H1zxwJoJDaL/xwb94Cu35F5k8z3KgXQYf/Mz1WINA3oIaHHfnYLx4pPAI3HpiVAr/70yy+vluIXv/rrzGYAHfbP3UCjSOOAblv6HC8RMzy7OxZAI7FpClBtAHTYP3cDDSMAGgi9MR67OxZA4wKgKTkZoHZLqWzm6xWOAOiubMTOUxtUtt62HUBVmXw7u9/96le/+p85n8a5zYkALXdkcX5AFYJ6BrTk9KstmLfpFQHQWGxaAbT7qM8u1741/qb0ETkVoIZLqWxi6rMFBdAu434CqNAOoKqMANq/l/Iuf555GHoaQP28pDluR8gVFEDXmfATQIV2AFXlGNCnP1iz+cVXV/mr7kB07P2UI3IiQC2XUtnE1QNofqb8BFChHUBVOQb07moZfnV74v7JD1df5X1m6EkAdfSeELE7Qp6gACr4CaBCO4CqcgToxzcPL51/kPuhoacB1HQplU1sfZagALqY9hNAhXYAVeUI0LvDI867ma9FOgWgnt5UJwHQDEEBVPATQIV2AFVlCOj6EdDDj0B6lPko6AkALXtxdC2AZr01U/OACnwCqNQOoKoMAf3sleEZ++qcHkCL9EffMkPQ1gEV/QRQod0poP+9f6ix++PB8Bz56BsHefrLF1KmljMC6OD954+/Exd7QAu/PK8eQDNO4xsHVDp/T9z06QHQWGzKAfqof5uj/o9EQB9lPi0+Gt+AGi+lskmqTxW0bUBn/ARQod0noA8W1x+GP9ICoA0Amipo04DO+QmgQrtXQF/c+yMtALr185IBTbyaqWVAZ/0EUKHdD6DdK8y/sn57ze7MfXF988eW0fe+uVh88dvrv22+EX7h6u7itXefXyy++uvNC4WKEeoaUOulVLg/8fZJgjYM6LyfACq0uwH04/4NOq69NgHog/D+7/039n5hBeiXuy++8BsAbQXQJEHbBTTCTwAV2r0AunLohYdXT3/Ra3RwCt/9/xWXf/7w6t3ux903Dn5hfXn76qc3F6/Zn8IfB0CL9Kf+QsrDoM0CGuMngArtXgB9sGFvz8yDLzY/v7smcv8b/Rd3+2sx767/DqCNAJpyPWirgEb5CaBCuxNAn/5gc336xzfXFB4BujozDy8A6r+x/wt3ezQ7VE0BffqjV4/z1zVeSF/8MxrqAzRB0EYBjfMTQIV2N4AeHM8dAfrZK3svANqIuvcLd7e3twa0XMwBtV9KhftzfilW0DYBjfQTQIV2J4DunRpPALp3ntx/A0AlQMt/SFiVgMY+k9QkoLF+AqjQ7gbQg5eYzx+BHv4CgA4ANfiQxToBjRTUM6CLnHdP+TDBTwAV2p0AevAYZ9xjoPu/AKDNAhonaHuALuL9BFCh3QmgK/v69zR6tBh9Emn7pHv3x+Ybe78AoIeAWnzMd72ARujSJKDmmz6yHUDjor0O9LlfX20u5Zy8DvS9m7vLmA5+4RDQzE8pGv1vKtY0jDGgp1hKhftzfzHGl+YATeATQKV2L4BePdp8VHD3XnTHgG5fiXR99439X9gHdP0Kpecefnwz75M2hv9NBTrGA6DD/uzfjACmNUCT/ARQod0NoJuXtv+4+/sIoFOvhe9+YR/Q9ZuIAugpllLhfsXvzgrTGKAp5+/KTR/RDqBxaeEd6cvFElALP6sGdFbQtgBN9BNAhXYAVcUpoKdZSoX7Vb89Y0xTgKb6CaBCO4Cq4hJQkwPQygGdEbQhQFOuXyqz6efaATQuAJoSS0BPtJQK9yt/X1SmLUBPveln2gE0LgCaEjtAbQ5AqwdUFLQdQNP5BFCpHUBVcQnoqZZS4X51g+BMK4BmHH4W2fRiO4DGBUBTYgao0QGoA0AFQRsBNOPxz0KbXmoH0LgAaEqsALXy0wOg05fUtwFoHp8AKrUDqCoAKiylwv0lSpoGNNdPABXaAVQVABWWUuH+MjXj1rQAaLafACq0nxTQhAAogA76C/WManP5gGY+/Fl000+0A2hcADQlADrsL9Qzyk0TgJ5900+0A2hc/i0pVjCVjTtAzfx0A+joMejFA6rgE0Cl9hMCeonxB+gJl1Lh/nJVx+BcOKCaw887ACq1A6gq3gC1OwD1BOixoJ4BnZ9e8/jnOgA63Q6gqjgD1NBPV4AePRB60YAq+QRQqR1AVQFQYSkV7i/a1g6g2sPPOwAqtQOoKr4AtfgsOWEpFe4v3Hcg6GUDqt1UADrdDqCqeAP0pEupcH/pwv2D0MsFVM8ngErtAKoKgApLqXB/6cL9x0EvFdACh593AFRqB1BVAFRYSoX7y1cGQS8U0AKPf64DoNPtAKqKK0BN/fQIaDgIvUxAy/AJoFI7gKriC9ATL6XC/RalW0EvEdBCh593AFRqB1BVPAFqewDqE9DtafwFAlrOTwAV2gFUFUeA3rjhej8w2xE6QS8P0HJ8AqjUDqCqAKiwlAr3WxWvD9UuDtCSfgKo0A6gqgCosJQK95s1d4KaCndyQIv6CaBCO4CqAqDCUircb9gd84aaOuJs24f1Zf0EUKEdQFUBUGEpFe63LI96T2INcZblQ0ALPn3Ux/XCAdCa4wfQGzd87we2079uLOgJAV0U99P3wgHQmuMIUOf7gTGgr9uexp8W0OJbp3ThYTuAths3gN4AULH8ddsHQk8HaHk+nS8cAK05fgA9w1Iq3G9avm43PI0/FaAGh593nC8cAK05XgC9AaByedce9/mWWcSZtO7aN/UGD3/2W8egc68dQNuNG0DPsZQK95uWb9o9Arpl34pP5wsHQGuOE0BvAOhM+a7dRtATAGrGp/OFA6A1xwugZ1lKhftNy0O7yUGoPaB2fDpfOABac3wAegNA58oPAC1PqDmgln76XjgAWnOcAHqepVS437R8v92A0BMAarl1DLsBtOm4APQGgM6WH7YXJ9QeUNOtY1kOoC3HB6BnWkqF+03Lh+3eALXcOL4XDoDWHA+A3gDQ+fLj9qKCGgJqfwDqe+EAaM1xAei5llLhftPykfaSB6F2gNo/Aup84QBozXEA6A0AjSgfB7QYoVaAbp+BB9Dpett2AFWlfkD3PkrO9X5wekBLEmoDaE8ngMr1tu0AqgqACkupcL9p+UR7KUEtAN3CCaByvW07gKoCoMJSKtxvWj7ZXobQ8oAGNgFUrrdtB1BVAFRYSoX7Tcun24ucxxcHdA9NAJXrbdsBVBUAFZZS4X7Tcqm9AKGFAT0gE0Dlett2AFWlekD3/PS9H5wR0AKEFgV0ICaAyvW27QCqSv2AnnEpFe43LZ9rVwpaENAjLwFUrrdtB1BVagf0BoDGlc+3qwgtB+ixlgAq19u2A6gq8HaKxwAAHm5JREFU1QN6zqVUuN+0PKJdcx5fCtAxKwFUrrdtB1BVKgf0BoBGlke15xNaBtBxKgFUrrdtB1BV7ABNyvcncuN1UjRrqz46Uzooz70ByFzObYGv1H0EengA6vtAooYj0HUWWYeh6iNQ8RXvrjc9R6Atp3JAz7uUCveblse3L3IIVQI684Yhrjc9gLacqgG9AaDR5WntyYSqAJ19kNP1pgfQllM3oGdeSoX7TctT2xMPQ/MBjXm3OtebHkBbTs2ADg9Afe8HlQGaSGguoHFv9ul60wNoy6kY0CM/fe8H1QF6J8XQHEAXcXpmTx8f1wsHQGsOgApLqXC/aXlmeyyh6YDG8+l80wNoywFQYSkV7jctz2+PelY+EdAEPJXTx/XbtgNouwFQYSkV7jct17Qv5g1NAHSRcuxZYPqYftt2AG039QJ67Kfv/aBiQNeZQTQW0Aw9S0w/12/bDqDtpmJAz7+UCveblhdolwyNATQPz1LTi/227QDabqoFdOQA1Pd+UD+g6ywW44zOABp+7azTT/bbtgNou6kX0AqWUuF+0/Jy7YuDzAB6cNsaph/vt20H0HZTK6BjB6C+9wM3gPZZjGTETLWdXVxvegBtOdUCWsNSKtxvWm7UPqplOTk3cb3pAbTlVAro6AGo7/3AJ6C7/jsBU4N2z5seQFtOrYBWsZQK95uWex6e6cV623YAVaVOQMcPQH3vBwAqtDO9UG/bDqCqVApoHUupcL9puefhmV6st20HUFWqBHTiANT3fgCgUzF6YHUvrhcOgNacOgGtZCkV7jctdzw8gMr1tu0AqkqNgE4dgPreDwB0KgAq19u2A6gqVQJay1Iq3G9a7nh4AJXrbdsBVJUKAZ08APW9HwDoVABUrrdtB1BV6gN02k/f+wGATgVA5XrbdgBVBUCFpVS437Tc8fAAKtfbtgOoKgAqLKXC/abljocHULneth1AVQFQYSkV7jctdzw8gMr1tu0Aqkp1gAp++t4PAHQqACrX27YDqCr1ASrd2YYLybdBrocHULneth1AVakNUOkA1Pd+AKBTAVC53rYdQFWpDlDxzrZbR3d8G+R6eACV623bAVSVygAVD0B97wcAOhUAlett2wFUldoAle9ss2XUtTs2yPXwACrX27YDqCp1ASofgPreDwB0KgAq19u2A6gqlQE6c2dbraK+3bFBrocHULneth1AVakK0JkDUN/7AYBOBUDlett2AFWlJkDn/PS9HwDoVABUrrdtB1BVKgJ01k/f+wGATgVA5XrbdgBVBUBDu2ODfA/P9GK9bTuAqgKgod3zXux6eKYX623bAVQVAA3tnvdi18MzvVhv2w6gqgBoaPe8F7senunFett2AFWlHkDn/fS9HwCo0M70Qr1tO4CqUg2gEX763g8AVGhneqHeth1AVakH0Jg722AB7bV73otdD8/0Yr1tO4CqUgugMQegvvcDABXamV6ot20HUFWqATTqzi6/fvbbPe/FrodnerHeth1AVakE0KgDUN/7AYAK7Uwv1Nu2A6gqtQAad2cXXz4H7Z73YtfDM71Yb9sOoKpUAmjknV169Ry2e96LXQ/P9GK9bTuAqgKgod3zXux6eKYX623bAVQVAA3tnvdi18MzvVhv2w6gqgBoaPe8F7senunFett2AFUFQEO7573Y9fBML9bbtgOoKgAa2j3vxa6HZ3qx3rYdQFUB0NDueS92PTzTi/W27QCqCoCGds97sevhmV6st20HUFUANLR73otdD8/0Yr1tO4CqAqCh3fNe7Hl4PlROrrdtB1BVADS0OzbI9fAAKtfbtgOoKgAa2h0b5Hp4AJXrbdsBVBUADe2ODXI9PIDK9bbtAKoKgIZ2xwa5Hh5A5XrbdgBVBUBDu2ODXA8PoHK9bTuAqgKgod2xQa6HB1C53rYdQFUB0NDu2CDXwwOoXG/bDqCqAGhod2yQ6+EBVK63bQdQVQA0tDs2yPXwACrX27YDqCoAGtodG+R6eACV623bAVQVAA3tjg1yPTyAyvW27QCqCoCGdscGuR4eQOV623YAVQVAQ7tjg1wPD6ByvW07gKoCoKHdsUGuhwdQud62HUBVAdDQ7tgg18MDqFxv2w6gqgBoaHdskOvhAVSut20HUFUANLQ7Nsj18AAq19u2A6gqABraHRvkengAlett2wFUFQAN7Y4Ncj08gMr1tu0AqgqAhnbHBvkenunFett2AFUFQEO7573Y9fBML9bbtgOoKgAa2j3vxa6HZ3qx3rYdQFUB0NDueS92PTzTi/W27QCqCoCGds97sevhmV6st20HUFUANLR73otdD8/0Yr1tO4CqAqCh3fNe7Hp4phfrbdsBVBU1oJ+/sexy6/7gBwA67Dct9zw804v1tu0Aqooa0E9vA2hkv2m55+GZXqy3bQdQVdSAPl5+Y/wHADrsNy33PDzTi/W27QCqihrQe8vvjf8AQIf9puWeh2d6sd62HUBV0QL67M2Xfj7+EwAd9puWex6e6cV623YAVUUL6OdvvPwvP10u//ado58A6LDftNzz8Ewv1tu2A6gqWkC3zyEtf7b9ztc2Sap5nRBSRZQiNBYtoI+Xy+9+cPXvby13Z/IASojjKEVoLFpA3988CX/8XBKn8MN+03LPwzO9WG/bzim8KqVeifR4+fIHh98B0GG/abnn4ZlerLdtB1BVSgH66e1b9w+/A6DDftNyz8MzvVhv2w6gqpQDlCPQuX7Tcs/DM71Yb9sOoKooAX325ubp9+MXJAHosN+03PPwTC/W27YDqCraI9B7PZw7SEMAdNhvWu54eD5UTq63bQdQVQpcB/rdD66e/PToOSQAPeo3LXc8PIDK9bbtAKqK+jHQ9zdvxnT0UiQAHfabljseHkDlett2AFVF/yTSk79fLl/67vD4E0CP+03LHQ8PoHK9bTuAqsI70od2xwa5Hh5A5XrbdgBVBUBDu2ODXA8PoHK9bTuAqgKgod2xQa6HB1C53rYdQFUB0NDu2CDXwwOoXG/bDqCqAGhod2yQ6+EBVK63bQdQVQA0tDs2yPXwACrX27YDqCoAGtodG+R6eACV623bAVQVAA3tjg1yPTyAyvW27QCqCoCGdscGuR4eQOV623YAVQVAQ7tjg1wPD6ByvW07gKoCoKHdsUGuhwdQud62HUBVAdDQ7tgg18MDqFxv2w6gqgBoaHdskOvhAVSut20HUFUANLQ7Nsj18AAq19u2A6gqABraHRvkengAlett2wFUFQAN7Y4Ncj08gMr1tu0AqgqAhnbHBvkenunFett2AFUFQEO7573Y9fBML9bbtgOoKgAa2j3vxa6HZ3qx3rYdQFUB0NDueS92PTzTi/W27QCqCoCGds97sevhmV6st20HUFUANLR73otdD8/0Yr1tO4CqAqCh3fNe7Hp4phfrbdsBVBUADe2e92LXwzO9WG/bDqCqAGho97wXux6e6cV623YAVQVAQ7vnvdj18Ewv1tu2A6gqABraPe/FrodnerHeth1AVQHQ0O55L3Y9PNOL9bbtAKoKgIZ2z3ux6+GZXqy3bQdQVQA0tHvei10Pz/RivW07gKoCoKHd817senimF+tt2wFUFQAN7Z73YtfDM71Yb9sOoKoAaGj3vBe7Hp7pxXrbdgBVBUBDu+e92PXwTC/W27YDqCoAGto978Wuh2d6sd62HUBVAdDQ7nkvdj0804v1tu0AqgqAhnbPe7Hn4flQObneth1AVQHQ0O7YINfDA6hcb9sOoKoAaGh3bJDr4QFUrrdtB1BVADS0OzbI9fAAKtfbtgOoKgAa2h0b5Hp4AJXrbdsBVBUADe2ODXI9PIDK9bbtAKoKgIZ2xwa5Hh5A5XrbdgBVBUBDu2ODXA8PoHK9bTuAqgKgod2xQa6HB1C53rYdQFUB0NDu2CDXwwOoXG/bDqCqAGhod2yQ6+EBVK63bQdQVQA0tDs2yPXwACrX27YDqCoAGtodG+R6eACV623bAVQVAA3tjg1yPTyAyvW27QCqCoCGdscGuR4eQOV623YAVQVAQ7tjg1wPD6ByvW07gKoCoKHdsUGuhwdQud62HUBVAdDQ7tgg18MDqFxv2w6gqgBoaHdskOvhAVSut20HUFUANLQ7Nsj18AAq19u2A6gqABraHRvke3imF+tt2wFUFQAN7Z73YtfDM71Yb9sOoKoAaGj3vBe7Hp7pxXrbdgBVBUBDu+e92PXwTC/W27YDqCoAGto978Wuh2d6sd62HUBVAdDQ7nkvdj0804v1tu0AqgqAhnbPe7Hr4ZlerLdtB1BVADS0e96LXQ/P9GK9bTuAqgKgod3zXux6eKYX623bAVQVAA3tnvdi18MzvVhv2w6gqgBoaPe8F7senunFett2AFUFQEO7573Y9fBML9bbtgOoKgAa2j3vxa6HZ3qx3rYdQFUB0NDueS92PTzTi/W27QCqCoCGds97sevhmV6st20HUFUANLR73otdD8/0Yr1tO4CqAqCh3fNe7Hp4phfrbdsBVBUADe2e92LXwzO9WG/bDqCqAGho97wXux6e6cV623YAVQVAQ7vnvdj18Ewv1tu2A6gqABraPe/FnofnQ+Xkett2AFUFQEO7Y4NcDw+gcr1tO4CqAqCh3bFBrocHULneth1AVQHQ0O7YINfDA6hcb9sOoKoAaGh3bJDr4QFUrrdtB1BVADS0OzbI9fAAKtfbtgOoKgAa2h0b5Hp4AJXrbdsBVBU7QJPyOmk3HaDnHoJsc24LfIUj0NDu+CDO9fAcgcr1tu0cgaoCoKHdsUGuhwdQud62HUBVAdDQ7tgg18MDqFxv2w6gqgBoaHdskOvhAVSut20HUFUANLQ7Nsj18AAq19u2A6gqABraHRvkengAlett2wFUFQAN7Y4Ncj08gMr1tu0AqgqAhnbHBrkeHkDlett2AFUFQEO7Y4NcDw+gcr1tO4CqAqCh3bFBrocHULneth1AVQHQ0O7YINfDA6hcb9sOoKoAaGh3bJDr4QFUrrdtB1BVADS0OzbI9/BML9bbtgOoKgAa2j3vxa6HZ3qx3rYdQFUB0NDueS92PTzTi/W27QCqCoCGds97sevhmV6st20HUFUANLR73otdD8/0Yr1tO4CqAqCh3fNe7Hp4phfrbdsBVBUADe2e92LXwzO9WG/bDqCqAGho97wXux6e6cV623YAVQVAQ7vnvdj18Ewv1tu2A6gqABraPe/FrodnerHeth1AVQHQ0O55L3Y9PNOL9bbtAKoKgIZ2z3ux6+GZXqy3bQdQVQA0tHvei10Pz/RivW07gKoCoKHd817senimF+tt2wFUFQAN7Z73YtfDM71Yb9sOoKoAaGj3vBe7Hp7pxXrbdgBVBUBDu+e92PXwTC/W27YDqCoAGto978Wuh2d6sd62HUBVAdDQ7nkvdj0804v1tu0AqgqAhnbPe7Hr4ZlerLdtB1BVADS0e96LPQ/Ph8rJ9bbtAKoKgIZ2xwa5Hh5A5XrbdgBVBUBDu2ODXA8PoHK9bTuAqgKgod2xQa6HB1C53rYdQFUB0NDu2CDXwwOoXG/bDqCqAGhod2yQ6+EBVK63bQdQVQA0tDs2yPXwACrX27YDqCoAGtodG+R6eACV623bAVQVAA3tjg1yPTyAyvW27QCqCoCGdscGuR4eQOV623YAVQVAQ7tjg1wPD6ByvW07gKoCoKHdsUGuhwdQud62HUBVAdDQ7tgg18MDqFxv2w6gqgBoaHdskOvhAVSut20HUFUANLQ7Nsj18AAq19u2A6gqABraHRvkengAlett2wFUFQAN7Y4Ncj08gMr1tu0AqgqAhnbHBrkeHkDlett2AFUFQEO7Y4NcDw+gcr1tO4CqAqCh3bFBrocHULneth1AVQHQ0O7YIN/DM71Yb9sOoKoAaGj3vBe7Hp7pxXrbdgBVBUBDu+e92PXwTC/W27YDqCoAGto978Wuh2d6sd62HUBVAdDQ7nkvdj0804v1tu0AqgqAhnbPe7Hr4ZlerLdtB1BVADS0e96LXQ/P9GK9bTuAqgKgod3zXux6eKYX623bAVQVAA3tnvdi18MzvVhv2w6gqgBoaPe8F7senunFett2AFUFQEO7573Y9fBML9bbtgOoKgAa2j3vxa6HZ3qx3rYdQFUB0NDueS92PTzTi/W27QCqCoCGds97sevhmV6st20HUFUANLR73otdD8/0Yr1tO4CqAqCh3fNe7Hp4phfrbdsBVBUADe2e92LXwzO9WG/bDqCqAGho97wXux6e6cV623YAVQVAQ7vnvdj18Ewv1tu2A6gqABraPe/FrodnerHeth1AVQHQ0O55L/Y8PB8qJ9fbtgOoKgAa2h0b5Hp4AJXrbdsBVBUADe2ODXI9PIDK9bbtAKoKgIZ2xwa5Hh5A5XrbdgBVBUBDu2ODXA8PoHK9bTuAqgKgod2xQa6HB1C53rYdQFUB0NDu2CDXwwOoXG/bDqCqAGhod2yQ6+EBVK63bQdQVQA0tDs2yPXwACrX27YDqCoAGtodG+R6eACV623bAVQVAA3tjg1yPTyAyvW27QCqCoCGdscGuR4eQOV623YAVQVAQ7tjg1wPD6ByvW07gKoCoKHdsUGuhwdQud62HUBV0QP67K3by+V33jn6PoAO+03LHQ8PoHK9bTuAqqIG9PM3luvcuj/8AYAO+03LHQ8PoHK9bTuAqqIG9N7y5Xeunry5fPmDwQ8AdNhvWu54eACV623bAVQVLaCf3r51/2p9HPrSzwc/AdBhv2m54+EBVK63bQdQVbSAvr/8xubP7w1+AqDDftNyx8MDqFxv2w6gqmgBvbf8Wffn4w2kIQA67Dctdzw8gMr1tu0AqooS0Gdvbk7dP729fRD0a5sk9bxOCKkiOhFaSyWAEkKIv5QD9Nb9wx+lnQnEnS4YdO6129bbTu96eKYX623blfU6P/yn/BHoNmXu38M7y6Bzr93zXux6eKYX623bAVQVAA3tnvdi18MzvVhv2w6gqlTyLHzknWXQudfueS92PTzTi/W27QCqiv460O8d/BlS5v49vLMMOvfaPe/FrodnerHeth1AVanklUiRd5ZB5167573Y9fBML9bbtgOoKlpAn725/HqB18JH3lkGnXvtnvdi18MzvVhv2w6gqqjfTORJkXdjiryzDDr32j3vxa6HZ3qx3rYdQFXRvx/ok7dWfn5nePwJoMf9puWeh2d6sd62HUBVqeQd6SPvLIPOvXbPe7Hr4ZlerLdtB1BVADS0e96LXQ/P9GK9bTuAqgKgod3zXux6eKYX623bAVQVAA3tnvdi18MzvVhv2w6gqgBoaPe8F7senunFett2AFUFQEO7573Y9fBML9bbtgOoKgAa2j3vxa6HZ3qx3rYdQFUB0NDueS92PTzTi/W27QCqCoCGds97sevhmV6st20HUFUANLR73os9D999qJxhv/OFU/fwZn44CYCGdscGuR4eQOV623YAVQVAQ7tjg1wPD6ByvW07gKoCoKHdsUGuhwdQud62HUBVAdDQ7tgg18MDqFxv2w6gqgBoaHdskOvhAVSut20HUFUANLQ7Nsj18AAq19u2A6gqABraHRvkengAlett2wFUFQAN7Y4Ncj08gMr1tu0AqgqAhnbHBrkeHkDlett2AFUFQEO7Y4NcDw+gcr1tO4CqAqCh3bFBrocHULneth1AVQHQ0O7YINfDA6hcb9sOoKoAaGh3bJDr4QFUrrdtB1BVADS0OzbI9fAAKtfbtgOoKgAa2h0b5Hp4AJXrbdsBVBUADe2ODXI9PIDK9bbtAKoKgIZ2xwa5Hh5A5XrbdgBVBUBDu2ODXA8PoHK9bTuAqgKgod2xQa6HB1C53rYdQFWxA9Rdvva1c0+giOvhmf58cT18BQHQXVwvJdfDM/354nr4CgKgu7heSq6HZ/rzxfXwFQRAd3G9lFwPz/Tni+vhKwiA7uJ6KbkenunPF9fDVxAA3cX1UnI9PNOfL66HryAAuovrpeR6eKY/X1wPX0EAdBfXS8n18Ex/vrgevoIAKCGEZAZACSEkMwBKCCGZAVBCCMkMgBJCSGYAlBBCMgOghBCSGQAlhJDMtAros7duL5ffeWfqW5+/sexy6/7pR5vPzPBXz367+uI//MMZJouJOP2zN5fb3Lp/junmMrftn/z9cvjzehIz/Ev/7YNzjOY1jQK6AfLW/YlvfXq74p14bvgnG/2/e/rRIiJPXzugkQvnpZ+ffrT5zA3/r/2G//r9o98kU2kU0HvLl9+5evLm8uUPxr/1ePmN8w03l5nhVwZ9/Z2rZ/9PpXvx3Kbv8+ltl9Ovtv3Rz+vJzPAr/FdfPPttncNXmjYB/fT2rftX639+w156+K17y++dZbCYzA3/eNl9cfV+lf8IzG76LiuIqrwD5qYf+Xk9mRv+3kbOe8ufnWM8n2kT0K0t74fd9OBbz96scQfYZGb4lT01r/+5Tb/9Vp1HQXPTb85c6rwPYhdO1adftaVNQO8dL5WDb33+xsv/8tPl8m+rfC5gZvjP37h1/xxjRWZu03f5/I0aAbqan77qI9CZ4XdHDZ/ervNfryrTJKAjS+XwW9vnkGrcjeeGX//fv/5vy+XX//FcE0qZ3fRd6nz0IWL63WOgFc4/NzyA5gRAx771eLn87gdX//5Wjc/DRAD6Vq9/jY8iRgFa5wHcVcz0z37bXwBRoUCzw9/bsH+v0sdPqkzrgN66P/at93dLqT6E5oZ/3O+/qz25RoVmN/06j2vdg+en//Sn/ZVAFT74Mzv86ryrXzjL7c/JbFoHVDiPvKpzT54b/vH20POex/PI/hs1PnSyTsxjP9X+4zW/6d/vz1z+K6fw8QHQyW9d7f9bXU/mT+FrfigrZtPXuNX7RJwF1/uPV8Sm/0P3zGmVC6fWNAlo3FPBV5UaNDP8jp86HYrY9BVfRuP6iezYVV/x9q8vbQK6vRLu4Iq48K26r4ibHX6zE9f4+MPs9OvU+MjzJrHbvkpAIzZ9l4q3f31pE9D512RUfD105PCV7gfzr0Sq+VUM89u+3lP42eE3itb6Kto60yag/cvFD14VfPCt/rmAJz+t8hguYvjvvFPpExnz019V/UqAuekf130FxNzwL/3D1dUfblf5D2+taRPQ7RsW3bp/tfsXd/9b2+cjb1V4Ncr88I837whU4dHz1fz0dZ7+bhO5cOo0aG74/hrWKo8aqk2jgF49WV9s/p3Nk7794cLetzbvjFjj5dDrzA6/fo/HOl+HejU/fZ2P3W4zN/3/u34/0Fq3/dzw61ewVfs+snWmVUAJIUQdACWEkMwAKCGEZAZACSEkMwBKCCGZAVBCCMkMgBJCSGYAlBBCMgOghBCSGQAlhJDMACghhGQGQAkhJDMASgghmQFQQgjJDIASQkhmAJQQQjIDoIQQkhkAJYSQzAAoIYRkBkDJZN77y8Xi2gsPHyy+8Jurq6c/WLz23jcXiy/+ZPWTT354c/Wjr/56e8vB192Xi6/8ePzLo98mxGsAlExkJWaXL/xwC+ir3Zervz/of7K49p/6m26/XrzQffnu9ssv/Wbky+GtCfEbACUTubsS7uHV07c3aK49vfaTq09+3An4pX++uvrd6nj0tfUtV1+vDyf/9HZv4sc3F8+tvnz6i8Xi+vGXw1sT4jgASsazcu/F7i8PAqAdl1efvbJ47uH6L6tvrX+yuuX1q81NV8Re9af8V2uC+8PVgy+HtybEcQCUjGfn3srLDaBf2J6Cb+Hrkd3dcn2b60cwHn15eGtCHAdAyWj2dLu7BbQ/7tx8vc7K1usHDt5d3+bR+hmif941HX55dGtCHAdAyWhWNr64+evuWfheviBp/9fVLfeyvunb3d+++K3f97c6+PL41oT4DYCS0YiAHhxDjpC4vv6pe9q9v3Jp/0sAJZcUACWj6c7O+8wfgY48lPm7Hz2/FvK14ZfjtybEZwCUjGbsMdDr+1+vs30MdPyhzPWFS3s/6b+cvDUhDgOgZDwjz8LvLj/aHlg+WqzP8+/unmbvdBzQO5R4cOuT/LcQYhQAJeMZuQ50A+HIdaAbCB91tO6uW+qFHHw5vDUhjgOgZCIPjl6JFB4U3b0S6cXt1z8Op+wrYK99e/XnJ/2l94Mvh7cmxHEAlEzk6d3Nc+f/ZQjo9GvhexE/vrn9+sWRL4e3JsRvAJRMZvhuTOHp8/79lF74ffh6/ST77v2Wnv7yy+srP7c/H3w5vDUhbgOgZC68YIiQiQAoGc/uYiVesk7IVACUjGd3sdLu6XhCyCAASsazcvPat1fHn+/e5AWXhEwEQMlEHm2fPP8Sn71ByHgAlEzlT2/3T5bzDBIhEwFQQgjJDIASQkhmAJQQQjIDoIQQkhkAJYSQzAAoIYRkBkAJISQzAEoIIZkBUEIIyQyAEkJIZgCUEEIyA6CEEJIZACWEkMwAKCGEZOb/Bz2gywovgaO9AAAAAElFTkSuQmCC" 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" 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" 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" 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" 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" 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" 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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>$ascgemeinschaft</code></pre>
-</div>
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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$groesse</code></pre>
-</div>
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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$entfernung</code></pre>
-</div>
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" 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-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$gemeinschaft</code></pre>
-</div>
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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$kultur</code></pre>
-</div>
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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$umweltbildung</code></pre>
-</div>
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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$zugang</code></pre>
-</div>
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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$gestaltung</code></pre>
-</div>
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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$beitrag</code></pre>
-</div>
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" class="img-fluid" width="672"></p>
-</div>
-<div class="cell-output cell-output-stdout">
-<pre><code>
-$ascklein</code></pre>
-</div>
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YIGBfTsBrQC9Hg/xbMkpTncpDX9aDEAKo4FxbHyiis2TwLQof0Uz5KU5nCT1vSjxTQPqKKgEQG9cAVvB+jRfopnSUpzuElr+tFiGgdUdQkaE9Dz288I0IH9FM+SlOZwk9b0o8UAqDgW9IbKLK7WfGkBCqDGzeEmrelHi2kdUE1BQwJ6YfPZAXq4n+JZktIcbtKafrSY5gE9+aFlBceC2ki5xfEBPf6HLp4lKc3hJq3pR4sJAKiaoPEAvXgFD6DGzeEmrelHiwFQcSxoDZRdXA/QS1vPENCD/RTPkpTmcJPW9KPFtA+onqDhAL28ALUD9Gg3xbMkpTncpDX9aDEAKo4FpXHyi6sBenHjAahtc7hJa/rRYgBUHAtK4+QX9wHo/n6KZ0lKc7hJa/rRYgBUHAtK4+QX12lOuII3BPRwP8WzJKU53KQ1/WgxIQBVEjQeoJc3HoDaNoebtKYfLSYAoGpLUACtm/39FM+SlOZwk9b0o8UAqDgWdIYpKAZQ0wCo4rCdJwagOoICaN0AaMBJa/rRYiIAqrUEDQZoip8AatwcbtKafrSYIICqCAqgdbO/m+JZktIcbtKafrSYEIAqLUFjAZryJCZbQK8BNN6kNf1oMQAqjgWNQYqK6wCasuUA1LY53KQ1/WgxUQDVEDQEoNsPlZskoGI3xbMkpTncpDX9aDExANVZgoYCNO0K3hbQawANN2lNP1pMGEAVBA0GaNKGA1Db5nCT1vSjxQQBVGUJGgnQxAWoOaC73RTPkpTmcJPW9KPFAKg4FsYPUVhcA9C07WYL6DWA+lVXGrbzxAF0vKAAWj0A6lddadjOEwVQjSVoIEBTr+DtAd3upniWpDSHm7SmHy0GQMWxMHqE0uIKgCZuNmNArwHUrbrSsJ0nEKCjBY0DaPIC1AHQzW6KZ0lKc7hJa/rRYsIAqrAEjQRo6lazBvQaQL2qKw3beQBUHAtjByguBlDTAKjisJ0nEqBjBQVQg2x3UzxLUprDTVrTjxYTB9DxS9BAgCZvNHNArwHUqbrSsJ0HQMWxMPL25cUAahoAVRy28wQCdLSgcQBN32YegM7UJ50ZAFUctvMAqDgWxt18RHFPgF4DqE91pWE7D4CKY2HczUcUA6hpAFRx2M4DoOJYGHfzEcWdATrTnnRmAFRx2M4TC9BxggKoTQDUpbrSsJ0nEqBjl6AAahMAdamuNGznAVBxLIy69Zhi1eYcPwHUuDncpDX9aDHBAB0lKIAaZbmb4lmS0hxu0pp+tJhQgI5cgsYANMtPADVuDjdpTT9aTDRAxwgKoEYBUI/qSsN2nliAjluChgA06yEkALVuDjdpTT9aDICKY2HEbUdFF9CsDeYE6CyiJSnN4Sat6UeLCQfoCEEB1CoA6lBdadjOEwzQUUvQCIBmXsEDqHFzuElr+tFiAFQcC+U3HRdVQPO2lxegs4CWpDSHm7SmHy0mHqDlggYANHcB6gToNYDaV1catvNEA3TMEjQEoJmbC0Btm8NNWtOPFgOg4lgovuXI9AjoLJ4lKc3hJq3pR4sJCGixoO0Dmn0F7wXoNYCaV1catvOEA3TEEjQCoLlbC0Btm8NNWtOPFhMR0FJBAdQui2t4pUnnB0AVh+088QAtX4I2D2j+FbwboNcAal1dadjOA6DiWCi83ejoAZq9sQDUtjncpDX9aDEhAS0UFEANM/bzV8YEQBWH7TwBAS1egrYOaMEVvB+goz8CcEQAVHHYzgOg4lgou9n4qAGav60A1LYZQIMlIqCl52bjgK4WoA18qNwqjtfwAKo4bOcBUHEsFN1KIVqArl1qA9D3AdS2utKwnQdAxbFQdCuFAKhtAFRx2M4DoOJYKLqVQlROq1l7gHoJCqCKw3YeABXHQtGtFKIE6HrurQDqtwQFUMVhO09QQItOTQC1DIAaV1catvOEBLRwCQqglnnf7xoeQBWH7TwAKo6FkhtppFNA3Z4KCqCKw3aeqICWnJoAahkANa6uNGzniQlo2anZNKAbNQH0cgBUcdjOExbQgnMTQC2z2NNOggKo4rCdJyigRadmy4DOADQ9AKo4bOcBUHEs5N9EJyqAbicOoJcCoIrDdp64gOafmwBqGQA1rq40bOeJCmjJudkwoDMAzQiAKg7beQBUHAvZt1CKBqC7eTcFqIugAKo4bOcpAvR3P7q6uveffn/09bxNf/K0UknBuQmgllnuaQC1q640bOcpAfS9q2W+/uvDb+Rt+tOnlUp6AnQGoDkBUMVhO08BoB/fv/e3Nzef/Ojq+cPv5G3606eVSvoCVEwbQC8FQBWH7TwFgL519d3FHx/f/9rhEjRv058+rVSSfw0PoJZZA+ohKIAqDtt5yh9E+uz1aQOav7ppFtCSj+PcbG+1zZ3dvKwGULPqSsN2nnJAP77/jcOHkfI2/ZnTSiXZq5uGAS3eSABq2wygwVIM6D/dv/q7zX9/dZ28Ed6vnZnqc7Smm/m/FNW3Za3MetlJJGYKD9+3rq7u/f32bwDqmob97GcnkZgpO3w//y//+v7Vvf98+OW8xf+ZCzud5F7Dt3oJP+IK3v0S3uUankt4xWE7T/m//78T1/Dr5G36c6eVTvoAdMRDSJMA1F5QAFUctvOMuID64OrwUaS8TX/utNJJL4CO2ETugHosQQFUcdjOMwLQ44fh8zb92dNKJQB6MQBq2wygwZIP6OdvrC/dpw9o5snZJqCjruAnAai5oACqOGznKXol0vN7f+6St+nPnlY66QPQMVvIH1CHJSiAKg7beYpeC3/1nd/ffP6rq3s/OfhO3qY/f1qpBEAvBUBtmwE0WEruA/1g9W5M9w4fhAfQwow5rcZdwU8DUGtBAVRx2M5T9CDSJ3895/Pbbx99PW/Tnz+tVJJ3bjYK6KgtNAFA7ZegAKo4bOcJ+470BecmgFoGQI2rKw3beQBUHAt5v79eugfUWFAAVRy284QHNOPcBFDLiD0NoBbVlYbtPMEBzTo3WwR0pJ8AatwMoMECoOJYyPv99QKgihszIQCqOGzniQ9o+skJoJbZA9RWUABVHLbzRAc0Z3XTIKAjnwU6EUCtl6AAqjhs5wFQcSxkHjtqGQPo8Xxb+lC5dQDUoLrSsJ2nA0CTT04AtQyAGldXGrbzhAc04+RsD9ABK5sE1FhQAFUctvP0AGjqydkioEPTBdALAVDFYTtPfEDTT87mAB2iEkAvB0AVh+08ACqOheyjRynlgA7NFkAvBUAVh+08XQCaeHYCqGUA1Li60rCdpwNAk8/O1gAdlLJVQC0FBVDFYTsPgIpjIf/w0UkxoIOTbRFQ2yUogCoO23n6ADTt7ARQywCocXWlYTtPD4Cmnp2NAToMZbOAGgoKoIrDdh4AFcdCwfGjklJAh+faJKCmS1AAVRy283QCaNLZCaCWAVDj6krDdp4uAE08O9sC9IST7QJqJyiAKg7beQBUHAslB5BGCgE9MdU2AbVcggKo4rCdB0DFsVByAGmk5LQ6xSSAXg6AKg7befoANO3sbAzQUzMF0EsBUMVhOw+AimOh6AhSCIBem94JCqCKw3YeABXHQtERpJCC0+qkks0CargEBVDFYTsPgIpjoegIUkgRoCdnCqCXAqCKw3aebgBNODtDAJqZSQFqJSiAKg7beToBNGl5A6CWOd7TAFq1utKwnQdAxbFQdgiND4AuA6BVqysN23n6AfTy2QmglgFQ4+pKw3aeXgBNOTsbAlTLz4kBaiQogCoO23kAVBwLhcfQ6ADoKgBas7rSsJ2nI0Avnp0AahkANa6uNGzn6QbQhLOzHUCznup5NhMD1EZQAFUctvMAqDgWSg+isSkAVGurTApQqyUogCoO23l6AvTS2QmglgFQ4+pKw3aefgC9fHY2A6jeFfzkADURFEAVh+08XQF64exsCFC1jTItQI2WoACqOGzn6QjQi2dnK4AqLkAB1DYAGi0AKo6F8sNoXLIB1dsmAGoaAI2WvgA9f3oCqGWG97SJoACqOGzn6QnQS2dnI4BqXsEDqG0ANFoAVBwLI46jUckFVHGTAKhpADRaOgP07OkJoJYBUOPqSsN2nq4AvXB6tgGo6hU8gNoGQKMFQMWxMOZAGpNMQDW3CICaBkCjpTdAz52fIQBt90PlljF5LRKAKg7befoC9PwCpwlAL/HYOKAmS1AAVRy28wCoOBZGHUkjkgfopRkC6KUAqOKwnaczQM+eny0AelHH9gGtLyiAKg7beQBUHAvjDqXyZAF6cYJtA2qxBAVQxWE7D4CKY2HcoVQeABUB0ErVlYbtPAAqjoVxh1J50rfcZRwDAFpdUABVHLbzAKg4FsYdSuXJAfTyBBsH1GAJCqCKw3ae/gA9fXpOH9AEGwH0cgBUcdjO0xug507PFgBNmB+AXgqAKg7beQBUHAsjj6XiAKhM/TtBAVRx2M7TIaAnT8/JA5pCY/uA1l+CAqjisJ2nO0DPnJ4NAJoyPQC9FABVHLbz9AjoqfMTQC1zDtDKggKo4rCdpz9ATy9wANQyZ/Y0gNaorjRs5wFQcSyMPpoKA6D7AdAa1ZWG7TxdAnri/ARQywCocXWlYTtPh4CePD+nDmgSjDEArSsogCoO23kAVBwL4w+nsgDoQQC0QnWlYTtPn4AOn58TBzQNRgBN2N5VRz/bDKDB0iOgp87PyQNaYVNMFdCqggKo4rCdB0DFsaBwPBUFQA8DoPrVlYbtPJ0COnh+ThtQ3c+D3wRATQOg0dIloCfOz6kDWmNLAKhpADRaegV06AQFUMtcALSmoACqOGzn6RPQ4RXOpAGtcwU/VUDrLkEBVHHYzgOg4lhQOaIKkgZolQ0BoKYB0GjpFtCBExRALQOgxtWVhu08nQI6eIJOGdBKV/CTBbSqoACqOGznAVBxLOgcUvlJArTOdgBQ0wBotPQK6NAJOmFAay1AAdQ2ABotACqOBaVjKjspgFbaDABqGgCNFgAVx4LSMZUdAD0OgGpXVxq28wCoOBaUjqnsXNxy1a7gpwxoPUEBVHHYzgOg4lhQOqaykwBorc0wWUBrLkEBVHHYztMxoEcn6GQBrbcABVDbAGi0dAvowAk6YUCrbQUANQ2ARguAimNB66DKDYAOpOKdoACqOGzn6RnQwxN0qoBWvIKfMKAVl6AAqjhs5+kX0OMTdLqA1tsIAGoaAI2WrgE9OENDABriQ+WWqXcND6CKw3aejgE9WuIAqGUu72kAVa2uNGznUQU0L+97Z+Y4+ZzMZlmTmmX9/ITTyv4hHafvFej+EmeiK9C8u0BZgSZs70rjJjSzAg2WngE9PEMB1DIpgFYSFEAVh+08nQO6d4YCqGUS9jSAalZXGrbzdA3odQuAZj4LFEATtnedYVOaATRYAFQcC5oHVk4uAJo5pUiA1hEUQBWH7Ty9AyrPUAC1TMqeBlDF6krDdp6+Ab2ePqC5r+ME0ITtXWXUpGYADZbuARWn6EQBzZ4RgF7a3lVGTWoG0GDpHNDrqQOa/UYiwQCtIiiAKg7beQBUHAu6h1Z6zgKaPaFAgFZaggKo4rCdB0B3pyiAWgZAjasrDdt5egf0etqA5r8VaDRAawgKoIrDdh4AnTig+fOJBGidJSiAKg7beboHVJyi0wO04L3oATRhe1cYM7EZQIMFQKcNaMF0ggFaQVAAVRy28wAogPokcU8DqFZ1pWE7D4BOGNCST5MD0ITtrT9kajOABguA7i4SJwho9dkDqGkANFoA9HqygNb8OONNJg9oDUEBVHHYzgOgUwa0/uQB1DQAGi0AuruGB1DLAKhxdaVhOw+A7k7RiQFqcQUPoLYB0GgB0N0SdHKAGswdQE0DoNECoNfbcxRALQOgxtWVhu08AHoNoC5JB1RdUABVHLbzAOj19hwFUMsk72kA1amuNGznAdBFpgmoxcwB1DQAGi0AushqCTotQE0WoABqGwCNFgBdZoqAmky8CUC1BQVQxWE7D4AuMz1AbRagLQCqvwQFUMVhOw+ALrNc5UwJUCM/AdQ2ABotALoKgFonB1BlQQFUcdjOA6CrLE7SCQFq8jLORVoAVH0JCqCKw3YeAF1naoAaTRtATQOg0QKg6wCocQDUuLrSsJ0HQNeZXzRPB1CzK/hWANUVFEAVh+08ALrJtAC1mnUTgGovQQFUcdjOA6CbVPkA3bQcbrkxC9BoHyq3DIAqVFcatvMA6DZTAnTMLEICqrpvAFRx2M4DoNsAqGmy9jSAjq+uNGznAdBtANQ0AGpcXWnYzgOg2/jdCXqw5UY9hASgCdtbc7C8ZgANFgDdZTKAjptETEA19w2AKg7beQB0l4kAOu45TDEB1d03AKo4bOcB0F3cruH3ttzI54ACaML2VhwrsxlAgwVARTeAGiYXUMV9A6CKw5bl4eypX17+qQezpx8VFlgFQEU3gBomc08D6NjqSsOWBUCHkrfpNU4r1bzvdS8ogF4OgI6trjRsWQB0KHmbXuO0Us28GkDNkg2o3r4BUMVhy5IGaAMBUNHt9UD8PqDjJhEVUM1dA6CKw5YFQIeSt+lVTivNLKpdBJVbbuwbMQFowvZWGym7GUBXAdCh5G16lUPpHPkAACAASURBVNNKM9MAdOQkADRhe6uNlN3cPaDv/ulsduvZR1tAP/r+3fkXvvKL9beXf519+Yerv23vA9271acvzJ578rNnZrMvvOR/DymAiu5rf0BHvxNoWEAVdw2AKg6blSffWx6fs6e+vwb04WydZ5fff2fz1y8uv7sG9OBWc0D/5Tflj3kGQEX3tdOT6Xdbbvw7KQNowvbWGii/uXNAH8ylfHTz5M2Fhgv75n4uFp9/eHMl6Id3Z0/P//rkp7PZ7dWPLwE9uNUc0Nmtf/Xo5qO5q89p+lUSABXdi/8DUKMAqHF1pWFzMgdyJd7DFYXzv9++WX/h1l+Je0YfrP5jBejhrRaAvrz4+3xl6v40JwAV3Yv/cwVU4bOQADRhe2sNlN/cN6APNkDODVz81+6hpLmFtzeMyp9fAPnw4FbzP9ZuPvB/KApARffy/x0EFYCOngSAJmxvrYHym7sGdKXkMkv6xN9XVj5ePJ70j7sbLL94eKsFoNt1K4COPa0Us65uG9CyOTsEQI2rKw2bkZ18K/oW1+K7LCx8c/lfX3jpt6ufWgK6eNBd3kp8AUDHn1aK2QBqLiiApkTv8T0AVRw2I4cUHgO6fL7S8uH15ROZAPRcdE4rxWyqzQXdbDl7P1sCVG+/AKjisBkZWIHePv6p3/zgmQWhi4eJNoDu3QpA11E6rfTiD6jfnB2aAdS2utKwGRm4D3T4UfTF85gW3zl5H+jUAf30T579bcFgeZte6bTSizegDgtQALVN34DuwJujuPivB9tH3ZeWHlF58lH4yQP6QtHrpPI2vdJppRd3QB3n7NBcAKjSjgFQxWFzsn3e5+55oOsl6OPlNfv2aUzrtenJ54FOHdAnb67vyc0zNG/TK51WetkBaizoast5LECbAlTtXzYAVRw2K6tXHu29EmnxcNHmkn1O461vzf9cvMZoex/o8qcOXok0dUDn+c3q4bDNy/qTkrfptU4rtWyrnQB1nbN9M4DaVlcaNi9vrh9k/38OXwu/Wol+eHfz9yWRm9fCP9i7VRuA3myfUrB9p5SLydv0WqeVWnwBdVmAtgaozo4BUMVhM/Obo3djekau05787EuLJ4KuH4I5/W5Mq5+eOKDz6by7fNeTWy+lPaSUt+m1Tiu1CEBtBV1sOR8/2wJU6182AFUc1jIT/IiPi09j+sPyDfpmX05ZhuZterXTSiu7agA1aAZQ2+pKw9bPA/HY/cDzRn1zYQX6s2e2LxR49vJgeZte7bTSCoCaNgOobXWlYevn4frNl3YPx08oZwDd6Ll4LH7xhn2Xf/e8Ta92WmlFVNsKCqCJUbpvBUAVh62fuZu3vjX36J27/nd5HuUUoOt7P3fPBn2ccPdD3qZXO620IqsBtHpzSTWAlldXGtYgjzePzX8x+fFss5x4HujqPfTlg0efvnBZ/7xNr3daKWUPUEtB51vOyU8AtQ2AFuUPb64eqp/aI0g3516JdGv/6UufvtDVCtR0CboAdApztm0uA1RjvwCo4rCd5xSg8m1Nl3nyD5efypS36fVOK6XsA2oo6Hw3AGhaALS4utKwnYd3YxLde38zFHS+G5ymDKC2AdBoOXEf6A/+fHe9/uGf/YvE+x7yNr3iaaWTg2pDQN0WoA0CqrBbAFRx2M5z6hJePGKU8vDRKnmbXvG00skhoGaC+i1AmwNUZa8AqOKwnScB0A/v9gmo3RJUcwEa90PlVgHQ0upKw3aeI0D3P6dEvlPK5eRtes3TSiVHgFoJOlOcdHxAx+8VAFUctvMcr0AfHwP6cuJgeZte87RSyVG1kaAzAM0IgBZWVxq28xwD+uT/ffHFP7t7649e3OQvDp/RdDJ5m171tNKIG6CakwbQywFQxWE7T8J9oOnJ2/Sqp5VGnACdAWhOALSwutKwnSfhaUzpydv0qqeVRrwAVZ10B4CO3isAqjhs5+GJ9KL78AsmgM4ANC8AWlZdadiM/HNWNGWql0NAn/zgxfnic/7/MqnL0bxNr3taKeS42kLQme6kAfRyAFRx2Iz88/Hj06fTKKCrj14+eC5Tp88DvbZ4KugMQDMDoGXVlYbNwQZAzydv0+ueVgoZArS2oEvsADQro3cKgCoOm4PN7H+lpllARyVv0yufVuMzVA2gtQKgxtWVhs3BBkDPJ2/TK59W4zMIaGVBATQ/AFpUXWnYHGwA9HzyNr3yaTU+g9WVBQXQ/ABoUXWlYXOw6Q/QJ//95/+QPljeplc+rcZnuLoqoCvrADQrAFpUXWnYHGx6AvSjf/fo5ubTxSfLffGvUgfL2/TKp9X4nAC0oqBr6gA0KwBaVF1p2BxsOgL04fKh9wfDD8L/7q+vru59++2jG+VteuXTanxOVNcEdF0MoDmZjRUUQBWHzcGmH0AfL9n88O7s6UcfvXD4ifC/ulrm3k8Ob5W36ZVPq/E5BWg1QWcAWhQALamuNGwONv0A+mD5HqCPZ7f+6vgT4T+4uve3NzefvHH1tV8f3Cpv02ufVqNzqroeoJtiAM0KgJZUVxo2B5tuAH3yvYWca0YP3prp8zeu/m7x52evr/4Uydv02qfV6JwEtJKgMwAty9hreABVHDYHm24AXZn56Quz2zdHgH72+nrl+dbVdw9ulrfptU+r0TlZXUnQrXMAmhkALaiuNGwONp0B+uHd5XvRn3pz0H4ArSPorAqgmQFQ0wBoH4CuLuEfLu8CPboPdJ3PXt8+ivTVdfKq328nM9XXG6yyeB9lUpZZjR1CaqcfQG8ezG4vHn5fyHn8KPwq7109v/lPAC0Ifo4IgLaYUYA+SP5otm3+293Zwq7lHw8PFTv6wl6e/OzZtI7TT2Na5OWbJ9+frdahh/mgm6cxXVe5hJd3VHIJn5txO4RLeMVhM2IM6BKx59Z/ZAL6ePnwT0JOP5F+ntvLB5JuDf3iH9y/d/gYfGBA9QXde6AHQHMDoPnVlYbNiDGgD+eX0bs/8jIa0JuPfvDnP5z/8emffOUXA999b2D9GRlQbUH3HygH0NyMeyITgCoOmxFzQJ8Tf+RlPKBn86tBPyMDqvxsegAdOWkAza6uNGxGxgL67jOzWy+tVpPv/un839AvvPRo9xj3wyV5H33/7mz25cWab3Uv5O31HxtG3/3m/GbfWv388gu7Gywa3nlmNpuvGJ98b30BnjCnnA2wzudvXX398EVIy+Rt+gqn1bicB1RVUAAFUOvqSsNmZCSgL862rL25/qyM29tnWc7Ne3n5zMtFFvc6DgL6cHWz1X8u/l/cYN7wpfV7fygB+j82+e3+19+6+sbvB2+Qt+krnFbjcr5aVVAAHQ3oiN0BoIrDZmQkoLOnf3HzzvK56Y9ntxb3L76zfHz7we7Z6p++MHv20c2Tn67e/2jvEn75/3Mu//jR/GaLby+/sHeDecPtR9uGkZfwH33/5GcivXfKz9iAal7EH7xWCEDzA6C51ZWGzchIQNcqzq/YH6xoXK06V9Yt///hmj1h5t5f1t9fkiu/sPrL6pXrq8HHArr/qXIHL+W82uT5g1vlbfoap9WoXAJUTdDD11oCaH4ANLe60rAZGQnocyuZ1hr94b//+28unmi5/soCxfVbeCxWmgsKjwBdgbvO6gvyBg9WaC5RHQvow9nsi3/x803+QT4N4IOrTgHVE/TwteoAmh8Aza2uNGxGNB6FX5n30Tdnm2eqr76zVHR11+V2zXcE6KcviKe0r0UVN3iw+fnxgM4HTrz9XvI2fY3TalQuVisJevRmHwCanzF3ggKo4rAZUQN08dDPrS//xT+uVpQL7B6vn7R+AVBxLb36QiVA96hOT96mr3FajUoCoBqCHr9ZEoAWBEAzqysNmxEtQBfru0c3m/tA51o9/ejB6mJ+j63LK9D9G6gCOvwGTBeSt+mrnFZjcrlaQ9CBN5sD0IIAaGZ1pWEzonEf6Id3n/rlxr35AnKJ6oNb//F7C7D27uNMuw9U3kD1Ep4V6GDGAzr0bp0AWpAR/5gBqOKwGRkJ6NPrF2ZuF46PV8/onP/5R3fXj/+snlS/enL9yUfhH64esX/u4AaKgJa9/AlAEzL0bscAWhIAzauuNGxGxj4PdPEszeU7HC0v4Z/8dP2U+OVdmZv/ePoXN+uncp58Hui7d7dPY9q7wT6gg+/hOTCn4S/Pl6Dfytk0q+Rt+jqn1YgkATpS0MF3iwfQkgBoXnWlYTOi8kqkxdvMrd8s7umfrtaJc1DXV8yPVy8sWv7QAKCbVyLd3n5B3kACuniY6ulHH969eCV+4hL+B382m936oxfX+fPEdzPJ2/R1TqsRSakeKejwp20AaEnKdwWAKg6bkbEPIs0Xiut13fol7bvXwW/Wi6uXtv/wZvXVI0BPvRb+h6uGHaCLNxEdAej+8+iPPxj+RPI2fZ3TakSSqkcCOlwMoCUB0KzqSsNmpNo70pfd46gSABXdST81RtATH/fGh8oVBUCzqisNm4NNJUALH/NWiepHI+Rt+kqnVXkSq8sFPSUbgBYFQLOqKw2bg00lQN8petmPTgBUdCf+XOmJe1I2AC1L6Y4AUMVhc7CpAuiDEx86ZJOzgD757bnvHidv09c6rYqTDmjRiXsaNgAtC4DmVFcaNgebKoD+dFbyjCGtnAZ08Z7PT/3y0z95Kf0DRfI2/eXTKgMCjSSf0UWCnnENQMsCoDnVl39keezkDpuDTT8fa3zz5M3Vo0efvjD7YvKrOvM2/eHJkJTRJ92ZpJ/RBYKe+90BtCwAmlN9/KUT51jesDnYdATog9nsi//n3ad++eTfzlKfk18IaKKcBo5mnNHZp+7Z3xlAywKgOdXiv0+dVq+88kruNs3Cph9AH89m31q/pcj6dVEpydv0Bzvv1WVee+3Vc6nqaM4ZnXmcnf9dAbQsAJpTvfi/YzCPkzlsDjb9APpg9ULR9Zvopz5JIG/T78v5ahKgg44qnM3LZJ3Rs5yz98IvCaBlydoHIj0CmiAngBbk3LsxrQFdvIFU2mB5m36QxjRAjxxVOKHzAU0+ey/9ggBaGABNS5qcBoBmpGlAV3SuAU1/c9C8TT8eUGVGM8/oZEEv/mYAWhgAvZTdyfGXCXYCaHYCAKpmaO4ZPUsj9PJvBaCFSf437OCkL7mRSmwB3Z0WcxcnAOg/ZyVnZL+c/Eyk7cfdpb81nh+g+4wWn9IF5+/FEzjlFwLQ0gDoqQg6VwHQKjn5hsq3N4BmfMBc3qZXB3SsoQVn9EVBk34ZAC0NgA7mSM+JANrNJfzi/USffbQE9KNvpr/SNG/T1wB0h2jJKV10Bp89hdN+EQAtTdk1fHBAj/GcCKA5fjYO6PKtm79099YfPTObpb/XXt6mrwXo1tBcRIvO6NmZczj1dwDQ4gDofgaWnlMC9P9OTfOALt6ReZ309yrN2/QVAS27li87o2cnCM3oB9DiAKjI7DSfAFopp99M5A8/+9J8X3zhK79IHyxv01cFdIdoxildfBIfI5rVDaDFAdBtzuAJoNXi+H6g1QF9NfNavvyMHrwTJ/3mAFqcM3egnDnp82+ilFqAnlt6AmjNBAc0i9BxZ3QxnwA6JgCaxCeAVsrJt7P7zc9//vP/kf5WoMvkbXobQHeIXj6lVc7ogvChcuXpHtAUPAG0WgYBXX7U5zK3Xsp5U/q8TW8HaCKhAGrarAVovqCBAE3lE0ArZQDQ1Xspb/PHtd6R3hLQV1Ou5QHUtFmpul9Ak67cAbRujgF98r3FXvnCi/P82XIhmvx+ytMG9PIyFEBNmwF0XLL4BNBKOQb0wXyvfGVz4f7R9+d/S/7M0LxNbw7oBtGTp7TOGZ0fAB2Rgmv4CIBm4Qmg1XIE6Id39586/zDjQ0PzNr0LoK+eWYUCqGmzVnWPgOYtPgG0Xo4AfXC44nyQ/lqkvE3vA+iZO0MB1LQZQMuSd9cngNbNIaCLe0D3PwLpcfq9oHmb3gvQk3eGAqhpsx6g2R/Fm/nzelEAtIxPAK2UQ0A/feHwin1+TR8N0A2ih6e00hmdHQAdlZ4ALcMzIKD/bXVX4/KPh4fXyEdf2MuTnz2b81ufzwCgB+8/f/yVk8nb9L6ADtwZCqCmzQCancLFZ0BAHy+2xHPrPzIBfZz+sPjl9AvoMaEAatqsCGjuZ5nn/bhiRgE6hs9ogD6c3X60+yMvAFqJUAA1bdar7gDQwkeOAgP6nPgjLwCqTejaUAA1bQbQ9IzmMwKgy1eYf3nx9prLK/fZ7fUfG0bf/eZs9oVvLf5r/YXdDW4ezF5+55nZ7Cu/WL9QSI3Q3gGVhAKoaTOApmY0niEA/XD1Bh23Xj4B6MPVmbz6z+f2bzAH9EvLvzz1SwDVz5pQADVtVn0Pv7yTPu9AVUwJoOMXnyEAnTv07KObJz9dabR3Cb/8/zmXf/zo5p3lt5df2LvB4unt8+/enb1c/xL+ONEBXRMKoKbNAJoSJT7bB/Thmj1h5t5f1t9/sCBSfmH1lwer52I+WPw3gNbI4FPrjQKgIxMWUDU+mwf0yffWz0//8O6CwiNA51fmuxcArb4gb/BgheYS1aqAPvnBi8f584BPpD+OH6EAOjJRAdXjMwCge+u5I0A/fUG8AGgtqrjBg83P1wZ0VPI2/aBijoCee5+RugHQkYkJqCafzQMqLo1PACquk1dfAFDjvOZFKICOTERAFa/eYwC69xLzyyvQ/RsAqEEW1S6E8qFyIzPLE7QBQLX5bB7Qvfs40+4DlTcAUIOsqh0EBdCxCQaoPp/NAzq3b/WeRo9ngw8ibR50X/6x/oK4AYAaZFNtTiiAjk0sQCvw2T6gn74we/oXN+uncp58Hui7d7dPY9q7wT6g6Z9SdHlOaiPdhAHU/DoeQMcm7xp+2oBW4bN9QG8erz8qePledMeAbl6JdHv7BXkDCejiFUpPP/rwbvInbZydk8IY2+Rt+mHEpgCoNaEAOjphAK1x9R4D0PVL23+4/O8BQE+9Fn55Awno4k1EAbRC9qotCQXQ0QkCaDU+IwA6yQCo6N7/q52gADo6WdfwUwW0Ip8AWikAKroPv2BFKICOTwBAa/IJoJUCoKL76CtG1/EAOj7NA1qXTwCtFAAV3QNfMyEUQMcn5xp+goBWvXpfBkCrBEBF9+BXDQgFUIW0DGh9PgG0UgBUdJ/4enVBAVQh7QJqwSeAVgqAiu6T36ksKIAqJOMafnqA1pRzHQCtEgAV3ae/VVdQANVIq4Ca8AmglQKgovvM96pexgOoRtoE1Gb5+QqAVgqAiu5z36z5WBKAaiT9Gn46gNrc/bnMJADNCIBeyjBiUwW0JqEAqpLmADXkE0ArBUBF96UfqCUogKqkMUBN+ZwEoP+cFU2Z6gVARfflH6lDKICqJPkafhKA2vI5CUAjBkBFd8LPVLmOB1CdNASoNZ8AWikAKrqTfqqCoHyonE7aAdTeTwCtEwAV3Yk/py4ogOqkGUDt+QTQSgFQ0Z36g9qLUADVSeqdoM6AOiw/XwHQSgFQ0Z38k8qCAqhSWgDU+MH3bQC0SgBUdGf8rCqhAKqUBgB14hNAKwVARXfOD2s+HA+gSpk8oG58AmilAKjozvtxPUIBVCtpgnoB6nX1vgyAVgmAiu7cG2gJCqBamTSgi8MlmTH1AGiVAKjozr+JjqAAqpUpA7pYff4lgAYLgIrugtuoCAqgWpkuoKurdwCNFgAV3SU30riMB1CtTBbQ9b2fABotACq6i26l8FgSgGol7an05oBuHzwC0GgBUNFdeLvRggKoWqYIqHjwHUCjBUBFd/EtRwoKoGqZIKDyuUsAGi0AKrrLbzpuEQqgakm6hjcFdO+pnwAaLQAqukfcdpSgAKqXiQF68NR5AI0WABXdo249glAA1cu0AD186RGARguAiu5xNy9/OB5A9ZJyDW8F6PErNwE0WgBUdI8doFRQAFXMdAAdeOU7gEYLgIru8UOUCQqgipkMoENvHAKg0QKgolthjKJFKB8qp5iEa3gLQIffeAlAowVARbfGICWCAqhmpgDoqTeuA9BoUQU0L68FzeLced8tM9/6CWTmeEyvs3zjulbjvfHaCitQ0a00TvYilBWoZi5fw9degZ5+22RWoNECoKJbbaRMQQFUNd6AnnnbeQCNFgAV3XpDZSoGoJrxBfTsx3YAaLQAqOhWHCvLMQBVzcVr+IqAXvjYIwCNFgAV3aqjAeiF5nrVfoBe+tQ4AI0WABXdyuMlQwagunED9OKHbgJotACo6NYeMJUyANXNpWv4WoBe/tBiAI0WABXd2gOmYgagynEBNOVD3wE0WgBUdOsPmcYZgCrHAdALjx5tFAPQYAFQ0V1j0BTPAFQ5F67hKwCaxCeAxguAiu46w14GDUC1Yw1oGp8AGi8AKrorjXtRNADVji2gicvPVwA0XgBUdNca+JJpAKqes4IqA5ruJ4CGC4CK7mojX1ANQNVjB2gGnwAaLwAquiuOfZY1AFWPGaBZfgJouACo6K46+hnXAFQ9VoBm8Qmg8QKgorvu8KdlA1D12ACat/x8BUDjBUBFd+XxT9LGh8qp5+wzQZUATXvu/L5iABosACq6qzecEBRA9WMDaLZiABosACq661cMCwqg+qkPaD6fABovACq6DToGL+MBVD/nruE1AC1Yfr4CoPECoKLbomToQR4ArZCqgBbc/blSDECDBUBFt03NMaEAWiEVAS3lE0DjBUBFt1URgF5bAHpS0JGAFvMJoPECoKLbrmpfUACtkVqAlvMJoPECoKLbsGtvEQqgNVIJ0DF+Ami4AKjoNuzauyMUQGvk9DX8CEBHXL4vFQPQYAFQ0W3aJgQF0CqpAOhIPwE0XABUdBv3bQUF0CrRB3QknwAaLwAquq0LN4tQAK2Sk9fwhYCOXX6+AqDxAqCi27xxLSiA1okqoOVP/pSKAWiwAKjoduhcEgqgdaIJqAafABovACq6PUoXJyaA1smpa/gCQFX4BNB4AVDR7VO7ELS6JacSGtBTS9B8QJX8BNBwAVDR7dSb9x7yuokO6KCguYDqXL4vFQPQYAFQ0e1VnPkxHJqJDeiJJWjegary6NFGMQANFgAV3W7FrwFonSgAqsgngMYLgIput+LXDt9eZAwZfKjcLsPX8DkHqiafABovACq63YoXzUqLUADdy1hAdf0E0HABUNHtVrwGVINQAN3LOEBVL9+XigFosACo6HYrXjWrEAqgexkDqOajRxvFADRYAFR0uxVvmhUEBdD9DAmadqDq8wmg8QKgotuteNc8WlAA3U8xoBX4BNB4AVDR7VYsmscKCqD7KQS0xvLzFQCNFwAV3W7FsnnkZTyA7qcM0Ep+Ami4AKjodiveax73WBKA7mfomaCXDtRafAJovACo6HYrPmgeIyiAHiQf0Hp+Ami4AKjodis+ai4XFEAPkg1oPT4BNF4AVHS7FR83Fy9CAfQgA9fw5w7UisvPVwA0XgBUdLsVDzSXCgqgh8kBtMJz5/cVA9BgAVDR7VY82FxGKIAeJgPQynwCaLwAqOh2Kx5uLhIUQA9zfA1/6kCtzSeAxguAim634lPNBYIC6FFSAa3vJ4CGC4CKbrfik835i1AAPUoaoNUv3xcB0GgBUNHtVny6OftZ9QB6lKNr+IEDtfajR+sAaLQAqOh2Kz7XnCkogB7nIqBGfAJovACo6HYrPt+cRSKAHudwCXp4oFrxCaDxAqCi2634QrPGGy2fSBeAHi5BDw5UMz4BNF4AVHS7FV9sriYogFr6CaDhAqCi2634cnOtRWgvgO4JKg9Uu8v3RQA0WgBUdLsVJzSPe5O7k+kD0OtTgJo9erQOgEYLgIput+Kk5iqCdgOoFHRzoFrzCaDxAqCi2604sbkCoZ0Aej0EqDmfABovACq63YpTm/UF7RhQez4BNF4AVHS7Fac3awvaD6BC0BsvPwE0XABUdLsVZzQrL0J7AfT6AFCHy/dFADRaAFR0uxXnNOs+HN8noPaPHq0DoNECoKLbrTivWZPQHgF14xNA4wVARbdbcW6znqDdALoT1I9PAI0XABXdbsX5zVqEdgfoYsM5KgagwQKgotutuKBZSdDOAF1dvQPouQBoTgBUdLsVFzWrCNoXoOurdwA9FwDNCYCKbrfismaNRWhPgG7v/ATQcwHQnACo6HYrLmxWeDi+H0Cvdw8eAei5AGhOAFR0uxUXN48WtB9AxYPvAHouAJoTABXdbsUjmkcS2gugi810506uJeoB0GgBUNHtVjymedx1fB+ALrfRqwCaFADNCYCKbrficc17hPKhckdZ8fnqHNA7mZaoB0CjBUBFt1vx2GahJoAeZMPnq6++CqApAdCcAKjodise37xlE0D3s+MTQNMCoDkBUNHtVqzRvIYTQGUkn7treAA9FwDNCYCKbrdileYVnQC6y2zfz+0SFEDPBUBzAqCi261YqXm2SboxgQE94nO7BAXQcwHQnACo6HYrVmsG0E0G+Hx1swQF0HMB0JwAqOh2K1ZsBtBlBvkE0JQAaE4AVHS7FWsDmi5oTEBP8Lm5hgfQcwHQnACo6HYrVgc0mdCIgA5fva8CoBcDoDkBUNHtVlwB0ERC4wF6js/1EhRAzwVAcwKgotutWBvQ9LtCowF6ns9XV0tQAD0XAM0JgIput2J9QFPfpikYoJf4BNDLAdCcAKjodiuuAWgaoaEAvczn6hoeQM8FQHMCoKLbrbgOoCnX8YEAvXj1vgqAXgiA5qQU0M9ef/74iwBaWFwJ0ARCwwCayOdyCQqg5wKgOSkF9K2r54+/CKCFxdUAvUhoEECT+Xx1ISiAnguA5qQM0M/fugJQxeKKgF64KzQGoBl8AuilAGhOigD93Y+uAFSzuCqgZwmNAGgWnwB6KQCakxJA37u6+s4/AahicWVAz1zHtw9oztX7MttP9nAIgEZLEaBf//ubDwBUsbh+86lXJ7UN6HpWmdsCQM8GQHNS+iDSHqBfXSdviNeIZVbWvB8pqyllb4n5NTw5nUIROg2A9pRQhBbhucgdGJCnIgAAGH5JREFUBD2XQhE6jQqgm3AJX1hs1nx0Hd/sJXzJtfsmftfwXMJHC4CKbrdiw+YDQhsFdAyfng8jAWi0AKjodis2bd57OKlFQIseOZJ5DUDPBEBzAqCi263YuFkQ2h6go/kE0PMB0JwAqOh2K7Zv3hDaGqDj9Xx1CaiToAAaLQAqut2KPZpPPTPUKvmAlj3rcyCvuT2MBKDRAqCi263Yp9lX0GxAtfgE0PMB0JwAqOh2K/abtB+iWYDq4bnIa27PZALQaAFQ0e1W7Dfp19yu5DMAVbt230x6/j8APRUAzQnvSC+63YodAV38n4uhqYAq47mZtM8SFECjBUBFt1uxM6AuhKYBWoHP9aRdBAXQaAFQ0e1W7A7oq/aPyl8GVPvKfX/SAHoiAJoTABXdbsVTAHRLqJGhFwCdVeMTQM8HQHMCoKLbrXgagC5iR+hZQOvhuZ20y5PpATRaAFR0uxVPB9BXza7lTwJacem5N2kAHQ6A5gRARbdb8aQANbqWHwa05pX7waQ9lqAAGi0AKrrdiicG6CLVET0G1ARPMWkAHQyA5gRARbdbce0PlTtTfWmoaoruAbqrGr0B0icNoIMB0JwAqOh2K54moNvhqiC6BdQWz0UA9FwANCcAKrrdiqcL6G5MbUUXgNrbucwWUHNBATRaAFR0uxVPHNDtuHqMyuFKpjkqm0kD6FAANCcAKrrdihsAdDf4OEf3h8icnFIA9FwANCcAKrrdilsBVFRkSnp0myk89cD+Gh5AowVARbdbcVuAyqb8bJv9AbVfggJotACo6HYrbhLQ/cpUN3fNAGpbnfqDAJoTABXdbsVtA1qWKQBqLiiARguAim63YgC1rd79J4AeBUBzAqCi260YQG2rd/9pvAQF0GgBUNHtVgygttXivwH0MACaEwAV3W7FAGpbLf7bdgkKoNECoKLbrRhAbavlXwD0IACaEwAV3W7FAGpbLf9iugQF0GgBUNHtVgygttV7f7MUFECjBUBFt1sxgNpW7/0NQPcDoDkBUNHtVgygttV7fwPQ/QBoTgBUdLsVA6ht9d7fLN9SBECjBUBFt1sxgNpW7/8VQPcCoDkBUNHtVjwZSyybJzNpwyUogEYLgIput+LJWGLZPJ1JA6gMgOYEQEW3W/F0LDFsns6kAVQGQHMCoKLbrXg6lhg2T2fSACoDoDkBUNHtVjwdSwybpzNpAJUB0JwAqOh2K56OJYbNE5q0maAAGi0AKrrdiidkiV3zlCYNoLsAaE4AVHS7FU/JErPmKU3aagkKoNECoKLbrXhKlpg1T2rSALoNgOYEQEW3W/GkLLFqntSkjZagABotACq63YonZYlV87QmDaCbAGhOAFR0uxVPyxKj5mlN2mYJCqDRAqCi2614WpYYNU9s0gC6DoDmBEBFt1vxxCyxaZ7YpE2WoAAaLQAqut2KJ2aJTfPUJm0hKIBGC4CKbrfiqVli0jy5SQPoMgCaEwAV3W7Fk7PEonlykzZYggJotACo6HYrnpwlFs3TmzSALgKgOQFQ0e1WPD1LDJqnN+n6S1AAjRYAFd1uxdOzxKB5gpOuLiiARguAim63Yj5Uzrb61DcA9BUAzQuAim63YgC1rT71DQB9BUDzAqCi260YQG2rT30DQF8B0LwAqOh2KwZQ2+pT3wDQVwA0LwAqut2KAdS2+uR3agsKoNECoKLbrRhAbatPfwtAATQrACq63YoB1Lb69LcqL0EBNFoAVHS7FQOobfWZ7wEogOYEQEW3WzGA2laf+V7dJSiARguAim63YgC1rT73zaqCAmi0AKjodisGUNvqs98FUABND4CKbrdiALWtPvvdmktQAI0WABXdbsUAalt9/tsACqDJAVDR7VYMoLbV579dcQkKoNECoKLbrRhAbasvfB9AATQ1ACq63YoB1Lb6wvfrLUEBNFoAVHS7FQOobfWlH6gmKIBGC4CKbrdiALWtvvgTtQQF0GgBUNHtVgygttUXfwJAATQtACq63YoB1Lb64k8AKICmBUBFt1vxlC2p1jzlSQMogKYFQEW3W/GULanWPOVJAyiApgVARbdb8ZQtqdY86UlXEhRAowVARbdb8aQtqdU87UnXERRAowVARbdb8bQtqdQ88UkDKIAmBEBFt1vxxC2p0zzxSVdZggJotACo6HYrnrgldZqnPukaggJotACo6HYrnrolVZonP2kABdCLAVDR7VY8eUtqNE9+0hWWoAAaLQAqut2KJ29JjebpT1pfUACNFgAV3W7F07ekQnMDkwZQAL0QABXdbsUNWKLf3MCk1ZegABotACq63YobsES/uYVJawsKoNECoKLbrbgFS9Sbm5i0sqAAGi0AKrrdipuwRLu5iUkDKICeDYCKbrfiJizRbm5i0gAKoGcDoKLbrbgJS7Sbm5g0gALo2QCo6HYrbsIS7eYmJg2gAHo2ACq63YqbsES7uY1J6woKoNECoKLbrbgNS5SbG5m0qqAAGi0AKrrdivlQOdvqrJ/WFBRAowVARbdbMYDaVmf99B1FQQE0WlQBzctrpEKWgHr/ErFy585f9hQ/EVoMK1DR7VbMCtS2OvPn9ZagrECjBUBFt1sxgNpW595ATVAAjRYAFd1uxQBqW519CwAF0OEAqOh2KwZQ2+rsW2gtQQE0WgBUdLsVA6htdf5NlAQF0GgBUNHtVgygttUFt9ERFECjBUBFt1sxgNpWF9wGQAF0KAAqut2KAdS2uuA2AAqgQwFQ0e1WDKC21QW30Xk9EoBGC4CKbrdiALWtLrmRiqAAGi0AKrrdigHUtrroVgAKoMcBUNHtVgygttVlN1MQFECjBUBFt1sxgNpWF95uvKAAGi0AKrrdigHUtrr0hgAKoAcBUNHtVgygttWlNxy9BAXQaAFQ0e1WDKC21cW3HCsogEYLgIput2IAta0uv+lIQQE0WgBUdLsVN2nJ2OYmJz3y2aAAGi0AKrrdipu0ZGxzm5MeJyiARguAim634jYtGdnc6KQBFEBFAFR0uxU3asm45kYnDaAAKgKgotutuFFLxjU3OulR1/AAGi0AKrrdihu1ZFxzq5MeIyiARguAim634lYtGdXc7KRHCAqg0QKgotutuFlLxjS3O+lyQQE0WgBUdLsVt2vJiOaGJw2gALoOgIput+KGLSlvbnnSpYICaLQAqOh2K27ZkuLmpiddKCiARguAim634qYtKW1ue9JlggJotACo6HYrbtuSwua2J132QBKARguAim634rYtKWxufNIACqA3ALrX7VbcuCVlza1PukRQAI0WABXdbsWtW1LU3PykCwQF0GgBUNHtVty8JSXNzU+64G5QAI0WABXdbsXNW1LS3P6k8wUF0GgBUNHtVty+JQXNASadLSiARguAim634gCW5DdHmHSuoAAaLQAqut2K+VA522q9oTIFBdBoAVDR7VYMoLbVimPlCQqg0QKgotutGEBtqzUHyxIUQKMFQEW3WzGA2larjgagPQdARbdbMYDaVusOlyEogEYLgIput2IAta1WHi9dUACNFgAV3W7FAGpbrTxe+t2gABotACq63YoB1LZae8A7qYQCaLQAqOh2KwZQ22r1EVMFBdBoAVDR7VYMoLbVFcZMExRAowVARbdbMYDaVtcYNElQAI0WABXdbsUAaltdZdQUQQE0WgBUdLsVA6htdZ1hAbTDAKjodisGUNvqSuNeFhRAowVARbdbMYDaVtca+OJVPIBGC4CKbrdiALWtrjXwxWczAWi0AKjodisGUNvqekNfEBRAowVARbdbMYDaVlcc+/wiFECjBUBFt1sxgNpW1xz8rKAAGi0AKrrdigHUtrru8GcEBdBoAVDR7VYc1pJzzXEnfVpQAI0WABXdbsVxLTnTHHjSJy/jATRaAFR0uxUHtuR0c+RJnxIUQKMFQEW3W3FkS042x570sKAAGi0AKrrdimNbcqI5+KQHF6EAGi0AKrrdioNbMtwcfdJ3BggF0GgBUNHtVhzdksHm+JMG0PgBUNHtVhzfkoHmHiZ9KCiARguAim634h4sOWruYtIHi1AAjRYAFd1uxV1YctjcxaQP7ggF0GgBUNHtVtyFJYfNnUx6j1AAjRYAFd1uxZ1Yst/czaQFoQAaLQAqut2Ku7FENnc06a2gABotACq63Yo7smTX3NWk14QCaLQAqOh2K+7Kkk1zX5NeXccDaLQAqOh2K+7LknVzb5NeEgqgwQKgotutuDdLls39TXroxZ1mAdAqAVDR7VbcnyWdTtpRUACtEgAV3W7FPVrS6aTdCAXQKgFQ0e1W3KclftV+za+5XccDaJUAqOh2K+ZD5Wyr/ZoX1XfueCAKoFUCoKLbrRhAbav9mlfVHoICaJUAqOh2KwZQ22q/5l21NaEAWiUAKrrdigHUttqvWVQbX8kDaJUAqOh2KwZQ22q/5r1qU0IBtEoAVHS7FQOobbVf81G1maEAWiUAKrrdigHUttqveaD6js1CFECrBEBFt1sxgNpW+zUPV1sQCqBVAqCi260YQG2r/ZpPV9c2FECrBEBFt1sxgNpW+zWfq75zp+bVPIBWCYCKbrdiALWt9mu+UF3RUACtEgAV3W7FAGpb7decVF0FUQCtEgAV3W7FAGpb7decWq2/FAXQKgFQ0e1WDKC21X7NOdV37mgyCqBVUgTo5z++f3X17bePvg6ghcUAalvt15xdfeeOkqMAWiUlgH72+tUiX/v14TcAtLAYQG2r/ZoLq++MdxRAq6QE0LeuvvH2zSdvXH3j9wffANDCYgC1rfZrHld95zAA6p0CQD++v1x7fvb6vZ8cfAdAC4sB1Lbar1ml+sjRFEsBtEoKAH3v6vn1n989+A6AFhYDqG21X7Nu9aCkJ0AF0CopAPStq79b/vnBGtJdALSwGEBtq/2aq1WfszRvsQqgOckH9PM31pfuH9/f3An61XXyBnqNEFIpOaDuJ1uEruMIKCHEIbPz8f712sooQA+fyJS3+D9xRaAwSGm3W7HfpB2bmbRtdaVhO4/KCnQTlf2hMEhpt1txuNMqpZlJ21ZXGrbzAKjodisOd1qlNDNp2+pKw3Yex0fhT+wPhUFKu92Kw51WKc1M2ra60rCdp+h5oN/d+3MXlf2hMEhpt1txuNMqpZlJ21ZXGrbzOL4S6cT+UBiktNutONxpldLMpG2rKw3beQoA/fyNq69rvBb+xP5QGKS026043GmV0sykbasrDdt5Sp719YnOuzGd2B8Kg5R2uxWHO61Smpm0bXWlYTtP0dNmP/nx3M9vH64/AbS4ONxpldLMpG2rKw3beRzfkf7E/lAYpLTbrTjcaZXSzKRtqysN23kAVHS7FYc7rVKambRtdaVhOw+Aim634nCnVUozk7atrjRs5wFQ0e1WHO60Smlm0rbVlYbtPAAqut2Kw51WKc1M2ra60rCdB0BFt1txuNMqpZlJ21ZXGrbzAKjodisOd1qlNDNp2+pKw3YeABXdbsXhTquUZiZtW11p2M4DoKLbrTjcaZXSzKRtqysN23kAVHS7FSs2Lz+VIaNarzkz8SxJaQ43aU0/WgyAim63YgC1rfZrDjdpTT9aDICKbrdiALWt9msON2lNP1oMgIput2IAta32aw43aU0/WgyAim63YgC1rfZrDjdpTT9aDICKbrdiALWt9msON2lNP1oMgIput2IAta32aw43aU0/WgyAim63YgC1rfZrDjdpTT9aDICKbrdiALWt9msON2lNP1oMgIput2IAta32aw43aU0/WgyAim63YgC1rfZrDjdpTT9aDICKbrdiALWt9msON2lNP1oMgIput2IAta32aw43aU0/WgyAim63YgC1rfZrDjdpTT9aDICKbrdiALWt9msON2lNP1oMgIput2IAta32aw43aU0/WgyAim63YgC1rfZrDjdpTT9aDICKbrdiALWt9msON2lNP1oMgIput2IAta32aw43aU0/WgyAim63YgC1rfZrDjdpTT9ajCqgCvnqV71/A4cw6V7S5aRDB0AnECbdS7qcdOgA6ATCpHtJl5MOHQCdQJh0L+ly0qEDoBMIk+4lXU46dAB0AmHSvaTLSYcOgE4gTLqXdDnp0AHQCYRJ95IuJx06UwOUEEKaCYASQkhhAJQQQgoDoIQQUhgAJYSQwgAoIYQUBkAJIaQwAEoIIYWZAqCf//j+1dW335Zf+t2Prq7u/affO/1CFhmY9CIf3/9G4FkfT/qz16+W+dqvvX6n6hnY05//av6l/+NvnX4hopgJALo+h+Qp9N7qrPp63LNqYNKLfP7GVWBAByb98f3ogA5M+pP1vxrf8futiFImAOhbV994++YTKcfH9+/N/3X+5EdXzzv+WnVzPOll5v9yBAZ0YNIfBN7HqxxPev6v5Nffvvn8/7u69xPPX4xoxB/Qj+8v/3H+7PXd4fTW1XfFdyJmYNKrL0cGdGjS610dNwOT/mC9HH0v/D8eHcQf0M1h9N7RqfTZ62EBHZ70fGnyN4HvAx2Y9OdvRF+FHU96vpf/zu3XIcrxB/St9eF0fDEX+AGV4Um/dfV84DkPTfqz17/xX390dfVv3nb6lerneNKB1wUdxh3Q7RrkiI5/uh/2X+rhSX8wv3wPDOjQpDePIfW0pxf/9U//19XV1//e8fciSpksoG9dXd0Le4QNTnp5N1lngH5wdfWd39/8/z8O+3jKMKA/Xv2rEfzu3y4yJUDllc3n/+Vf37+695+dfqnaGZz08vGUPgDdTnpzD2HYx5IGJv3B1fJfjc9/FfZfjZ4yJUAP6fhd2Gv4oUm/t3z8vQ9Aj+b4QdQnHwxM+oPN0vMtHoVvP1MGtKvT6uP7yy91CmjYZ6yd3tOh93U/cQf0zKPwgY+w40mvX3wV+VU57Olltv9YhP1Xo6f4A7p5gtzAE+XinlbHk+4A0DN7Ou4LkoYmvV6Bhr3A6in+gA6+Eun5vT/j5cQrkSL/m3FuTwd+bvnZwzvoI2c9xR/Q1SuDD14LH/1hyoFJrxIZ0JN7+pMfxV2LDU/622+HPrw7ij+gm/emWf5Dvb6D/YPVtey9qMuSwUkvExnQoUmv77n42tvOv1q9DB3e96Mf3v1kAoDefLJ4XvG3l25sLPnkr+fH1/G7ZQbKwKRX/x0Y0JN7+juB5zw46cVbhAZ+/WpHmQKghBDSZACUEEIKA6CEEFIYACWEkMIAKCGEFAZACSGkMABKCCGFAVBCCCkMgBJCSGEAlBBCCgOghBBSGAAlhJDCACghhBQGQMmofHh39pz464PZ048GfurElwlpPABKRgVASc8BUDIqAEp6DoCSUUkDlJCYAVAyKgBKeg6AklEBUNJzAJQc5zffvzubzb700trCj5Z//fIPN99e/n391w2gj2ezL/5SALr8mVtf+cXqFqsvf/rC7LknP3tmNvvCSyhLQgRAyWGevDlbZ0Hizc07+3+9ebj+67OLv6wBfXx39d0NoJufWf3QDtB/+c29oQhpOwBKDjPH79k5dx/Nrbt9syTy6flC8slPV39dfHv+94Wyz91sAP1w7ecG0PnPLBaff3hzLegW0Nmtf/Xo5qPvzfau+wlpNQBKDjJn7vbmPxbsPZw9tVouPlj+x/qrN0++t/zrEtCtn2sp539fDTG/8a2/upGAvnyzui13lZIIAVBykMcr87ZEPtz8ffPtFYLrn1sAuvNzLeXW3MUYt28EoGs3H2x/gJCWA6DkZFbMPV48GPSP2y8+3LdvjudXXth9aSnlWs3dF3aA3h4chJBGA6BkKH/4zc9/8MxsxdzqMaUvvPTb5XcOnqj04d3ldzd3aS6/u7hU3+WpX8pH4Vc/BqAkRgCUHOWdZyR+Nzfv/un6ofPFM5cGAP3i3a2HAEq6CoCSg8wvwBdPAn3xP/xW3FP5mx8sUX15CNCnHz2cba7NN4De3h8TQEnMACg5yPpZTDeHD/Usnsc0Z3D7xdU9nctH4Rfmvry+yeo+0IMH2QGUxAyAkv3s9Pt0+eCQeERoaef2Ufg5nS+L54E+JZ4H+mD7wP16NAAlMQOgZD87QB/O9p/GtPrO9rlID3bPA13+7O3VF9fPA12PseYWQEnMACg5yPoS/jeLF10u6Jyzd+tbi1cmra/THw+8EmmBq3jG/GKMxSNO66t+ACVRA6DkIJ+uX64++8pPV2Kun6m0fbLS0Gvhl6w+2j3EtH0t/Ga1CqAkYgCUHGb5hkmL585vHk1/8rMvLZ4I+uxv1z8w9G5McyMX/yHejekZ8Q5OAEpiBkAJIaQwAEoIIYUBUEIIKQyAEkJIYQCUEEIKA6CEEFIYACWEkMIAKCGEFAZACSGkMABKCCGFAVBCCCkMgBJCSGEAlBBCCgOghBBSGAAlhJDCACghhBQGQAkhpDAASgghhQFQQggpDIASQkhh/jc0AcD8cs2FGQAAAABJRU5ErkJggg==" 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-</div>
-</section>
-</section>
-
-</main>
-<!-- /main column -->
-<script id="quarto-html-after-body" type="application/javascript">
-window.document.addEventListener("DOMContentLoaded", function (event) {
- const toggleBodyColorMode = (bsSheetEl) => {
- const mode = bsSheetEl.getAttribute("data-mode");
- const bodyEl = window.document.querySelector("body");
- if (mode === "dark") {
- bodyEl.classList.add("quarto-dark");
- bodyEl.classList.remove("quarto-light");
- } else {
- bodyEl.classList.add("quarto-light");
- bodyEl.classList.remove("quarto-dark");
- }
- }
- const toggleBodyColorPrimary = () => {
- const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");
- if (bsSheetEl) {
- toggleBodyColorMode(bsSheetEl);
- }
- }
- toggleBodyColorPrimary();
- const icon = "";
- const anchorJS = new window.AnchorJS();
- anchorJS.options = {
- placement: 'right',
- icon: icon
- };
- anchorJS.add('.anchored');
- const isCodeAnnotation = (el) => {
- for (const clz of el.classList) {
- if (clz.startsWith('code-annotation-')) {
- return true;
- }
- }
- return false;
- }
- const clipboard = new window.ClipboardJS('.code-copy-button', {
- text: function(trigger) {
- const codeEl = trigger.previousElementSibling.cloneNode(true);
- for (const childEl of codeEl.children) {
- if (isCodeAnnotation(childEl)) {
- childEl.remove();
- }
- }
- return codeEl.innerText;
- }
- });
- clipboard.on('success', function(e) {
- // button target
- const button = e.trigger;
- // don't keep focus
- button.blur();
- // flash "checked"
- button.classList.add('code-copy-button-checked');
- var currentTitle = button.getAttribute("title");
- button.setAttribute("title", "Copied!");
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+ }
+ }
+ return false;
+ };
+
+ const categorySelector = "header.quarto-title-block .quarto-category";
+ const activateCategories = (href) => {
+ // Find any categories
+ // Surround them with a link pointing back to:
+ // #category=Authoring
+ try {
+ const categoryEls = window.document.querySelectorAll(categorySelector);
+ for (const categoryEl of categoryEls) {
+ const categoryText = categoryEl.textContent;
+ if (categoryText) {
+ const link = `${href}#category=${encodeURIComponent(categoryText)}`;
+ const linkEl = window.document.createElement("a");
+ linkEl.setAttribute("href", link);
+ for (const child of categoryEl.childNodes) {
+ linkEl.append(child);
+ }
+ categoryEl.appendChild(linkEl);
+ }
+ }
+ } catch {
+ // Ignore errors
+ }
+ };
+ function hasTitleCategories() {
+ return window.document.querySelector(categorySelector) !== null;
+ }
+
+ function offsetRelativeUrl(url) {
+ const offset = getMeta("quarto:offset");
+ return offset ? offset + url : url;
+ }
+
+ function offsetAbsoluteUrl(url) {
+ const offset = getMeta("quarto:offset");
+ const baseUrl = new URL(offset, window.location);
+
+ const projRelativeUrl = url.replace(baseUrl, "");
+ if (projRelativeUrl.startsWith("/")) {
+ return projRelativeUrl;
+ } else {
+ return "/" + projRelativeUrl;
+ }
+ }
+
+ // read a meta tag value
+ function getMeta(metaName) {
+ const metas = window.document.getElementsByTagName("meta");
+ for (let i = 0; i < metas.length; i++) {
+ if (metas[i].getAttribute("name") === metaName) {
+ return metas[i].getAttribute("content");
+ }
+ }
+ return "";
+ }
+
+ async function findAndActivateCategories() {
+ const currentPagePath = offsetAbsoluteUrl(window.location.href);
+ const response = await fetch(offsetRelativeUrl("listings.json"));
+ if (response.status == 200) {
+ return response.json().then(function (listingPaths) {
+ const listingHrefs = [];
+ for (const listingPath of listingPaths) {
+ const pathWithoutLeadingSlash = listingPath.listing.substring(1);
+ for (const item of listingPath.items) {
+ if (
+ item === currentPagePath ||
+ item === currentPagePath + "index.html"
+ ) {
+ // Resolve this path against the offset to be sure
+ // we already are using the correct path to the listing
+ // (this adjusts the listing urls to be rooted against
+ // whatever root the page is actually running against)
+ const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
+ const baseUrl = window.location;
+ const resolvedPath = new URL(relative, baseUrl);
+ listingHrefs.push(resolvedPath.pathname);
+ break;
+ }
+ }
+ }
+
+ // Look up the tree for a nearby linting and use that if we find one
+ const nearestListing = findNearestParentListing(
+ offsetAbsoluteUrl(window.location.pathname),
+ listingHrefs
+ );
+ if (nearestListing) {
+ activateCategories(nearestListing);
+ } else {
+ // See if the referrer is a listing page for this item
+ const referredRelativePath = offsetAbsoluteUrl(document.referrer);
+ const referrerListing = listingHrefs.find((listingHref) => {
+ const isListingReferrer =
+ listingHref === referredRelativePath ||
+ listingHref === referredRelativePath + "index.html";
+ return isListingReferrer;
+ });
+
+ if (referrerListing) {
+ // Try to use the referrer if possible
+ activateCategories(referrerListing);
+ } else if (listingHrefs.length > 0) {
+ // Otherwise, just fall back to the first listing
+ activateCategories(listingHrefs[0]);
+ }
+ }
+ });
+ }
+ }
+ if (hasTitleCategories()) {
+ findAndActivateCategories();
+ }
+
+ const findNearestParentListing = (href, listingHrefs) => {
+ if (!href || !listingHrefs) {
+ return undefined;
+ }
+ // Look up the tree for a nearby linting and use that if we find one
+ const relativeParts = href.substring(1).split("/");
+ while (relativeParts.length > 0) {
+ const path = relativeParts.join("/");
+ for (const listingHref of listingHrefs) {
+ if (listingHref.startsWith(path)) {
+ return listingHref;
+ }
+ }
+ relativeParts.pop();
+ }
+
+ return undefined;
+ };
+
+ const manageSidebarVisiblity = (el, placeholderDescriptor) => {
+ let isVisible = true;
+ let elRect;
+
+ return (hiddenRegions) => {
+ if (el === null) {
+ return;
+ }
+
+ // Find the last element of the TOC
+ const lastChildEl = el.lastElementChild;
+
+ if (lastChildEl) {
+ // Converts the sidebar to a menu
+ const convertToMenu = () => {
+ for (const child of el.children) {
+ child.style.opacity = 0;
+ child.style.overflow = "hidden";
+ }
+
+ nexttick(() => {
+ const toggleContainer = window.document.createElement("div");
+ toggleContainer.style.width = "100%";
+ toggleContainer.classList.add("zindex-over-content");
+ toggleContainer.classList.add("quarto-sidebar-toggle");
+ toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+ toggleContainer.id = placeholderDescriptor.id;
+ toggleContainer.style.position = "fixed";
+
+ const toggleIcon = window.document.createElement("i");
+ toggleIcon.classList.add("quarto-sidebar-toggle-icon");
+ toggleIcon.classList.add("bi");
+ toggleIcon.classList.add("bi-caret-down-fill");
+
+ const toggleTitle = window.document.createElement("div");
+ const titleEl = window.document.body.querySelector(
+ placeholderDescriptor.titleSelector
+ );
+ if (titleEl) {
+ toggleTitle.append(
+ titleEl.textContent || titleEl.innerText,
+ toggleIcon
+ );
+ }
+ toggleTitle.classList.add("zindex-over-content");
+ toggleTitle.classList.add("quarto-sidebar-toggle-title");
+ toggleContainer.append(toggleTitle);
+
+ const toggleContents = window.document.createElement("div");
+ toggleContents.classList = el.classList;
+ toggleContents.classList.add("zindex-over-content");
+ toggleContents.classList.add("quarto-sidebar-toggle-contents");
+ for (const child of el.children) {
+ if (child.id === "toc-title") {
+ continue;
+ }
+
+ const clone = child.cloneNode(true);
+ clone.style.opacity = 1;
+ clone.style.display = null;
+ toggleContents.append(clone);
+ }
+ toggleContents.style.height = "0px";
+ const positionToggle = () => {
+ // position the element (top left of parent, same width as parent)
+ if (!elRect) {
+ elRect = el.getBoundingClientRect();
+ }
+ toggleContainer.style.left = `${elRect.left}px`;
+ toggleContainer.style.top = `${elRect.top}px`;
+ toggleContainer.style.width = `${elRect.width}px`;
+ };
+ positionToggle();
+
+ toggleContainer.append(toggleContents);
+ el.parentElement.prepend(toggleContainer);
+
+ // Process clicks
+ let tocShowing = false;
+ // Allow the caller to control whether this is dismissed
+ // when it is clicked (e.g. sidebar navigation supports
+ // opening and closing the nav tree, so don't dismiss on click)
+ const clickEl = placeholderDescriptor.dismissOnClick
+ ? toggleContainer
+ : toggleTitle;
+
+ const closeToggle = () => {
+ if (tocShowing) {
+ toggleContainer.classList.remove("expanded");
+ toggleContents.style.height = "0px";
+ tocShowing = false;
+ }
+ };
+
+ // Get rid of any expanded toggle if the user scrolls
+ window.document.addEventListener(
+ "scroll",
+ throttle(() => {
+ closeToggle();
+ }, 50)
+ );
+
+ // Handle positioning of the toggle
+ window.addEventListener(
+ "resize",
+ throttle(() => {
+ elRect = undefined;
+ positionToggle();
+ }, 50)
+ );
+
+ window.addEventListener("quarto-hrChanged", () => {
+ elRect = undefined;
+ });
+
+ // Process the click
+ clickEl.onclick = () => {
+ if (!tocShowing) {
+ toggleContainer.classList.add("expanded");
+ toggleContents.style.height = null;
+ tocShowing = true;
+ } else {
+ closeToggle();
+ }
+ };
+ });
+ };
+
+ // Converts a sidebar from a menu back to a sidebar
+ const convertToSidebar = () => {
+ for (const child of el.children) {
+ child.style.opacity = 1;
+ child.style.overflow = null;
+ }
+
+ const placeholderEl = window.document.getElementById(
+ placeholderDescriptor.id
+ );
+ if (placeholderEl) {
+ placeholderEl.remove();
+ }
+
+ el.classList.remove("rollup");
+ };
+
+ if (isReaderMode()) {
+ convertToMenu();
+ isVisible = false;
+ } else {
+ // Find the top and bottom o the element that is being managed
+ const elTop = el.offsetTop;
+ const elBottom =
+ elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
+
+ if (!isVisible) {
+ // If the element is current not visible reveal if there are
+ // no conflicts with overlay regions
+ if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+ convertToSidebar();
+ isVisible = true;
+ }
+ } else {
+ // If the element is visible, hide it if it conflicts with overlay regions
+ // and insert a placeholder toggle (or if we're in reader mode)
+ if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+ convertToMenu();
+ isVisible = false;
+ }
+ }
+ }
+ }
+ };
+ };
+
+ const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
+ for (const tabEl of tabEls) {
+ const id = tabEl.getAttribute("data-bs-target");
+ if (id) {
+ const columnEl = document.querySelector(
+ `${id} .column-margin, .tabset-margin-content`
+ );
+ if (columnEl)
+ tabEl.addEventListener("shown.bs.tab", function (event) {
+ const el = event.srcElement;
+ if (el) {
+ const visibleCls = `${el.id}-margin-content`;
+ // walk up until we find a parent tabset
+ let panelTabsetEl = el.parentElement;
+ while (panelTabsetEl) {
+ if (panelTabsetEl.classList.contains("panel-tabset")) {
+ break;
+ }
+ panelTabsetEl = panelTabsetEl.parentElement;
+ }
+
+ if (panelTabsetEl) {
+ const prevSib = panelTabsetEl.previousElementSibling;
+ if (
+ prevSib &&
+ prevSib.classList.contains("tabset-margin-container")
+ ) {
+ const childNodes = prevSib.querySelectorAll(
+ ".tabset-margin-content"
+ );
+ for (const childEl of childNodes) {
+ if (childEl.classList.contains(visibleCls)) {
+ childEl.classList.remove("collapse");
+ } else {
+ childEl.classList.add("collapse");
+ }
+ }
+ }
+ }
+ }
+
+ layoutMarginEls();
+ });
+ }
+ }
+
+ // Manage the visibility of the toc and the sidebar
+ const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+ id: "quarto-toc-toggle",
+ titleSelector: "#toc-title",
+ dismissOnClick: true,
+ });
+ const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+ id: "quarto-sidebarnav-toggle",
+ titleSelector: ".title",
+ dismissOnClick: false,
+ });
+ let tocLeftScrollVisibility;
+ if (leftTocEl) {
+ tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+ id: "quarto-lefttoc-toggle",
+ titleSelector: "#toc-title",
+ dismissOnClick: true,
+ });
+ }
+
+ // Find the first element that uses formatting in special columns
+ const conflictingEls = window.document.body.querySelectorAll(
+ '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+ );
+
+ // Filter all the possibly conflicting elements into ones
+ // the do conflict on the left or ride side
+ const arrConflictingEls = Array.from(conflictingEls);
+ const leftSideConflictEls = arrConflictingEls.filter((el) => {
+ if (el.tagName === "ASIDE") {
+ return false;
+ }
+ return Array.from(el.classList).find((className) => {
+ return (
+ className !== "column-body" &&
+ className.startsWith("column-") &&
+ !className.endsWith("right") &&
+ !className.endsWith("container") &&
+ className !== "column-margin"
+ );
+ });
+ });
+ const rightSideConflictEls = arrConflictingEls.filter((el) => {
+ if (el.tagName === "ASIDE") {
+ return true;
+ }
+
+ const hasMarginCaption = Array.from(el.classList).find((className) => {
+ return className == "margin-caption";
+ });
+ if (hasMarginCaption) {
+ return true;
+ }
+
+ return Array.from(el.classList).find((className) => {
+ return (
+ className !== "column-body" &&
+ !className.endsWith("container") &&
+ className.startsWith("column-") &&
+ !className.endsWith("left")
+ );
+ });
+ });
+
+ const kOverlapPaddingSize = 10;
+ function toRegions(els) {
+ return els.map((el) => {
+ const boundRect = el.getBoundingClientRect();
+ const top =
+ boundRect.top +
+ document.documentElement.scrollTop -
+ kOverlapPaddingSize;
+ return {
+ top,
+ bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+ };
+ });
+ }
+
+ let hasObserved = false;
+ const visibleItemObserver = (els) => {
+ let visibleElements = [...els];
+ const intersectionObserver = new IntersectionObserver(
+ (entries, _observer) => {
+ entries.forEach((entry) => {
+ if (entry.isIntersecting) {
+ if (visibleElements.indexOf(entry.target) === -1) {
+ visibleElements.push(entry.target);
+ }
+ } else {
+ visibleElements = visibleElements.filter((visibleEntry) => {
+ return visibleEntry !== entry;
+ });
+ }
+ });
+
+ if (!hasObserved) {
+ hideOverlappedSidebars();
+ }
+ hasObserved = true;
+ },
+ {}
+ );
+ els.forEach((el) => {
+ intersectionObserver.observe(el);
+ });
+
+ return {
+ getVisibleEntries: () => {
+ return visibleElements;
+ },
+ };
+ };
+
+ const rightElementObserver = visibleItemObserver(rightSideConflictEls);
+ const leftElementObserver = visibleItemObserver(leftSideConflictEls);
+
+ const hideOverlappedSidebars = () => {
+ marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
+ sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
+ if (tocLeftScrollVisibility) {
+ tocLeftScrollVisibility(
+ toRegions(leftElementObserver.getVisibleEntries())
+ );
+ }
+ };
+
+ window.quartoToggleReader = () => {
+ // Applies a slow class (or removes it)
+ // to update the transition speed
+ const slowTransition = (slow) => {
+ const manageTransition = (id, slow) => {
+ const el = document.getElementById(id);
+ if (el) {
+ if (slow) {
+ el.classList.add("slow");
+ } else {
+ el.classList.remove("slow");
+ }
+ }
+ };
+
+ manageTransition("TOC", slow);
+ manageTransition("quarto-sidebar", slow);
+ };
+ const readerMode = !isReaderMode();
+ setReaderModeValue(readerMode);
+
+ // If we're entering reader mode, slow the transition
+ if (readerMode) {
+ slowTransition(readerMode);
+ }
+ highlightReaderToggle(readerMode);
+ hideOverlappedSidebars();
+
+ // If we're exiting reader mode, restore the non-slow transition
+ if (!readerMode) {
+ slowTransition(!readerMode);
+ }
+ };
+
+ const highlightReaderToggle = (readerMode) => {
+ const els = document.querySelectorAll(".quarto-reader-toggle");
+ if (els) {
+ els.forEach((el) => {
+ if (readerMode) {
+ el.classList.add("reader");
+ } else {
+ el.classList.remove("reader");
+ }
+ });
+ }
+ };
+
+ const setReaderModeValue = (val) => {
+ if (window.location.protocol !== "file:") {
+ window.localStorage.setItem("quarto-reader-mode", val);
+ } else {
+ localReaderMode = val;
+ }
+ };
+
+ const isReaderMode = () => {
+ if (window.location.protocol !== "file:") {
+ return window.localStorage.getItem("quarto-reader-mode") === "true";
+ } else {
+ return localReaderMode;
+ }
+ };
+ let localReaderMode = null;
+
+ const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
+ const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
+
+ // Walk the TOC and collapse/expand nodes
+ // Nodes are expanded if:
+ // - they are top level
+ // - they have children that are 'active' links
+ // - they are directly below an link that is 'active'
+ const walk = (el, depth) => {
+ // Tick depth when we enter a UL
+ if (el.tagName === "UL") {
+ depth = depth + 1;
+ }
+
+ // It this is active link
+ let isActiveNode = false;
+ if (el.tagName === "A" && el.classList.contains("active")) {
+ isActiveNode = true;
+ }
+
+ // See if there is an active child to this element
+ let hasActiveChild = false;
+ for (child of el.children) {
+ hasActiveChild = walk(child, depth) || hasActiveChild;
+ }
+
+ // Process the collapse state if this is an UL
+ if (el.tagName === "UL") {
+ if (tocOpenDepth === -1 && depth > 1) {
+ el.classList.add("collapse");
+ } else if (
+ depth <= tocOpenDepth ||
+ hasActiveChild ||
+ prevSiblingIsActiveLink(el)
+ ) {
+ el.classList.remove("collapse");
+ } else {
+ el.classList.add("collapse");
+ }
+
+ // untick depth when we leave a UL
+ depth = depth - 1;
+ }
+ return hasActiveChild || isActiveNode;
+ };
+
+ // walk the TOC and expand / collapse any items that should be shown
+
+ if (tocEl) {
+ walk(tocEl, 0);
+ updateActiveLink();
+ }
+
+ // Throttle the scroll event and walk peridiocally
+ window.document.addEventListener(
+ "scroll",
+ throttle(() => {
+ if (tocEl) {
+ updateActiveLink();
+ walk(tocEl, 0);
+ }
+ if (!isReaderMode()) {
+ hideOverlappedSidebars();
+ }
+ }, 5)
+ );
+ window.addEventListener(
+ "resize",
+ throttle(() => {
+ if (!isReaderMode()) {
+ hideOverlappedSidebars();
+ }
+ }, 10)
+ );
+ hideOverlappedSidebars();
+ highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+ function getTabSettings() {
+ const data = localStorage.getItem("quarto-persistent-tabsets-data");
+ if (!data) {
+ localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+ return {};
+ }
+ if (data) {
+ return JSON.parse(data);
+ }
+ }
+
+ function setTabSettings(data) {
+ localStorage.setItem(
+ "quarto-persistent-tabsets-data",
+ JSON.stringify(data)
+ );
+ }
+
+ function setTabState(groupName, groupValue) {
+ const data = getTabSettings();
+ data[groupName] = groupValue;
+ setTabSettings(data);
+ }
+
+ function toggleTab(tab, active) {
+ const tabPanelId = tab.getAttribute("aria-controls");
+ const tabPanel = document.getElementById(tabPanelId);
+ if (active) {
+ tab.classList.add("active");
+ tabPanel.classList.add("active");
+ } else {
+ tab.classList.remove("active");
+ tabPanel.classList.remove("active");
+ }
+ }
+
+ function toggleAll(selectedGroup, selectorsToSync) {
+ for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+ const active = selectedGroup === thisGroup;
+ for (const tab of tabs) {
+ toggleTab(tab, active);
+ }
+ }
+ }
+
+ function findSelectorsToSyncByLanguage() {
+ const result = {};
+ const tabs = Array.from(
+ document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+ );
+ for (const item of tabs) {
+ const div = item.parentElement.parentElement.parentElement;
+ const group = div.getAttribute("data-group");
+ if (!result[group]) {
+ result[group] = {};
+ }
+ const selectorsToSync = result[group];
+ const value = item.innerHTML;
+ if (!selectorsToSync[value]) {
+ selectorsToSync[value] = [];
+ }
+ selectorsToSync[value].push(item);
+ }
+ return result;
+ }
+
+ function setupSelectorSync() {
+ const selectorsToSync = findSelectorsToSyncByLanguage();
+ Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+ Object.entries(tabSetsByValue).forEach(([value, items]) => {
+ items.forEach((item) => {
+ item.addEventListener("click", (_event) => {
+ setTabState(group, value);
+ toggleAll(value, selectorsToSync[group]);
+ });
+ });
+ });
+ });
+ return selectorsToSync;
+ }
+
+ const selectorsToSync = setupSelectorSync();
+ for (const [group, selectedName] of Object.entries(getTabSettings())) {
+ const selectors = selectorsToSync[group];
+ // it's possible that stale state gives us empty selections, so we explicitly check here.
+ if (selectors) {
+ toggleAll(selectedName, selectors);
+ }
+ }
+});
+
+function throttle(func, wait) {
+ let waiting = false;
+ return function () {
+ if (!waiting) {
+ func.apply(this, arguments);
+ waiting = true;
+ setTimeout(function () {
+ waiting = false;
+ }, wait);
+ }
+ };
+}
+
+function nexttick(func) {
+ return setTimeout(func, 0);
+}
+</script>
+<script>/**
+ * @popperjs/core v2.11.4 - MIT License
+ */
+
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(e,t){void 0===t&&(t=!1);var n=e.getBoundingClientRect(),o=1,i=1;if(r(e)&&t){var a=e.offsetHeight,f=e.offsetWidth;f>0&&(o=s(n.width)/f||1),a>0&&(i=s(n.height)/a||1)}return{width:n.width/o,height:n.height/i,top:n.top/i,right:n.right/o,bottom:n.bottom/i,left:n.left/o,x:n.left/o,y:n.top/i}}function c(e){var 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K}next(){this._slide(Q)}nextWhenVisible(){!document.hidden&&l(this._element)&&this.next()}prev(){this._slide(G)}pause(t){t||(this._isPaused=!0),V.findOne(".carousel-item-next, .carousel-item-prev",this._element)&&(s(this._element),this.cycle(!0)),clearInterval(this._interval),this._interval=null}cycle(t){t||(this._isPaused=!1),this._interval&&(clearInterval(this._interval),this._interval=null),this._config&&this._config.interval&&!this._isPaused&&(this._updateInterval(),this._interval=setInterval((document.visibilityState?this.nextWhenVisible:this.next).bind(this),this._config.interval))}to(t){this._activeElement=V.findOne(nt,this._element);const e=this._getItemIndex(this._activeElement);if(t>this._items.length-1||t<0)return;if(this._isSliding)return void j.one(this._element,et,(()=>this.to(t)));if(e===t)return this.pause(),void this.cycle();const i=t>e?Q:G;this._slide(i,this._items[t])}_getConfig(t){return t={...X,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(K,t,Y),t}_handleSwipe(){const t=Math.abs(this.touchDeltaX);if(t<=40)return;const e=t/this.touchDeltaX;this.touchDeltaX=0,e&&this._slide(e>0?J:Z)}_addEventListeners(){this._config.keyboard&&j.on(this._element,"keydown.bs.carousel",(t=>this._keydown(t))),"hover"===this._config.pause&&(j.on(this._element,"mouseenter.bs.carousel",(t=>this.pause(t))),j.on(this._element,"mouseleave.bs.carousel",(t=>this.cycle(t)))),this._config.touch&&this._touchSupported&&this._addTouchEventListeners()}_addTouchEventListeners(){const t=t=>this._pointerEvent&&("pen"===t.pointerType||"touch"===t.pointerType),e=e=>{t(e)?this.touchStartX=e.clientX:this._pointerEvent||(this.touchStartX=e.touches[0].clientX)},i=t=>{this.touchDeltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this.touchStartX},n=e=>{t(e)&&(this.touchDeltaX=e.clientX-this.touchStartX),this._handleSwipe(),"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((t=>this.cycle(t)),500+this._config.interval))};V.find(".carousel-item img",this._element).forEach((t=>{j.on(t,"dragstart.bs.carousel",(t=>t.preventDefault()))})),this._pointerEvent?(j.on(this._element,"pointerdown.bs.carousel",(t=>e(t))),j.on(this._element,"pointerup.bs.carousel",(t=>n(t))),this._element.classList.add("pointer-event")):(j.on(this._element,"touchstart.bs.carousel",(t=>e(t))),j.on(this._element,"touchmove.bs.carousel",(t=>i(t))),j.on(this._element,"touchend.bs.carousel",(t=>n(t))))}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=tt[t.key];e&&(t.preventDefault(),this._slide(e))}_getItemIndex(t){return this._items=t&&t.parentNode?V.find(".carousel-item",t.parentNode):[],this._items.indexOf(t)}_getItemByOrder(t,e){const i=t===Q;return v(this._items,e,i,this._config.wrap)}_triggerSlideEvent(t,e){const i=this._getItemIndex(t),n=this._getItemIndex(V.findOne(nt,this._element));return j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e<i.length;e++)if(Number.parseInt(i[e].getAttribute("data-bs-slide-to"),10)===this._getItemIndex(t)){i[e].classList.add(it),i[e].setAttribute("aria-current","true");break}}}_updateInterval(){const t=this._activeElement||V.findOne(nt,this._element);if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);e?(this._config.defaultInterval=this._config.defaultInterval||this._config.interval,this._config.interval=e):this._config.interval=this._config.defaultInterval||this._config.interval}_slide(t,e){const i=this._directionToOrder(t),n=V.findOne(nt,this._element),s=this._getItemIndex(n),o=e||this._getItemByOrder(i,n),r=this._getItemIndex(o),a=Boolean(this._interval),l=i===Q,c=l?"carousel-item-start":"carousel-item-end",h=l?"carousel-item-next":"carousel-item-prev",d=this._orderToDirection(i);if(o&&o.classList.contains(it))return void(this._isSliding=!1);if(this._isSliding)return;if(this._triggerSlideEvent(o,d).defaultPrevented)return;if(!n||!o)return;this._isSliding=!0,a&&this.pause(),this._setActiveIndicatorElement(o),this._activeElement=o;const f=()=>{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;e<i;e++)st.carouselInterface(t[e],st.getInstance(t[e]))})),g(st);const ot="collapse",rt={toggle:!0,parent:null},at={toggle:"boolean",parent:"(null|element)"},lt="show",ct="collapse",ht="collapsing",dt="collapsed",ut=":scope .collapse .collapse",ft='[data-bs-toggle="collapse"]';class pt extends B{constructor(t,e){super(t),this._isTransitioning=!1,this._config=this._getConfig(e),this._triggerArray=[];const n=V.find(ft);for(let t=0,e=n.length;t<e;t++){const e=n[t],s=i(e),o=V.find(s).filter((t=>t===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t<e;t++){const e=this._triggerArray[t],i=n(e);i&&!this._isShown(i)&&this._addAriaAndCollapsedClass([e],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const i=pt.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}j.on(document,"click.bs.collapse.data-api",ft,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=i(this);V.find(e).forEach((t=>{pt.getOrCreateInstance(t,{toggle:!1}).toggle()}))})),g(pt);var mt="top",gt="bottom",_t="right",bt="left",vt="auto",yt=[mt,gt,_t,bt],wt="start",Et="end",At="clippingParents",Tt="viewport",Ot="popper",Ct="reference",kt=yt.reduce((function(t,e){return t.concat([e+"-"+wt,e+"-"+Et])}),[]),Lt=[].concat(yt,[vt]).reduce((function(t,e){return t.concat([e,e+"-"+wt,e+"-"+Et])}),[]),xt="beforeRead",Dt="read",St="afterRead",Nt="beforeMain",It="main",Pt="afterMain",jt="beforeWrite",Mt="write",Ht="afterWrite",Bt=[xt,Dt,St,Nt,It,Pt,jt,Mt,Ht];function Rt(t){return t?(t.nodeName||"").toLowerCase():null}function Wt(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return 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Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new 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this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" 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null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map</script>
+<style type="text/css">@font-face {
+font-display: block;
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) format("woff");
+}
+.bi::before,
+[class^="bi-"]::before,
+[class*=" bi-"]::before {
+display: inline-block;
+font-family: bootstrap-icons !important;
+font-style: normal;
+font-weight: normal !important;
+font-variant: normal;
+text-transform: none;
+line-height: 1;
+vertical-align: -.125em;
+-webkit-font-smoothing: antialiased;
+-moz-osx-font-smoothing: grayscale;
+}
+.bi-123::before { content: "\f67f"; }
+.bi-alarm-fill::before { content: "\f101"; }
+.bi-alarm::before { content: "\f102"; }
+.bi-align-bottom::before { content: "\f103"; }
+.bi-align-center::before { content: "\f104"; }
+.bi-align-end::before { content: "\f105"; }
+.bi-align-middle::before { content: "\f106"; }
+.bi-align-start::before { content: "\f107"; }
+.bi-align-top::before { content: "\f108"; }
+.bi-alt::before { content: "\f109"; }
+.bi-app-indicator::before { content: "\f10a"; }
+.bi-app::before { content: "\f10b"; }
+.bi-archive-fill::before { content: "\f10c"; }
+.bi-archive::before { content: "\f10d"; }
+.bi-arrow-90deg-down::before { content: "\f10e"; }
+.bi-arrow-90deg-left::before { content: "\f10f"; }
+.bi-arrow-90deg-right::before { content: "\f110"; }
+.bi-arrow-90deg-up::before { content: "\f111"; }
+.bi-arrow-bar-down::before { content: "\f112"; }
+.bi-arrow-bar-left::before { content: "\f113"; }
+.bi-arrow-bar-right::before { content: "\f114"; }
+.bi-arrow-bar-up::before { content: "\f115"; }
+.bi-arrow-clockwise::before { content: "\f116"; }
+.bi-arrow-counterclockwise::before { content: "\f117"; }
+.bi-arrow-down-circle-fill::before { content: "\f118"; }
+.bi-arrow-down-circle::before { content: "\f119"; }
+.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
+.bi-arrow-down-left-circle::before { content: "\f11b"; }
+.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
+.bi-arrow-down-left-square::before { content: "\f11d"; }
+.bi-arrow-down-left::before { content: "\f11e"; }
+.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
+.bi-arrow-down-right-circle::before { content: "\f120"; }
+.bi-arrow-down-right-square-fill::before { content: "\f121"; }
+.bi-arrow-down-right-square::before { content: "\f122"; }
+.bi-arrow-down-right::before { content: "\f123"; }
+.bi-arrow-down-short::before { content: "\f124"; }
+.bi-arrow-down-square-fill::before { content: "\f125"; }
+.bi-arrow-down-square::before { content: "\f126"; }
+.bi-arrow-down-up::before { content: "\f127"; }
+.bi-arrow-down::before { content: "\f128"; }
+.bi-arrow-left-circle-fill::before { content: "\f129"; }
+.bi-arrow-left-circle::before { content: "\f12a"; }
+.bi-arrow-left-right::before { content: "\f12b"; }
+.bi-arrow-left-short::before { content: "\f12c"; }
+.bi-arrow-left-square-fill::before { content: "\f12d"; }
+.bi-arrow-left-square::before { content: "\f12e"; }
+.bi-arrow-left::before { content: "\f12f"; }
+.bi-arrow-repeat::before { content: "\f130"; }
+.bi-arrow-return-left::before { content: "\f131"; }
+.bi-arrow-return-right::before { content: "\f132"; }
+.bi-arrow-right-circle-fill::before { content: "\f133"; }
+.bi-arrow-right-circle::before { content: "\f134"; }
+.bi-arrow-right-short::before { content: "\f135"; }
+.bi-arrow-right-square-fill::before { content: "\f136"; }
+.bi-arrow-right-square::before { content: "\f137"; }
+.bi-arrow-right::before { content: "\f138"; }
+.bi-arrow-up-circle-fill::before { content: "\f139"; }
+.bi-arrow-up-circle::before { content: "\f13a"; }
+.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
+.bi-arrow-up-left-circle::before { content: "\f13c"; }
+.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
+.bi-arrow-up-left-square::before { content: "\f13e"; }
+.bi-arrow-up-left::before { content: "\f13f"; }
+.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
+.bi-arrow-up-right-circle::before { content: "\f141"; }
+.bi-arrow-up-right-square-fill::before { content: "\f142"; }
+.bi-arrow-up-right-square::before { content: "\f143"; }
+.bi-arrow-up-right::before { content: "\f144"; }
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+.bi-7-square::before { content: "\f7bd"; }
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+.bi-8-square::before { content: "\f7c3"; }
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+.bi-9-circle-fill::before { content: "\f7c6"; }
+.bi-9-circle::before { content: "\f7c7"; }
+.bi-9-square-fill::before { content: "\f7c8"; }
+.bi-9-square::before { content: "\f7c9"; }
+.bi-airplane-engines-fill::before { content: "\f7ca"; }
+.bi-airplane-engines::before { content: "\f7cb"; }
+.bi-airplane-fill::before { content: "\f7cc"; }
+.bi-airplane::before { content: "\f7cd"; }
+.bi-alexa::before { content: "\f7ce"; }
+.bi-alipay::before { content: "\f7cf"; }
+.bi-android::before { content: "\f7d0"; }
+.bi-android2::before { content: "\f7d1"; }
+.bi-box-fill::before { content: "\f7d2"; }
+.bi-box-seam-fill::before { content: "\f7d3"; }
+.bi-browser-chrome::before { content: "\f7d4"; }
+.bi-browser-edge::before { content: "\f7d5"; }
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+.bi-c-circle-fill-1::before { content: "\f7d9"; }
+.bi-c-circle-fill::before { content: "\f7da"; }
+.bi-c-circle::before { content: "\f7db"; }
+.bi-c-square-fill::before { content: "\f7dc"; }
+.bi-c-square::before { content: "\f7dd"; }
+.bi-capsule-pill::before { content: "\f7de"; }
+.bi-capsule::before { content: "\f7df"; }
+.bi-car-front-fill::before { content: "\f7e0"; }
+.bi-car-front::before { content: "\f7e1"; }
+.bi-cassette-fill::before { content: "\f7e2"; }
+.bi-cassette::before { content: "\f7e3"; }
+.bi-cc-circle-1::before { content: "\f7e4"; }
+.bi-cc-circle-fill-1::before { content: "\f7e5"; }
+.bi-cc-circle-fill::before { content: "\f7e6"; }
+.bi-cc-circle::before { content: "\f7e7"; }
+.bi-cc-square-fill::before { content: "\f7e8"; }
+.bi-cc-square::before { content: "\f7e9"; }
+.bi-cup-hot-fill::before { content: "\f7ea"; }
+.bi-cup-hot::before { content: "\f7eb"; }
+.bi-currency-rupee::before { content: "\f7ec"; }
+.bi-dropbox::before { content: "\f7ed"; }
+.bi-escape::before { content: "\f7ee"; }
+.bi-fast-forward-btn-fill::before { content: "\f7ef"; }
+.bi-fast-forward-btn::before { content: "\f7f0"; }
+.bi-fast-forward-circle-fill::before { content: "\f7f1"; }
+.bi-fast-forward-circle::before { content: "\f7f2"; }
+.bi-fast-forward-fill::before { content: "\f7f3"; }
+.bi-fast-forward::before { content: "\f7f4"; }
+.bi-filetype-sql::before { content: "\f7f5"; }
+.bi-fire::before { content: "\f7f6"; }
+.bi-google-play::before { content: "\f7f7"; }
+.bi-h-circle-1::before { content: "\f7f8"; }
+.bi-h-circle-fill-1::before { content: "\f7f9"; }
+.bi-h-circle-fill::before { content: "\f7fa"; }
+.bi-h-circle::before { content: "\f7fb"; }
+.bi-h-square-fill::before { content: "\f7fc"; }
+.bi-h-square::before { content: "\f7fd"; }
+.bi-indent::before { content: "\f7fe"; }
+.bi-lungs-fill::before { content: "\f7ff"; }
+.bi-lungs::before { content: "\f800"; }
+.bi-microsoft-teams::before { content: "\f801"; }
+.bi-p-circle-1::before { content: "\f802"; }
+.bi-p-circle-fill-1::before { content: "\f803"; }
+.bi-p-circle-fill::before { content: "\f804"; }
+.bi-p-circle::before { content: "\f805"; }
+.bi-p-square-fill::before { content: "\f806"; }
+.bi-p-square::before { content: "\f807"; }
+.bi-pass-fill::before { content: "\f808"; }
+.bi-pass::before { content: "\f809"; }
+.bi-prescription::before { content: "\f80a"; }
+.bi-prescription2::before { content: "\f80b"; }
+.bi-r-circle-1::before { content: "\f80c"; }
+.bi-r-circle-fill-1::before { content: "\f80d"; }
+.bi-r-circle-fill::before { content: "\f80e"; }
+.bi-r-circle::before { content: "\f80f"; }
+.bi-r-square-fill::before { content: "\f810"; }
+.bi-r-square::before { content: "\f811"; }
+.bi-repeat-1::before { content: "\f812"; }
+.bi-repeat::before { content: "\f813"; }
+.bi-rewind-btn-fill::before { content: "\f814"; }
+.bi-rewind-btn::before { content: "\f815"; }
+.bi-rewind-circle-fill::before { content: "\f816"; }
+.bi-rewind-circle::before { content: "\f817"; }
+.bi-rewind-fill::before { content: "\f818"; }
+.bi-rewind::before { content: "\f819"; }
+.bi-train-freight-front-fill::before { content: "\f81a"; }
+.bi-train-freight-front::before { content: "\f81b"; }
+.bi-train-front-fill::before { content: "\f81c"; }
+.bi-train-front::before { content: "\f81d"; }
+.bi-train-lightrail-front-fill::before { content: "\f81e"; }
+.bi-train-lightrail-front::before { content: "\f81f"; }
+.bi-truck-front-fill::before { content: "\f820"; }
+.bi-truck-front::before { content: "\f821"; }
+.bi-ubuntu::before { content: "\f822"; }
+.bi-unindent::before { content: "\f823"; }
+.bi-unity::before { content: "\f824"; }
+.bi-universal-access-circle::before { content: "\f825"; }
+.bi-universal-access::before { content: "\f826"; }
+.bi-virus::before { content: "\f827"; }
+.bi-virus2::before { content: "\f828"; }
+.bi-wechat::before { content: "\f829"; }
+.bi-yelp::before { content: "\f82a"; }
+.bi-sign-stop-fill::before { content: "\f82b"; }
+.bi-sign-stop-lights-fill::before { content: "\f82c"; }
+.bi-sign-stop-lights::before { content: "\f82d"; }
+.bi-sign-stop::before { content: "\f82e"; }
+.bi-sign-turn-left-fill::before { content: "\f82f"; }
+.bi-sign-turn-left::before { content: "\f830"; }
+.bi-sign-turn-right-fill::before { content: "\f831"; }
+.bi-sign-turn-right::before { content: "\f832"; }
+.bi-sign-turn-slight-left-fill::before { content: "\f833"; }
+.bi-sign-turn-slight-left::before { content: "\f834"; }
+.bi-sign-turn-slight-right-fill::before { content: "\f835"; }
+.bi-sign-turn-slight-right::before { content: "\f836"; }
+.bi-sign-yield-fill::before { content: "\f837"; }
+.bi-sign-yield::before { content: "\f838"; }
+.bi-ev-station-fill::before { content: "\f839"; }
+.bi-ev-station::before { content: "\f83a"; }
+.bi-fuel-pump-diesel-fill::before { content: "\f83b"; }
+.bi-fuel-pump-diesel::before { content: "\f83c"; }
+.bi-fuel-pump-fill::before { content: "\f83d"; }
+.bi-fuel-pump::before { content: "\f83e"; }
+.bi-0-circle-fill::before { content: "\f83f"; }
+.bi-0-circle::before { content: "\f840"; }
+.bi-0-square-fill::before { content: "\f841"; }
+.bi-0-square::before { content: "\f842"; }
+.bi-rocket-fill::before { content: "\f843"; }
+.bi-rocket-takeoff-fill::before { content: "\f844"; }
+.bi-rocket-takeoff::before { content: "\f845"; }
+.bi-rocket::before { content: "\f846"; }
+.bi-stripe::before { content: "\f847"; }
+.bi-subscript::before { content: "\f848"; }
+.bi-superscript::before { content: "\f849"; }
+.bi-trello::before { content: "\f84a"; }
+.bi-envelope-at-fill::before { content: "\f84b"; }
+.bi-envelope-at::before { content: "\f84c"; }
+.bi-regex::before { content: "\f84d"; }
+.bi-text-wrap::before { content: "\f84e"; }
+.bi-sign-dead-end-fill::before { content: "\f84f"; }
+.bi-sign-dead-end::before { content: "\f850"; }
+.bi-sign-do-not-enter-fill::before { content: "\f851"; }
+.bi-sign-do-not-enter::before { content: "\f852"; }
+.bi-sign-intersection-fill::before { content: "\f853"; }
+.bi-sign-intersection-side-fill::before { content: "\f854"; }
+.bi-sign-intersection-side::before { content: "\f855"; }
+.bi-sign-intersection-t-fill::before { content: "\f856"; }
+.bi-sign-intersection-t::before { content: "\f857"; }
+.bi-sign-intersection-y-fill::before { content: "\f858"; }
+.bi-sign-intersection-y::before { content: "\f859"; }
+.bi-sign-intersection::before { content: "\f85a"; }
+.bi-sign-merge-left-fill::before { content: "\f85b"; }
+.bi-sign-merge-left::before { content: "\f85c"; }
+.bi-sign-merge-right-fill::before { content: "\f85d"; }
+.bi-sign-merge-right::before { content: "\f85e"; }
+.bi-sign-no-left-turn-fill::before { content: "\f85f"; }
+.bi-sign-no-left-turn::before { content: "\f860"; }
+.bi-sign-no-parking-fill::before { content: "\f861"; }
+.bi-sign-no-parking::before { content: "\f862"; }
+.bi-sign-no-right-turn-fill::before { content: "\f863"; }
+.bi-sign-no-right-turn::before { content: "\f864"; }
+.bi-sign-railroad-fill::before { content: "\f865"; }
+.bi-sign-railroad::before { content: "\f866"; }
+.bi-building-add::before { content: "\f867"; }
+.bi-building-check::before { content: "\f868"; }
+.bi-building-dash::before { content: "\f869"; }
+.bi-building-down::before { content: "\f86a"; }
+.bi-building-exclamation::before { content: "\f86b"; }
+.bi-building-fill-add::before { content: "\f86c"; }
+.bi-building-fill-check::before { content: "\f86d"; }
+.bi-building-fill-dash::before { content: "\f86e"; }
+.bi-building-fill-down::before { content: "\f86f"; }
+.bi-building-fill-exclamation::before { content: "\f870"; }
+.bi-building-fill-gear::before { content: "\f871"; }
+.bi-building-fill-lock::before { content: "\f872"; }
+.bi-building-fill-slash::before { content: "\f873"; }
+.bi-building-fill-up::before { content: "\f874"; }
+.bi-building-fill-x::before { content: "\f875"; }
+.bi-building-fill::before { content: "\f876"; }
+.bi-building-gear::before { content: "\f877"; }
+.bi-building-lock::before { content: "\f878"; }
+.bi-building-slash::before { content: "\f879"; }
+.bi-building-up::before { content: "\f87a"; }
+.bi-building-x::before { content: "\f87b"; }
+.bi-buildings-fill::before { content: "\f87c"; }
+.bi-buildings::before { content: "\f87d"; }
+.bi-bus-front-fill::before { content: "\f87e"; }
+.bi-bus-front::before { content: "\f87f"; }
+.bi-ev-front-fill::before { content: "\f880"; }
+.bi-ev-front::before { content: "\f881"; }
+.bi-globe-americas::before { content: "\f882"; }
+.bi-globe-asia-australia::before { content: "\f883"; }
+.bi-globe-central-south-asia::before { content: "\f884"; }
+.bi-globe-europe-africa::before { content: "\f885"; }
+.bi-house-add-fill::before { content: "\f886"; }
+.bi-house-add::before { content: "\f887"; }
+.bi-house-check-fill::before { content: "\f888"; }
+.bi-house-check::before { content: "\f889"; }
+.bi-house-dash-fill::before { content: "\f88a"; }
+.bi-house-dash::before { content: "\f88b"; }
+.bi-house-down-fill::before { content: "\f88c"; }
+.bi-house-down::before { content: "\f88d"; }
+.bi-house-exclamation-fill::before { content: "\f88e"; }
+.bi-house-exclamation::before { content: "\f88f"; }
+.bi-house-gear-fill::before { content: "\f890"; }
+.bi-house-gear::before { content: "\f891"; }
+.bi-house-lock-fill::before { content: "\f892"; }
+.bi-house-lock::before { content: "\f893"; }
+.bi-house-slash-fill::before { content: "\f894"; }
+.bi-house-slash::before { content: "\f895"; }
+.bi-house-up-fill::before { content: "\f896"; }
+.bi-house-up::before { content: "\f897"; }
+.bi-house-x-fill::before { content: "\f898"; }
+.bi-house-x::before { content: "\f899"; }
+.bi-person-add::before { content: "\f89a"; }
+.bi-person-down::before { content: "\f89b"; }
+.bi-person-exclamation::before { content: "\f89c"; }
+.bi-person-fill-add::before { content: "\f89d"; }
+.bi-person-fill-check::before { content: "\f89e"; }
+.bi-person-fill-dash::before { content: "\f89f"; }
+.bi-person-fill-down::before { content: "\f8a0"; }
+.bi-person-fill-exclamation::before { content: "\f8a1"; }
+.bi-person-fill-gear::before { content: "\f8a2"; }
+.bi-person-fill-lock::before { content: "\f8a3"; }
+.bi-person-fill-slash::before { content: "\f8a4"; }
+.bi-person-fill-up::before { content: "\f8a5"; }
+.bi-person-fill-x::before { content: "\f8a6"; }
+.bi-person-gear::before { content: "\f8a7"; }
+.bi-person-lock::before { content: "\f8a8"; }
+.bi-person-slash::before { content: "\f8a9"; }
+.bi-person-up::before { content: "\f8aa"; }
+.bi-scooter::before { content: "\f8ab"; }
+.bi-taxi-front-fill::before { content: "\f8ac"; }
+.bi-taxi-front::before { content: "\f8ad"; }
+.bi-amd::before { content: "\f8ae"; }
+.bi-database-add::before { content: "\f8af"; }
+.bi-database-check::before { content: "\f8b0"; }
+.bi-database-dash::before { content: "\f8b1"; }
+.bi-database-down::before { content: "\f8b2"; }
+.bi-database-exclamation::before { content: "\f8b3"; }
+.bi-database-fill-add::before { content: "\f8b4"; }
+.bi-database-fill-check::before { content: "\f8b5"; }
+.bi-database-fill-dash::before { content: "\f8b6"; }
+.bi-database-fill-down::before { content: "\f8b7"; }
+.bi-database-fill-exclamation::before { content: "\f8b8"; }
+.bi-database-fill-gear::before { content: "\f8b9"; }
+.bi-database-fill-lock::before { content: "\f8ba"; }
+.bi-database-fill-slash::before { content: "\f8bb"; }
+.bi-database-fill-up::before { content: "\f8bc"; }
+.bi-database-fill-x::before { content: "\f8bd"; }
+.bi-database-fill::before { content: "\f8be"; }
+.bi-database-gear::before { content: "\f8bf"; }
+.bi-database-lock::before { content: "\f8c0"; }
+.bi-database-slash::before { content: "\f8c1"; }
+.bi-database-up::before { content: "\f8c2"; }
+.bi-database-x::before { content: "\f8c3"; }
+.bi-database::before { content: "\f8c4"; }
+.bi-houses-fill::before { content: "\f8c5"; }
+.bi-houses::before { content: "\f8c6"; }
+.bi-nvidia::before { content: "\f8c7"; }
+.bi-person-vcard-fill::before { content: "\f8c8"; }
+.bi-person-vcard::before { content: "\f8c9"; }
+.bi-sina-weibo::before { content: "\f8ca"; }
+.bi-tencent-qq::before { content: "\f8cb"; }
+.bi-wikipedia::before { content: "\f8cc"; }
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+</style>
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+</head>
+
+<body class="fullcontent">
+
+<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
+
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">Simulation experimental design</h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+ <div>
+ <div class="quarto-title-meta-heading">Author</div>
+ <div class="quarto-title-meta-contents">
+ <p>Julian Sagebiel </p>
+ </div>
+ </div>
+
+ <div>
+ <div class="quarto-title-meta-heading">Published</div>
+ <div class="quarto-title-meta-contents">
+ <p class="date">January 5, 2023</p>
+ </div>
+ </div>
+
+
+ </div>
+
+
+</header>
+
+<p>The simulation has 360 respondents and 2 runs.</p>
+<p>the parameters used for the simulation are:</p>
+<div class="cell">
+<div class="cell-output cell-output-stdout">
+<pre><code>$b_beitrag
+b_beitrag
+ 1.15
+
+$b_entfernung
+b_entfernung
+ -0.23
+
+$b_gemeinschaft
+b_gemeinschaft
+ 0.13
+
+$b_gestaltung
+b_gestaltung
+ 0.39
+
+$b_groesse
+b_groesse
+ 0.07
+
+$b_kultur
+b_kultur
+ 0.16
+
+$b_umweltbildung
+b_umweltbildung
+ 0.14
+
+$b_zugang
+b_zugang
+ 0.07 </code></pre>
+</div>
+</div>
+<p>The average frequencies of choices for each alternative are</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>gr<span class="ot"><-</span><span class="fu">map_dfr</span>(<span class="fu">seq_along</span>(<span class="dv">1</span><span class="sc">:</span>nosim), <span class="sc">~</span> <span class="fu">table</span>(all_designs[[<span class="st">"bayeff"</span>]][[.]][[<span class="st">"data"</span>]][[<span class="st">"CHOICE"</span>]])<span class="sc">/</span><span class="fu">length</span>(all_designs[[<span class="st">"bayeff"</span>]][[<span class="dv">1</span>]][[<span class="st">"data"</span>]][[<span class="st">"CHOICE"</span>]])) <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">everything</span>(),as.numeric)) </span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(<span class="fu">summary</span>(gr)[<span class="dv">4</span>,])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<section id="statistics-and-power" class="level1">
+<h1>Statistics and power</h1>
+<p>Here you see the statistics of your parameters for the 2 runs.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(summaryall ,<span class="at">digits =</span> <span class="dv">3</span>) <span class="sc">%>%</span> <span class="fu">kable_styling</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output-display">
+<table class="table table-sm table-striped small" data-quarto-postprocess="true">
+<thead>
+<tr class="header">
+<th style="text-align: left;" data-quarto-table-cell-role="th"></th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.n</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.n</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.mean</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.mean</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.sd</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.sd</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.min</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.min</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.max</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.max</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.range</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.range</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">bayefficient.se</th>
+<th style="text-align: right;" data-quarto-table-cell-role="th">efficient.se</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td style="text-align: left;">est_asc_gemeinschaft</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.604</td>
+<td style="text-align: right;">0.648</td>
+<td style="text-align: right;">0.065</td>
+<td style="text-align: right;">0.242</td>
+<td style="text-align: right;">0.558</td>
+<td style="text-align: right;">0.477</td>
+<td style="text-align: right;">0.650</td>
+<td style="text-align: right;">0.819</td>
+<td style="text-align: right;">0.092</td>
+<td style="text-align: right;">0.342</td>
+<td style="text-align: right;">0.046</td>
+<td style="text-align: right;">0.171</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">est_b_groesse</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.074</td>
+<td style="text-align: right;">0.065</td>
+<td style="text-align: right;">0.023</td>
+<td style="text-align: right;">0.032</td>
+<td style="text-align: right;">0.058</td>
+<td style="text-align: right;">0.043</td>
+<td style="text-align: right;">0.091</td>
+<td style="text-align: right;">0.088</td>
+<td style="text-align: right;">0.033</td>
+<td style="text-align: right;">0.045</td>
+<td style="text-align: right;">0.016</td>
+<td style="text-align: right;">0.022</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">est_b_entfernung</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">-0.222</td>
+<td style="text-align: right;">-0.223</td>
+<td style="text-align: right;">0.006</td>
+<td style="text-align: right;">0.003</td>
+<td style="text-align: right;">-0.227</td>
+<td style="text-align: right;">-0.225</td>
+<td style="text-align: right;">-0.218</td>
+<td style="text-align: right;">-0.221</td>
+<td style="text-align: right;">0.009</td>
+<td style="text-align: right;">0.004</td>
+<td style="text-align: right;">0.005</td>
+<td style="text-align: right;">0.002</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">est_b_gemeinschaft</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.101</td>
+<td style="text-align: right;">0.124</td>
+<td style="text-align: right;">0.005</td>
+<td style="text-align: right;">0.042</td>
+<td style="text-align: right;">0.098</td>
+<td style="text-align: right;">0.094</td>
+<td style="text-align: right;">0.105</td>
+<td style="text-align: right;">0.154</td>
+<td style="text-align: right;">0.006</td>
+<td style="text-align: right;">0.060</td>
+<td style="text-align: right;">0.003</td>
+<td style="text-align: right;">0.030</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">est_b_kultur</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.145</td>
+<td style="text-align: right;">0.160</td>
+<td style="text-align: right;">0.035</td>
+<td style="text-align: right;">0.038</td>
+<td style="text-align: right;">0.121</td>
+<td style="text-align: right;">0.133</td>
+<td style="text-align: right;">0.170</td>
+<td style="text-align: right;">0.187</td>
+<td style="text-align: right;">0.049</td>
+<td style="text-align: right;">0.054</td>
+<td style="text-align: right;">0.025</td>
+<td style="text-align: right;">0.027</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">est_b_umweltbildung</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.119</td>
+<td style="text-align: right;">0.154</td>
+<td style="text-align: right;">0.049</td>
+<td style="text-align: right;">0.059</td>
+<td style="text-align: right;">0.085</td>
+<td style="text-align: right;">0.112</td>
+<td style="text-align: right;">0.153</td>
+<td style="text-align: right;">0.196</td>
+<td style="text-align: right;">0.069</td>
+<td style="text-align: right;">0.083</td>
+<td style="text-align: right;">0.034</td>
+<td style="text-align: right;">0.042</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">est_b_zugang</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.058</td>
+<td style="text-align: right;">0.060</td>
+<td style="text-align: right;">0.004</td>
+<td style="text-align: right;">0.005</td>
+<td style="text-align: right;">0.055</td>
+<td style="text-align: right;">0.056</td>
+<td style="text-align: right;">0.060</td>
+<td style="text-align: right;">0.063</td>
+<td style="text-align: right;">0.006</td>
+<td style="text-align: right;">0.007</td>
+<td style="text-align: right;">0.003</td>
+<td style="text-align: right;">0.003</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">est_b_gestaltung</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.384</td>
+<td style="text-align: right;">0.336</td>
+<td style="text-align: right;">0.025</td>
+<td style="text-align: right;">0.066</td>
+<td style="text-align: right;">0.366</td>
+<td style="text-align: right;">0.289</td>
+<td style="text-align: right;">0.401</td>
+<td style="text-align: right;">0.382</td>
+<td style="text-align: right;">0.035</td>
+<td style="text-align: right;">0.094</td>
+<td style="text-align: right;">0.018</td>
+<td style="text-align: right;">0.047</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">est_b_beitrag</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">1.136</td>
+<td style="text-align: right;">1.067</td>
+<td style="text-align: right;">0.107</td>
+<td style="text-align: right;">0.092</td>
+<td style="text-align: right;">1.060</td>
+<td style="text-align: right;">1.002</td>
+<td style="text-align: right;">1.211</td>
+<td style="text-align: right;">1.132</td>
+<td style="text-align: right;">0.151</td>
+<td style="text-align: right;">0.131</td>
+<td style="text-align: right;">0.076</td>
+<td style="text-align: right;">0.065</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">est_asc_klein</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.378</td>
+<td style="text-align: right;">0.485</td>
+<td style="text-align: right;">0.124</td>
+<td style="text-align: right;">0.257</td>
+<td style="text-align: right;">0.291</td>
+<td style="text-align: right;">0.303</td>
+<td style="text-align: right;">0.466</td>
+<td style="text-align: right;">0.667</td>
+<td style="text-align: right;">0.175</td>
+<td style="text-align: right;">0.363</td>
+<td style="text-align: right;">0.087</td>
+<td style="text-align: right;">0.182</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">rob_pval0_asc_gemeinschaft</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">rob_pval0_b_groesse</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.030</td>
+<td style="text-align: right;">0.075</td>
+<td style="text-align: right;">0.042</td>
+<td style="text-align: right;">0.106</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.060</td>
+<td style="text-align: right;">0.150</td>
+<td style="text-align: right;">0.060</td>
+<td style="text-align: right;">0.150</td>
+<td style="text-align: right;">0.030</td>
+<td style="text-align: right;">0.075</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">rob_pval0_b_entfernung</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">rob_pval0_b_gemeinschaft</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.010</td>
+<td style="text-align: right;">0.010</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.014</td>
+<td style="text-align: right;">0.010</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.010</td>
+<td style="text-align: right;">0.020</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.020</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.010</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">rob_pval0_b_kultur</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">rob_pval0_b_umweltbildung</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.020</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.028</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.040</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.040</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.020</td>
+<td style="text-align: right;">0.000</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">rob_pval0_b_zugang</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">rob_pval0_b_gestaltung</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">rob_pval0_b_beitrag</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">rob_pval0_asc_klein</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">2</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+<td style="text-align: right;">0.000</td>
+</tr>
+</tbody>
+</table>
+
+
+</div>
+</div>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>powa</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>$bayefficient
+
+FALSE TRUE
+ 50 50
+
+$efficient
+
+FALSE TRUE
+ 50 50 </code></pre>
+</div>
+</div>
+<section id="illustration-of-simulated-parameter-values" class="level2">
+<h2 class="anchored" data-anchor-id="illustration-of-simulated-parameter-values">Illustration of simulated parameter values</h2>
+<p>To facilitate interpretation and judgement of the different designs, you can plot the densities of simulated parameter values from the different experimental designs.</p>
+<div class="cell">
+<div class="cell-output-display">
+<p><img src="data:image/png;base64,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" 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" 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" 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" 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" 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" 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" 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" 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>$ascgemeinschaft</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$groesse</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$entfernung</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$gemeinschaft</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$kultur</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$umweltbildung</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$zugang</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$gestaltung</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$beitrag</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" class="img-fluid" width="672"></p>
+</div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+$ascklein</code></pre>
+</div>
+<div class="cell-output-display">
+<p><img 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" 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" class="img-fluid" width="672"></p>
+</div>
+</div>
+</section>
+</section>
+
+</main>
+<!-- /main column -->
+<script id="quarto-html-after-body" type="application/javascript">
+window.document.addEventListener("DOMContentLoaded", function (event) {
+ const toggleBodyColorMode = (bsSheetEl) => {
+ const mode = bsSheetEl.getAttribute("data-mode");
+ const bodyEl = window.document.querySelector("body");
+ if (mode === "dark") {
+ bodyEl.classList.add("quarto-dark");
+ bodyEl.classList.remove("quarto-light");
+ } else {
+ bodyEl.classList.add("quarto-light");
+ bodyEl.classList.remove("quarto-dark");
+ }
+ }
+ const toggleBodyColorPrimary = () => {
+ const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");
+ if (bsSheetEl) {
+ toggleBodyColorMode(bsSheetEl);
+ }
+ }
+ toggleBodyColorPrimary();
+ const icon = "";
+ const anchorJS = new window.AnchorJS();
+ anchorJS.options = {
+ placement: 'right',
+ icon: icon
+ };
+ anchorJS.add('.anchored');
+ const isCodeAnnotation = (el) => {
+ for (const clz of el.classList) {
+ if (clz.startsWith('code-annotation-')) {
+ return true;
+ }
+ }
+ return false;
+ }
+ const clipboard = new window.ClipboardJS('.code-copy-button', {
+ text: function(trigger) {
+ const codeEl = trigger.previousElementSibling.cloneNode(true);
+ for (const childEl of codeEl.children) {
+ if (isCodeAnnotation(childEl)) {
+ childEl.remove();
+ }
+ }
+ return codeEl.innerText;
+ }
+ });
+ clipboard.on('success', function(e) {
+ // button target
+ const button = e.trigger;
+ // don't keep focus
+ button.blur();
+ // flash "checked"
+ button.classList.add('code-copy-button-checked');
+ var currentTitle = button.getAttribute("title");
+ button.setAttribute("title", "Copied!");
+ let tooltip;
+ if (window.bootstrap) {
+ button.setAttribute("data-bs-toggle", "tooltip");
+ button.setAttribute("data-bs-placement", "left");
+ button.setAttribute("data-bs-title", "Copied!");
+ tooltip = new bootstrap.Tooltip(button,
+ { trigger: "manual",
+ customClass: "code-copy-button-tooltip",
+ offset: [0, -8]});
+ tooltip.show();
+ }
+ setTimeout(function() {
+ if (tooltip) {
+ tooltip.hide();
+ button.removeAttribute("data-bs-title");
+ button.removeAttribute("data-bs-toggle");
+ button.removeAttribute("data-bs-placement");
+ }
+ button.setAttribute("title", currentTitle);
+ button.classList.remove('code-copy-button-checked');
+ }, 1000);
+ // clear code selection
+ e.clearSelection();
+ });
+ function tippyHover(el, contentFn) {
+ const config = {
+ allowHTML: true,
+ content: contentFn,
+ maxWidth: 500,
+ delay: 100,
+ arrow: false,
+ appendTo: function(el) {
+ return el.parentElement;
+ },
+ interactive: true,
+ interactiveBorder: 10,
+ theme: 'quarto',
+ placement: 'bottom-start'
+ };
+ window.tippy(el, config);
+ }
+ const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
+ for (var i=0; i<noterefs.length; i++) {
+ const ref = noterefs[i];
+ tippyHover(ref, function() {
+ // use id or data attribute instead here
+ let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
+ try { href = new URL(href).hash; } catch {}
+ const id = href.replace(/^#\/?/, "");
+ const note = window.document.getElementById(id);
+ return note.innerHTML;
+ });
+ }
+ let selectedAnnoteEl;
+ const selectorForAnnotation = ( cell, annotation) => {
+ let cellAttr = 'data-code-cell="' + cell + '"';
+ let lineAttr = 'data-code-annotation="' + annotation + '"';
+ const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
+ return selector;
+ }
+ const selectCodeLines = (annoteEl) => {
+ const doc = window.document;
+ const targetCell = annoteEl.getAttribute("data-target-cell");
+ const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
+ const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
+ const lines = annoteSpan.getAttribute("data-code-lines").split(",");
+ const lineIds = lines.map((line) => {
+ return targetCell + "-" + line;
+ })
+ let top = null;
+ let height = null;
+ let parent = null;
+ if (lineIds.length > 0) {
+ //compute the position of the single el (top and bottom and make a div)
+ const el = window.document.getElementById(lineIds[0]);
+ top = el.offsetTop;
+ height = el.offsetHeight;
+ parent = el.parentElement.parentElement;
+ if (lineIds.length > 1) {
+ const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
+ const bottom = lastEl.offsetTop + lastEl.offsetHeight;
+ height = bottom - top;
+ }
+ if (top !== null && height !== null && parent !== null) {
+ // cook up a div (if necessary) and position it
+ let div = window.document.getElementById("code-annotation-line-highlight");
+ if (div === null) {
+ div = window.document.createElement("div");
+ div.setAttribute("id", "code-annotation-line-highlight");
+ div.style.position = 'absolute';
+ parent.appendChild(div);
+ }
+ div.style.top = top - 2 + "px";
+ div.style.height = height + 4 + "px";
+ let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
+ if (gutterDiv === null) {
+ gutterDiv = window.document.createElement("div");
+ gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
+ gutterDiv.style.position = 'absolute';
+ const codeCell = window.document.getElementById(targetCell);
+ const gutter = codeCell.querySelector('.code-annotation-gutter');
+ gutter.appendChild(gutterDiv);
+ }
+ gutterDiv.style.top = top - 2 + "px";
+ gutterDiv.style.height = height + 4 + "px";
+ }
+ selectedAnnoteEl = annoteEl;
+ }
+ };
+ const unselectCodeLines = () => {
+ const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
+ elementsIds.forEach((elId) => {
+ const div = window.document.getElementById(elId);
+ if (div) {
+ div.remove();
+ }
+ });
+ selectedAnnoteEl = undefined;
+ };
+ // Attach click handler to the DT
+ const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
+ for (const annoteDlNode of annoteDls) {
+ annoteDlNode.addEventListener('click', (event) => {
+ const clickedEl = event.target;
+ if (clickedEl !== selectedAnnoteEl) {
+ unselectCodeLines();
+ const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
+ if (activeEl) {
+ activeEl.classList.remove('code-annotation-active');
+ }
+ selectCodeLines(clickedEl);
+ clickedEl.classList.add('code-annotation-active');
+ } else {
+ // Unselect the line
+ unselectCodeLines();
+ clickedEl.classList.remove('code-annotation-active');
+ }
+ });
+ }
+ const findCites = (el) => {
+ const parentEl = el.parentElement;
+ if (parentEl) {
+ const cites = parentEl.dataset.cites;
+ if (cites) {
+ return {
+ el,
+ cites: cites.split(' ')
+ };
+ } else {
+ return findCites(el.parentElement)
+ }
+ } else {
+ return undefined;
+ }
+ };
+ var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
+ for (var i=0; i<bibliorefs.length; i++) {
+ const ref = bibliorefs[i];
+ const citeInfo = findCites(ref);
+ if (citeInfo) {
+ tippyHover(citeInfo.el, function() {
+ var popup = window.document.createElement('div');
+ citeInfo.cites.forEach(function(cite) {
+ var citeDiv = window.document.createElement('div');
+ citeDiv.classList.add('hanging-indent');
+ citeDiv.classList.add('csl-entry');
+ var biblioDiv = window.document.getElementById('ref-' + cite);
+ if (biblioDiv) {
+ citeDiv.innerHTML = biblioDiv.innerHTML;
+ }
+ popup.appendChild(citeDiv);
+ });
+ return popup.innerHTML;
+ });
+ }
+ }
+});
+</script>
+</div> <!-- /content -->
+
+
+
</body></html>
\ No newline at end of file
diff --git a/simulationcore_purrr.R b/simulationcore_purrr.R
index acb7dfff616bf1ea393b93d7919d6cabc0020d1d..21a22e8e712e2858265055b9aa760ee0a933abe4 100644
--- a/simulationcore_purrr.R
+++ b/simulationcore_purrr.R
@@ -6,7 +6,7 @@ library("formula.tools")
#source("parameters_SE Design-Agri.R")
-source("parameters_ip2_parks.R")
+source("parameters_ip2.R")
designfile<-list.files(designpath,full.names = T)