diff --git a/README.Rmd b/README.Rmd
index dfc2f669902f7ff442510ac55e23cf7898c4d2b4..bb8f0c1a66fc221ec7d157ff1bee37c23487ae45 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -46,33 +46,40 @@ remotes::install_gitlab(repo = "dj44vuri/simulateDCE" , host = "https://git.idiv
This is a basic example for a simulation:
```{r example}
- library(simulateDCE)
+
+
+rm(list=ls())
+library(simulateDCE)
library(rlang)
library(formula.tools)
-# Designpath indicates the folder where all designs that should be simulated are stored. Can be either ngd files (for NGENE) or R objects for spdesign)
+
+library(rlang)
+
designpath<- system.file("extdata","SE_DRIVE" ,package = "simulateDCE")
-# on your computer, it would be something like
-# designpath <- "c:/myfancyDCE/Designs"
+resps =120 # number of respondents
+nosim= 2 # number of simulations to run (about 500 is minimum)
+
-# number of respondents
-resps =120
-# number of simulations to run (about 200 is minimum if you want to be serious -- but it takes some time. always test your code with 2 simulations, and if it runs smoothly, go for more.)
-nosim= 2
-# If you want to use different groups of respondents, use this. The following line means that you have one group of 70% size and one group of 30% size
+
+# bpreis = -0.036
+# blade = -0.034
+# bwarte = -0.049
+
+
decisiongroups=c(0,0.7,1)
-# set the values of the parameters you want to use in the simulation
+# wrong parameters
+
+#
bpreis = -0.01
blade = -0.07
bwarte = 0.02
-# If you want to do some manipulations in the design before you simulate, add a list called manipulations. Here, we devide some attributes by 10
-
manipulations = list(alt1.x2= expr(alt1.x2/10),
alt1.x3= expr(alt1.x3/10),
alt2.x2= expr(alt2.x2/10),
@@ -80,8 +87,8 @@ manipulations = list(alt1.x2= expr(alt1.x2/10),
)
-#place your utility functions here. We have two utility functions and two sets of utility functions. This is because we assume that 70% act according to the utility u1 and 30% act to the utility u2 (that is, they only decide according to the price and ignore the other attributes)
-u<-list( u1 =
+#place your utility functions here
+ul<-list( u1 =
list(
v1 =V.1~ bpreis * alt1.x1 + blade*alt1.x2 + bwarte*alt1.x3 ,
@@ -94,11 +101,11 @@ u<-list( u1 =
)
-# specify the designtype "ngene" or "spdesign"
+
destype="ngene"
+sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
+ designpath = designpath, u=ul)
-#lets go
-sedrive <- sim_all()
```
diff --git a/README.md b/README.md
index 6a4f6b2f75db025dfe8b7a2e7116710f46cbf5cb..77d6112d23362c6ce53d493c022d351aaba0354b 100644
--- a/README.md
+++ b/README.md
@@ -45,7 +45,9 @@ remotes::install_gitlab(repo = "dj44vuri/simulateDCE" , host = "https://git.idiv
This is a basic example for a simulation:
``` r
- library(simulateDCE)
+
+rm(list=ls())
+library(simulateDCE)
library(rlang)
library(formula.tools)
#>
@@ -54,29 +56,33 @@ library(formula.tools)
#>
#> env
-# Designpath indicates the folder where all designs that should be simulated are stored. Can be either ngd files (for NGENE) or R objects for spdesign)
+
+library(rlang)
+
designpath<- system.file("extdata","SE_DRIVE" ,package = "simulateDCE")
-# on your computer, it would be something like
-# designpath <- "c:/myfancyDCE/Designs"
+resps =120 # number of respondents
+nosim= 2 # number of simulations to run (about 500 is minimum)
+
+
-# number of respondents
-resps =120
-# number of simulations to run (about 200 is minimum if you want to be serious -- but it takes some time. always test your code with 2 simulations, and if it runs smoothly, go for more.)
-nosim= 2
-# If you want to use different groups of respondents, use this. The following line means that you have one group of 70% size and one group of 30% size
+# bpreis = -0.036
+# blade = -0.034
+# bwarte = -0.049
+
+
decisiongroups=c(0,0.7,1)
-# set the values of the parameters you want to use in the simulation
+# wrong parameters
+
+#
bpreis = -0.01
blade = -0.07
bwarte = 0.02
-# If you want to do some manipulations in the design before you simulate, add a list called manipulations. Here, we devide some attributes by 10
-
manipulations = list(alt1.x2= expr(alt1.x2/10),
alt1.x3= expr(alt1.x3/10),
alt2.x2= expr(alt2.x2/10),
@@ -84,8 +90,8 @@ manipulations = list(alt1.x2= expr(alt1.x2/10),
)
-#place your utility functions here. We have two utility functions and two sets of utility functions. This is because we assume that 70% act according to the utility u1 and 30% act to the utility u2 (that is, they only decide according to the price and ignore the other attributes)
-u<-list( u1 =
+#place your utility functions here
+ul<-list( u1 =
list(
v1 =V.1~ bpreis * alt1.x1 + blade*alt1.x2 + bwarte*alt1.x3 ,
@@ -98,12 +104,11 @@ u<-list( u1 =
)
-# specify the designtype "ngene" or "spdesign"
-destype="ngene"
+destype="ngene"
-#lets go
-sedrive <- sim_all()
+sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
+ designpath = designpath, u=ul)
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
@@ -128,10 +133,6 @@ sedrive <- sim_all()
#> New names:
#> • `Choice situation` -> `Choice.situation`
#> • `` -> `...10`
-#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
-#> e.g.:
-#> dat <- vroom(...)
-#> problems(dat)
#>
#> does sou_gis exist: FALSE
#>
@@ -151,13 +152,13 @@ sedrive <- sim_all()
#> 4 1 32 40 20.0 2.5 80 2.5 0 1
#> 5 1 39 40 20.0 0.0 80 10.0 10 1
#> 6 1 48 60 5.0 2.5 20 5.0 10 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.800 2.1016946 2.7841271 1.3266946 0.9841271 1
-#> 2 1 -0.275 -0.775 -1.5595593 0.9636286 -1.8345593 0.1886286 2
-#> 3 1 -0.800 -0.950 0.9803524 -0.1170033 0.1803524 -1.0670033 1
-#> 4 1 -1.750 -0.975 -0.9363311 -0.2117177 -2.6863311 -1.1867177 2
-#> 5 1 -1.800 -1.300 0.2762203 2.7548043 -1.5237797 1.4548043 2
-#> 6 1 -0.900 -0.350 1.3557159 0.7504347 0.4557159 0.4004347 1
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.800 -0.28937157 2.15097352 -1.064372 0.3509735 2
+#> 2 1 -0.275 -0.775 -0.96139278 -0.20476786 -1.236393 -0.9797679 2
+#> 3 1 -0.800 -0.950 -1.22764761 -0.06043672 -2.027648 -1.0104367 2
+#> 4 1 -1.750 -0.975 -0.01653508 0.83311025 -1.766535 -0.1418897 2
+#> 5 1 -1.800 -1.300 0.55064443 -0.20286674 -1.249356 -1.5028667 1
+#> 6 1 -0.900 -0.350 -0.31623091 0.72473769 -1.216231 0.3747377 2
#>
#>
#> This is Run number 1
@@ -179,29 +180,29 @@ sedrive <- sim_all()
#> 4 1 32 40 20.0 2.5 80 2.5 0 1
#> 5 1 39 40 20.0 0.0 80 10.0 10 1
#> 6 1 48 60 5.0 2.5 20 5.0 10 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.800 -0.40273624 -0.10075471 -1.1777362 -1.9007547 1
-#> 2 1 -0.275 -0.775 0.48834933 1.92667239 0.2133493 1.1516724 2
-#> 3 1 -0.800 -0.950 2.11775049 -0.36980424 1.3177505 -1.3198042 1
-#> 4 1 -1.750 -0.975 -0.21469434 -0.05134613 -1.9646943 -1.0263461 2
-#> 5 1 -1.800 -1.300 1.66727931 1.79712166 -0.1327207 0.4971217 2
-#> 6 1 -0.900 -0.350 -0.01714569 1.40783581 -0.9171457 1.0578358 2
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.800 0.4008023 4.3514331 -0.3741977 2.5514331 2
+#> 2 1 -0.275 -0.775 -0.1892883 -0.7606078 -0.4642883 -1.5356078 1
+#> 3 1 -0.800 -0.950 1.2266380 -0.2061132 0.4266380 -1.1561132 1
+#> 4 1 -1.750 -0.975 1.5461599 -0.9432939 -0.2038401 -1.9182939 1
+#> 5 1 -1.800 -1.300 -0.9309889 1.7478688 -2.7309889 0.4478688 2
+#> 6 1 -0.900 -0.350 1.3557092 1.1181441 0.4557092 0.7681441 2
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
-#> -920.0 -992.5 605.0
+#> 140 -1110 320
#> initial value 998.131940
-#> iter 2 value 994.367289
-#> iter 3 value 965.312071
-#> iter 4 value 964.856191
-#> iter 5 value 960.956078
-#> iter 6 value 960.938761
-#> iter 6 value 960.938748
-#> iter 6 value 960.938748
-#> final value 960.938748
+#> iter 2 value 987.542841
+#> iter 3 value 976.359534
+#> iter 4 value 976.315710
+#> iter 5 value 971.176423
+#> iter 6 value 971.173751
+#> iter 6 value 971.173748
+#> iter 6 value 971.173748
+#> final value 971.173748
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
@@ -222,30 +223,29 @@ sedrive <- sim_all()
#> 4 1 32 40 20.0 2.5 80 2.5 0 1
#> 5 1 39 40 20.0 0.0 80 10.0 10 1
#> 6 1 48 60 5.0 2.5 20 5.0 10 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.800 -0.5753867 1.1246768 -1.3503867 -0.6753232 2
-#> 2 1 -0.275 -0.775 0.9246181 -0.7283475 0.6496181 -1.5033475 1
-#> 3 1 -0.800 -0.950 1.6224900 -0.8607449 0.8224900 -1.8107449 1
-#> 4 1 -1.750 -0.975 -0.6271373 -0.9882054 -2.3771373 -1.9632054 2
-#> 5 1 -1.800 -1.300 2.4317376 0.4448995 0.6317376 -0.8551005 1
-#> 6 1 -0.900 -0.350 0.7924767 -0.6407351 -0.1075233 -0.9907351 1
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.800 0.93025769 2.47570731 0.1552577 0.6757073 2
+#> 2 1 -0.275 -0.775 1.60707885 -0.35547058 1.3320789 -1.1304706 1
+#> 3 1 -0.800 -0.950 1.27471866 -0.07559595 0.4747187 -1.0255960 1
+#> 4 1 -1.750 -0.975 0.39775368 -0.33144802 -1.3522463 -1.3064480 2
+#> 5 1 -1.800 -1.300 1.28873901 1.16104216 -0.5112610 -0.1389578 2
+#> 6 1 -0.900 -0.350 0.05237432 0.77241297 -0.8476257 0.4224130 2
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
-#> -1740 -650 400
+#> -520 -925 320
#> initial value 998.131940
-#> iter 2 value 992.981264
-#> iter 3 value 992.638207
-#> iter 4 value 992.633735
-#> iter 5 value 973.931212
-#> iter 6 value 972.829429
-#> iter 7 value 972.824075
-#> iter 7 value 972.824073
-#> iter 7 value 972.824073
-#> final value 972.824073
+#> iter 2 value 989.267873
+#> iter 3 value 979.462597
+#> iter 4 value 979.399625
+#> iter 5 value 974.067680
+#> iter 6 value 974.065735
+#> iter 6 value 974.065733
+#> iter 6 value 974.065733
+#> final value 974.065733
#> converged
#>
#>
@@ -253,15 +253,15 @@ sedrive <- sim_all()
#> \ vars n mean sd min max range se
#> ================ ==== === ===== ==== ===== ===== ===== ====
#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
-#> est_blade 2 2 -0.04 0.00 -0.05 -0.04 0.00 0.00
-#> est_bwarte 3 2 0.02 0.02 0.01 0.04 0.02 0.01
+#> est_blade 2 2 -0.05 0.00 -0.05 -0.04 0.00 0.00
+#> est_bwarte 3 2 0.01 0.00 0.01 0.01 0.00 0.00
#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
-#> rob_pval0_bwarte 6 2 0.12 0.16 0.00 0.23 0.23 0.11
+#> rob_pval0_bwarte 6 2 0.34 0.02 0.33 0.36 0.03 0.01
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
-#> FALSE TRUE
-#> 50 50
+#> FALSE
+#> 100
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
@@ -286,10 +286,6 @@ sedrive <- sim_all()
#> New names:
#> • `Choice situation` -> `Choice.situation`
#> • `` -> `...10`
-#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
-#> e.g.:
-#> dat <- vroom(...)
-#> problems(dat)
#>
#> does sou_gis exist: FALSE
#>
@@ -309,13 +305,13 @@ sedrive <- sim_all()
#> 4 1 25 60 5.0 10.0 20 20.0 5 1
#> 5 1 29 20 5.0 10.0 80 5.0 0 1
#> 6 1 32 40 10.0 2.5 80 2.5 5 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.400 0.4766761 0.2671861 -0.29832392 -1.1328139 1
-#> 2 1 -0.800 -0.750 1.5938385 3.8773811 0.79383850 3.1273811 2
-#> 3 1 -1.600 -1.300 1.3023142 0.4975025 -0.29768579 -0.8024975 1
-#> 4 1 -0.750 -1.500 0.6538038 1.3130123 -0.09619617 -0.1869877 1
-#> 5 1 -0.350 -1.150 1.1097526 -0.4923369 0.75975258 -1.6423369 1
-#> 6 1 -1.050 -0.875 -0.3331011 -0.3042609 -1.38310109 -1.1792609 2
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.400 0.5000790 -1.2708067 -0.2749210 -2.6708067 1
+#> 2 1 -0.800 -0.750 -1.9947176 -0.6174753 -2.7947176 -1.3674753 2
+#> 3 1 -1.600 -1.300 0.6003003 1.1010281 -0.9996997 -0.1989719 2
+#> 4 1 -0.750 -1.500 -1.2502306 2.4331480 -2.0002306 0.9331480 2
+#> 5 1 -0.350 -1.150 0.1058614 1.8360816 -0.2441386 0.6860816 2
+#> 6 1 -1.050 -0.875 1.8917336 0.7028783 0.8417336 -0.1721217 1
#>
#>
#> This is Run number 1
@@ -337,30 +333,29 @@ sedrive <- sim_all()
#> 4 1 25 60 5.0 10.0 20 20.0 5 1
#> 5 1 29 20 5.0 10.0 80 5.0 0 1
#> 6 1 32 40 10.0 2.5 80 2.5 5 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.400 -0.1853781 5.5643363 -0.9603781 4.1643363 2
-#> 2 1 -0.800 -0.750 0.4194371 -0.8106381 -0.3805629 -1.5606381 1
-#> 3 1 -1.600 -1.300 0.3558603 0.6710868 -1.2441397 -0.6289132 2
-#> 4 1 -0.750 -1.500 1.6643355 0.1201793 0.9143355 -1.3798207 1
-#> 5 1 -0.350 -1.150 -1.0250955 1.4973893 -1.3750955 0.3473893 2
-#> 6 1 -1.050 -0.875 0.5258572 1.4751937 -0.5241428 0.6001937 2
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.400 2.6464470 1.8930848 1.8714470 0.4930848 1
+#> 2 1 -0.800 -0.750 0.6943881 -0.0951414 -0.1056119 -0.8451414 1
+#> 3 1 -1.600 -1.300 3.0441699 2.6667389 1.4441699 1.3667389 1
+#> 4 1 -0.750 -1.500 1.1984493 1.9151346 0.4484493 0.4151346 1
+#> 5 1 -0.350 -1.150 3.5252196 -0.8557313 3.1752196 -2.0057313 1
+#> 6 1 -1.050 -0.875 0.5099513 -0.4707311 -0.5400487 -1.3457311 1
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
-#> -2160.0 -795.0 397.5
+#> -1440.0 -1057.5 510.0
#> initial value 998.131940
-#> iter 2 value 990.951216
-#> iter 3 value 973.839132
-#> iter 4 value 973.439833
-#> iter 5 value 965.450645
-#> iter 6 value 965.442009
-#> iter 7 value 965.441990
-#> iter 7 value 965.441990
-#> iter 7 value 965.441990
-#> final value 965.441990
+#> iter 2 value 992.072549
+#> iter 3 value 964.484472
+#> iter 4 value 964.438157
+#> iter 5 value 960.231915
+#> iter 6 value 960.220989
+#> iter 6 value 960.220975
+#> iter 6 value 960.220975
+#> final value 960.220975
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
@@ -381,30 +376,30 @@ sedrive <- sim_all()
#> 4 1 25 60 5.0 10.0 20 20.0 5 1
#> 5 1 29 20 5.0 10.0 80 5.0 0 1
#> 6 1 32 40 10.0 2.5 80 2.5 5 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.400 0.649258598 -0.6307138 -0.1257414 -2.0307138 1
-#> 2 1 -0.800 -0.750 0.223937357 3.6350297 -0.5760626 2.8850297 2
-#> 3 1 -1.600 -1.300 0.725491030 -0.9744697 -0.8745090 -2.2744697 1
-#> 4 1 -0.750 -1.500 -0.003168789 -1.2301114 -0.7531688 -2.7301114 1
-#> 5 1 -0.350 -1.150 2.699417707 -1.0402518 2.3494177 -2.1902518 1
-#> 6 1 -1.050 -0.875 -0.294873234 0.5681585 -1.3448732 -0.3068415 2
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.400 -0.8673571 0.5735753 -1.6423571 -0.8264247 2
+#> 2 1 -0.800 -0.750 0.1338568 2.1243864 -0.6661432 1.3743864 2
+#> 3 1 -1.600 -1.300 1.3074577 -0.1769248 -0.2925423 -1.4769248 1
+#> 4 1 -0.750 -1.500 1.7452195 -0.7334989 0.9952195 -2.2334989 1
+#> 5 1 -0.350 -1.150 3.2417667 0.8099365 2.8917667 -0.3400635 1
+#> 6 1 -1.050 -0.875 -0.8125169 -0.6517018 -1.8625169 -1.5267018 2
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
-#> bpreis blade bwarte
-#> -780 -1135 595
+#> bpreis blade bwarte
+#> -640.0 -1295.0 362.5
#> initial value 998.131940
-#> iter 2 value 987.994441
-#> iter 3 value 971.376969
-#> iter 4 value 971.238977
-#> iter 5 value 961.110685
-#> iter 6 value 961.070507
-#> iter 7 value 961.070355
-#> iter 7 value 961.070355
-#> iter 7 value 961.070355
-#> final value 961.070355
+#> iter 2 value 981.273463
+#> iter 3 value 964.055548
+#> iter 4 value 963.472083
+#> iter 5 value 957.462611
+#> iter 6 value 957.449595
+#> iter 7 value 957.449577
+#> iter 7 value 957.449577
+#> iter 7 value 957.449577
+#> final value 957.449577
#> converged
#>
#>
@@ -412,15 +407,15 @@ sedrive <- sim_all()
#> \ vars n mean sd min max range se
#> ================ ==== === ===== ==== ===== ===== ===== ====
#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
-#> est_blade 2 2 -0.05 0.00 -0.05 -0.05 0.00 0.00
-#> est_bwarte 3 2 0.01 0.02 0.00 0.03 0.03 0.01
+#> est_blade 2 2 -0.05 0.00 -0.06 -0.05 0.01 0.00
+#> est_bwarte 3 2 0.01 0.01 0.00 0.01 0.01 0.01
#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
-#> rob_pval0_bwarte 6 2 0.44 0.60 0.01 0.86 0.85 0.42
+#> rob_pval0_bwarte 6 2 0.53 0.52 0.16 0.90 0.74 0.37
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
-#> FALSE TRUE
-#> 50 50
+#> FALSE
+#> 100
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
@@ -445,10 +440,6 @@ sedrive <- sim_all()
#> New names:
#> • `Choice situation` -> `Choice.situation`
#> • `` -> `...10`
-#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
-#> e.g.:
-#> dat <- vroom(...)
-#> problems(dat)
#>
#> does sou_gis exist: FALSE
#>
@@ -468,13 +459,13 @@ sedrive <- sim_all()
#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1
#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1
#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -1.150 -0.350 -0.6442381 0.4295041 -1.7942381 0.07950409 2
-#> 2 1 -0.675 -1.500 3.1970276 -0.2347316 2.5220276 -1.73473164 1
-#> 3 1 -0.925 -1.600 0.3004974 -0.3128014 -0.6245026 -1.91280136 1
-#> 4 1 -0.875 -0.850 0.3732139 -0.4194695 -0.5017861 -1.26946953 1
-#> 5 1 -0.550 -0.900 -0.9300505 0.3951342 -1.4800505 -0.50486581 2
-#> 6 1 -1.550 -0.725 2.0446600 1.5811755 0.4946600 0.85617546 2
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -1.150 -0.350 1.018356215 -0.622267218 -0.13164379 -0.9722672 1
+#> 2 1 -0.675 -1.500 2.065375543 0.448415040 1.39037554 -1.0515850 1
+#> 3 1 -0.925 -1.600 0.068572712 0.001884789 -0.85642729 -1.5981152 1
+#> 4 1 -0.875 -0.850 4.451064209 -0.131594375 3.57606421 -0.9815944 1
+#> 5 1 -0.550 -0.900 0.001325549 1.769899979 -0.54867445 0.8699000 2
+#> 6 1 -1.550 -0.725 1.585052229 -0.559602808 0.03505223 -1.2846028 1
#>
#>
#> This is Run number 1
@@ -496,29 +487,29 @@ sedrive <- sim_all()
#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1
#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1
#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -1.150 -0.350 0.6290714 1.29877259 -0.5209286 0.9487726 2
-#> 2 1 -0.675 -1.500 1.8066986 -0.97425446 1.1316986 -2.4742545 1
-#> 3 1 -0.925 -1.600 2.6601535 1.12301507 1.7351535 -0.4769849 1
-#> 4 1 -0.875 -0.850 -1.0099599 1.88168762 -1.8849599 1.0316876 2
-#> 5 1 -0.550 -0.900 2.6583790 0.09147805 2.1083790 -0.8085220 1
-#> 6 1 -1.550 -0.725 -1.1958958 1.93592122 -2.7458958 1.2109212 2
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -1.150 -0.350 -0.08786308 -0.5863325 -1.23786308 -0.9363325 2
+#> 2 1 -0.675 -1.500 0.06248520 1.0111311 -0.61251480 -0.4888689 2
+#> 3 1 -0.925 -1.600 0.95443352 -0.3946771 0.02943352 -1.9946771 1
+#> 4 1 -0.875 -0.850 -0.76545318 1.6682085 -1.64045318 0.8182085 2
+#> 5 1 -0.550 -0.900 1.13173817 -0.3986287 0.58173817 -1.2986287 1
+#> 6 1 -1.550 -0.725 3.42387572 1.2824413 1.87387572 0.5574413 1
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
-#> -1620.0 -1040.0 382.5
+#> -960.0 -1017.5 245.0
#> initial value 998.131940
-#> iter 2 value 992.061567
-#> iter 3 value 967.996960
-#> iter 4 value 967.486565
-#> iter 5 value 962.185161
-#> iter 6 value 962.173351
-#> iter 6 value 962.173337
-#> iter 6 value 962.173337
-#> final value 962.173337
+#> iter 2 value 994.181427
+#> iter 3 value 972.722564
+#> iter 4 value 971.807620
+#> iter 5 value 969.894601
+#> iter 6 value 969.892112
+#> iter 6 value 969.892111
+#> iter 6 value 969.892111
+#> final value 969.892111
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
@@ -539,30 +530,30 @@ sedrive <- sim_all()
#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1
#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1
#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -1.150 -0.350 -0.7309834 1.1284057 -1.88098344 0.7784057 2
-#> 2 1 -0.675 -1.500 0.8179671 1.0964035 0.14296711 -0.4035965 1
-#> 3 1 -0.925 -1.600 1.3940762 1.1170727 0.46907619 -0.4829273 1
-#> 4 1 -0.875 -0.850 0.5730054 3.1034379 -0.30199458 2.2534379 2
-#> 5 1 -0.550 -0.900 1.9047401 4.1223683 1.35474011 3.2223683 2
-#> 6 1 -1.550 -0.725 1.6121097 -0.7004755 0.06210972 -1.4254755 1
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -1.150 -0.350 0.6747222 0.25151850 -0.4752778 -0.0984815 2
+#> 2 1 -0.675 -1.500 1.8163839 -0.09688587 1.1413839 -1.5968859 1
+#> 3 1 -0.925 -1.600 -0.8974158 3.69592175 -1.8224158 2.0959217 2
+#> 4 1 -0.875 -0.850 -0.5523539 3.18018561 -1.4273539 2.3301856 2
+#> 5 1 -0.550 -0.900 -1.0057814 0.20974090 -1.5557814 -0.6902591 2
+#> 6 1 -1.550 -0.725 0.9409876 0.52310305 -0.6090124 -0.2018970 2
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
-#> -1540.0 -942.5 492.5
+#> -2920.0 -597.5 467.5
#> initial value 998.131940
-#> iter 2 value 992.664599
-#> iter 3 value 971.477616
-#> iter 4 value 971.475065
-#> iter 5 value 967.041341
-#> iter 6 value 966.930735
-#> iter 7 value 966.930651
-#> iter 7 value 966.930651
-#> iter 7 value 966.930651
-#> final value 966.930651
+#> iter 2 value 988.559582
+#> iter 3 value 984.350429
+#> iter 4 value 984.238420
+#> iter 5 value 967.198497
+#> iter 6 value 967.168244
+#> iter 7 value 967.168103
+#> iter 7 value 967.168103
+#> iter 7 value 967.168103
+#> final value 967.168103
#> converged
#>
#>
@@ -570,11 +561,11 @@ sedrive <- sim_all()
#> \ vars n mean sd min max range se
#> ================ ==== === ===== ==== ===== ===== ===== ====
#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
-#> est_blade 2 2 -0.05 0.01 -0.05 -0.04 0.01 0.00
-#> est_bwarte 3 2 0.01 0.01 0.00 0.01 0.02 0.01
+#> est_blade 2 2 -0.04 0.01 -0.05 -0.04 0.01 0.00
+#> est_bwarte 3 2 0.00 0.01 -0.01 0.01 0.01 0.01
#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
-#> rob_pval0_bwarte 6 2 0.54 0.57 0.14 0.94 0.80 0.40
+#> rob_pval0_bwarte 6 2 0.52 0.11 0.44 0.59 0.15 0.07
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
@@ -603,10 +594,6 @@ sedrive <- sim_all()
#> New names:
#> • `Choice situation` -> `Choice.situation`
#> • `` -> `...10`
-#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
-#> e.g.:
-#> dat <- vroom(...)
-#> problems(dat)
#>
#> does sou_gis exist: FALSE
#>
@@ -626,13 +613,13 @@ sedrive <- sim_all()
#> 4 1 70 80 5.0 10 20 20.0 2.5 1
#> 5 1 71 60 20.0 10 80 10.0 0.0 1
#> 6 1 73 60 10.0 0 40 20.0 10.0 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -1.150 -1.800 0.2796585 0.34664924 -0.8703415 -1.4533508 1
-#> 2 1 -0.575 -1.800 0.8234627 -0.32814816 0.2484627 -2.1281482 1
-#> 3 1 -1.400 -0.975 -0.9750900 0.16466845 -2.3750900 -0.8103316 2
-#> 4 1 -0.950 -1.550 1.4931059 0.50414150 0.5431059 -1.0458585 1
-#> 5 1 -1.800 -1.500 -0.6878677 -0.07763469 -2.4878677 -1.5776347 2
-#> 6 1 -1.300 -1.600 5.5219839 0.72366180 4.2219839 -0.8763382 1
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -1.150 -1.800 0.458928485 2.0701754 -0.6910715 0.27017541 2
+#> 2 1 -0.575 -1.800 0.253655240 -0.6611997 -0.3213448 -2.46119970 1
+#> 3 1 -1.400 -0.975 -0.102031250 0.2036489 -1.5020312 -0.77135113 2
+#> 4 1 -0.950 -1.550 -0.559421410 0.8864091 -1.5094214 -0.66359093 2
+#> 5 1 -1.800 -1.500 5.674505169 1.5661040 3.8745052 0.06610405 1
+#> 6 1 -1.300 -1.600 -0.002309409 1.5711319 -1.3023094 -0.02886812 2
#>
#>
#> This is Run number 1
@@ -654,30 +641,30 @@ sedrive <- sim_all()
#> 4 1 70 80 5.0 10 20 20.0 2.5 1
#> 5 1 71 60 20.0 10 80 10.0 0.0 1
#> 6 1 73 60 10.0 0 40 20.0 10.0 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -1.150 -1.800 -0.7572712 3.5425547 -1.9072712 1.7425547 2
-#> 2 1 -0.575 -1.800 -0.4468573 1.9263080 -1.0218573 0.1263080 2
-#> 3 1 -1.400 -0.975 -0.6624036 -0.4899357 -2.0624036 -1.4649357 2
-#> 4 1 -0.950 -1.550 -0.3297019 0.8584054 -1.2797019 -0.6915946 2
-#> 5 1 -1.800 -1.500 3.2012567 0.2906511 1.4012567 -1.2093489 1
-#> 6 1 -1.300 -1.600 1.6578812 1.1758015 0.3578812 -0.4241985 1
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -1.150 -1.800 -0.01463130 -0.22509832 -1.164631 -2.0250983 1
+#> 2 1 -0.575 -1.800 2.36022782 -0.36376052 1.785228 -2.1637605 1
+#> 3 1 -1.400 -0.975 1.59659499 -0.13121663 0.196595 -1.1062166 1
+#> 4 1 -0.950 -1.550 -1.33824416 1.40724463 -2.288244 -0.1427554 2
+#> 5 1 -1.800 -1.500 0.49308644 1.66211472 -1.306914 0.1621147 2
+#> 6 1 -1.300 -1.600 0.04407743 0.04227857 -1.255923 -1.5577214 1
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
-#> -3380.0 -2975.0 927.5
+#> -2640.0 -3282.5 1152.5
#> initial value 998.131940
-#> iter 2 value 965.332966
-#> iter 3 value 954.229410
-#> iter 4 value 953.971301
-#> iter 5 value 914.603698
-#> iter 6 value 914.486744
-#> iter 7 value 914.486127
-#> iter 7 value 914.486126
-#> iter 7 value 914.486126
-#> final value 914.486126
+#> iter 2 value 963.302525
+#> iter 3 value 925.984100
+#> iter 4 value 925.959587
+#> iter 5 value 905.721674
+#> iter 6 value 905.488066
+#> iter 7 value 905.484178
+#> iter 7 value 905.484176
+#> iter 7 value 905.484176
+#> final value 905.484176
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
@@ -698,30 +685,30 @@ sedrive <- sim_all()
#> 4 1 70 80 5.0 10 20 20.0 2.5 1
#> 5 1 71 60 20.0 10 80 10.0 0.0 1
#> 6 1 73 60 10.0 0 40 20.0 10.0 1
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -1.150 -1.800 1.1341457 -0.26483742 -0.01585434 -2.064837 1
-#> 2 1 -0.575 -1.800 -0.7963652 0.04667859 -1.37136517 -1.753321 1
-#> 3 1 -1.400 -0.975 0.2048657 -0.21097918 -1.19513427 -1.185979 2
-#> 4 1 -0.950 -1.550 0.3900273 -0.41202049 -0.55997270 -1.962020 1
-#> 5 1 -1.800 -1.500 1.2810776 -1.50479969 -0.51892242 -3.004800 1
-#> 6 1 -1.300 -1.600 0.1633678 2.89194036 -1.13663222 1.291940 2
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -1.150 -1.800 3.0041977 0.38322958 1.8541977 -1.4167704 1
+#> 2 1 -0.575 -1.800 3.0101002 0.72197923 2.4351002 -1.0780208 1
+#> 3 1 -1.400 -0.975 1.1260977 0.06998784 -0.2739023 -0.9050122 1
+#> 4 1 -0.950 -1.550 1.9511131 0.39768983 1.0011131 -1.1523102 1
+#> 5 1 -1.800 -1.500 -0.4686622 -0.83175553 -2.2686622 -2.3317555 1
+#> 6 1 -1.300 -1.600 1.5102954 0.98561984 0.2102954 -0.6143802 1
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
-#> -3440.0 -2690.0 1097.5
+#> -2840.0 -2792.5 980.0
#> initial value 998.131940
-#> iter 2 value 968.342074
-#> iter 3 value 949.110332
-#> iter 4 value 948.984724
-#> iter 5 value 924.015573
-#> iter 6 value 923.964006
-#> iter 7 value 923.963865
-#> iter 7 value 923.963864
-#> iter 7 value 923.963864
-#> final value 923.963864
+#> iter 2 value 970.451816
+#> iter 3 value 943.596118
+#> iter 4 value 943.593445
+#> iter 5 value 937.085850
+#> iter 6 value 927.191585
+#> iter 7 value 927.129298
+#> iter 8 value 927.128981
+#> iter 8 value 927.128980
+#> final value 927.128980
#> converged
#>
#>
@@ -729,11 +716,11 @@ sedrive <- sim_all()
#> \ vars n mean sd min max range se
#> ================ ==== === ===== ==== ===== ===== ===== ====
#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
-#> est_blade 2 2 -0.04 0.00 -0.04 -0.04 0.01 0.00
-#> est_bwarte 3 2 0.01 0.01 0.01 0.02 0.01 0.01
+#> est_blade 2 2 -0.04 0.00 -0.05 -0.04 0.01 0.00
+#> est_bwarte 3 2 0.01 0.00 0.01 0.02 0.00 0.00
#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
-#> rob_pval0_bwarte 6 2 0.22 0.29 0.01 0.42 0.41 0.20
+#> rob_pval0_bwarte 6 2 0.05 0.06 0.01 0.09 0.08 0.04
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE TRUE
@@ -780,13 +767,13 @@ sedrive <- sim_all()
#> 4 1 4 1 20 80 20.0 2.5 0 10
#> 5 1 5 1 40 80 10.0 5.0 10 5
#> 6 1 6 1 60 80 5.0 2.5 0 0
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.500 2.0013484 1.2686068 1.2263484 -0.2313932 1
-#> 2 1 -0.850 -0.900 -0.2160302 -0.1560303 -1.0660302 -1.0560303 2
-#> 3 1 -2.000 -1.400 0.3647555 1.7510865 -1.6352445 0.3510865 2
-#> 4 1 -1.600 -0.775 -0.7047881 -0.1607646 -2.3047881 -0.9357646 2
-#> 5 1 -0.900 -1.050 -0.2254104 -1.0337366 -1.1254104 -2.0837366 1
-#> 6 1 -0.950 -0.975 0.2606067 -0.2131090 -0.6893933 -1.1881090 1
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.500 0.1987193 -1.0900058 -0.5762807 -2.590005753 1
+#> 2 1 -0.850 -0.900 0.2937177 1.3988380 -0.5562823 0.498837973 2
+#> 3 1 -2.000 -1.400 1.2142542 0.5244772 -0.7857458 -0.875522760 1
+#> 4 1 -1.600 -0.775 2.2587676 0.2695545 0.6587676 -0.505445461 1
+#> 5 1 -0.900 -1.050 0.7958478 1.0485339 -0.1041522 -0.001466141 2
+#> 6 1 -0.950 -0.975 0.3019734 -0.4699530 -0.6480266 -1.444953045 1
#>
#>
#> This is Run number 1
@@ -808,29 +795,29 @@ sedrive <- sim_all()
#> 4 1 4 1 20 80 20.0 2.5 0 10
#> 5 1 5 1 40 80 10.0 5.0 10 5
#> 6 1 6 1 60 80 5.0 2.5 0 0
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.500 -0.1653281 1.5240819 -0.9403281 0.02408193 2
-#> 2 1 -0.850 -0.900 1.7604798 -1.0703138 0.9104798 -1.97031379 1
-#> 3 1 -2.000 -1.400 -0.2342433 -0.2639953 -2.2342433 -1.66399530 2
-#> 4 1 -1.600 -0.775 2.8543824 -0.4878686 1.2543824 -1.26286864 1
-#> 5 1 -0.900 -1.050 0.1456366 1.8645970 -0.7543634 0.81459700 2
-#> 6 1 -0.950 -0.975 2.0912305 -1.0990719 1.1412305 -2.07407190 1
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.500 -0.3472514 2.3168155 -1.1222514 0.8168155 2
+#> 2 1 -0.850 -0.900 0.2943507 -1.7082525 -0.5556493 -2.6082525 1
+#> 3 1 -2.000 -1.400 0.4742717 1.1619537 -1.5257283 -0.2380463 2
+#> 4 1 -1.600 -0.775 0.5628095 2.7090502 -1.0371905 1.9340502 2
+#> 5 1 -0.900 -1.050 -0.2128470 -1.3149155 -1.1128470 -2.3649155 1
+#> 6 1 -0.950 -0.975 1.6192730 0.3695527 0.6692730 -0.6054473 1
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
#> bpreis blade bwarte
-#> 720.0 -947.5 160.0
+#> -620.0 -877.5 407.5
#> initial value 998.131940
-#> iter 2 value 995.788784
-#> iter 3 value 986.036982
-#> iter 4 value 985.747072
-#> iter 5 value 981.687192
-#> iter 6 value 981.685153
-#> iter 6 value 981.685152
-#> iter 6 value 981.685152
-#> final value 981.685152
+#> iter 2 value 991.196029
+#> iter 3 value 975.595269
+#> iter 4 value 975.571052
+#> iter 5 value 971.582698
+#> iter 6 value 971.577726
+#> iter 6 value 971.577720
+#> iter 6 value 971.577720
+#> final value 971.577720
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
@@ -851,30 +838,29 @@ sedrive <- sim_all()
#> 4 1 4 1 20 80 20.0 2.5 0 10
#> 5 1 5 1 40 80 10.0 5.0 10 5
#> 6 1 6 1 60 80 5.0 2.5 0 0
-#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
-#> 1 1 -0.775 -1.500 3.8883463 0.05263711 3.1133463 -1.4473629 1
-#> 2 1 -0.850 -0.900 5.5864242 0.57285677 4.7364242 -0.3271432 1
-#> 3 1 -2.000 -1.400 -0.3144368 0.03415373 -2.3144368 -1.3658463 2
-#> 4 1 -1.600 -0.775 0.3462736 2.49581876 -1.2537264 1.7208188 2
-#> 5 1 -0.900 -1.050 0.1750145 0.10667154 -0.7249855 -0.9433285 1
-#> 6 1 -0.950 -0.975 0.4323006 -0.48374785 -0.5176994 -1.4587478 1
+#> group V_1 V_2 e_1 e_2 U_1 U_2 CHOICE
+#> 1 1 -0.775 -1.500 0.5287428 1.2994989 -0.2462572 -0.2005011 2
+#> 2 1 -0.850 -0.900 3.2064378 0.4735037 2.3564378 -0.4264963 1
+#> 3 1 -2.000 -1.400 1.2595242 0.1146570 -0.7404758 -1.2853430 1
+#> 4 1 -1.600 -0.775 4.2748306 -0.7146858 2.6748306 -1.4896858 1
+#> 5 1 -0.900 -1.050 0.1088246 2.2911218 -0.7911754 1.2411218 2
+#> 6 1 -0.950 -0.975 1.1654639 1.3627596 0.2154639 0.3877596 2
#>
#>
#> This is the utility functions
#> U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;Initial function value: -998.1319
#> Initial gradient value:
-#> bpreis blade bwarte
-#> -520 -870 380
+#> bpreis blade bwarte
+#> -580.0 -1077.5 495.0
#> initial value 998.131940
-#> iter 2 value 989.856026
-#> iter 3 value 986.668308
-#> iter 4 value 986.626161
-#> iter 5 value 973.334324
-#> iter 6 value 973.329236
-#> iter 7 value 973.329219
-#> iter 7 value 973.329219
-#> iter 7 value 973.329219
-#> final value 973.329219
+#> iter 2 value 984.134259
+#> iter 3 value 967.898748
+#> iter 4 value 967.884812
+#> iter 5 value 960.128712
+#> iter 6 value 960.126933
+#> iter 6 value 960.126928
+#> iter 6 value 960.126928
+#> final value 960.126928
#> converged
#>
#>
@@ -882,29 +868,29 @@ sedrive <- sim_all()
#> \ vars n mean sd min max range se
#> ================ ==== === ===== ==== ===== ===== ===== ====
#> est_bpreis 1 2 -0.01 0.00 -0.01 -0.01 0.00 0.00
-#> est_blade 2 2 -0.04 0.00 -0.05 -0.04 0.01 0.00
-#> est_bwarte 3 2 0.01 0.01 0.00 0.01 0.01 0.01
+#> est_blade 2 2 -0.05 0.01 -0.06 -0.05 0.01 0.00
+#> est_bwarte 3 2 0.02 0.00 0.02 0.02 0.01 0.00
#> rob_pval0_bpreis 4 2 0.00 0.00 0.00 0.00 0.00 0.00
#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
-#> rob_pval0_bwarte 6 2 0.60 0.56 0.20 0.99 0.79 0.39
+#> rob_pval0_bwarte 6 2 0.11 0.04 0.08 0.13 0.05 0.03
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
#> 100
-#> 33.325 sec elapsed
+#> 32.978 sec elapsed
#> $tic
#> elapsed
-#> 0.813
+#> 0.8
#>
#> $toc
#> elapsed
-#> 34.138
+#> 33.778
#>
#> $msg
#> logical(0)
#>
#> $callback_msg
-#> [1] "33.325 sec elapsed"
+#> [1] "32.978 sec elapsed"
```
<img src="man/figures/README-example-1.png" width="100%" /><img src="man/figures/README-example-2.png" width="100%" /><img src="man/figures/README-example-3.png" width="100%" />
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diff --git a/man/figures/README-example-2.png b/man/figures/README-example-2.png
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diff --git a/man/figures/README-example-3.png b/man/figures/README-example-3.png
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