Skip to content
Snippets Groups Projects
Commit 2fbe5898 authored by samuelsmock's avatar samuelsmock
Browse files

proper README markdown

parent 7e3c489c
Branches
Tags
No related merge requests found
......@@ -19,7 +19,7 @@ knitr::opts_chunk$set(
<!-- badges: end -->
The goal of simulateDCE is to make it easy to simulate choice experiment datasets using designs from NGENE or `spdesign`. You have to store the design file in a subfolder and need to specify certain parameters and the utility functions for the data generating process. The package is useful for
The goal of simulateDCE is to make it easy to simulate choice experiment datasets using designs from NGENE or `spdesign`. You have to store the design file in a subfolder and need to specify certain parameters and the utility functions for the data generating process. The package is useful for:
1. Test different designs in terms of statistical power, efficiency and unbiasedness
......@@ -66,16 +66,17 @@ nosim= 2 # number of simulations to run (about 500 is minimum)
# bpreis = -0.036
# blade = -0.034
# bwarte = -0.049
# bcoeff <- list(
# bpreis = -0.036,
# blade = -0.034,
# bwarte = -0.049)
decisiongroups=c(0,0.7,1)
# wrong parameters
#
# place b coefficients into an r list:
bcoeff = list(
bpreis = -0.01,
blade = -0.07,
......
README.html 0 → 100644
Source diff could not be displayed: it is too large. Options to address this: view the blob.
......@@ -10,7 +10,7 @@ The goal of simulateDCE is to make it easy to simulate choice experiment
datasets using designs from NGENE or `spdesign`. You have to store the
design file in a subfolder and need to specify certain parameters and
the utility functions for the data generating process. The package is
useful for
useful for:
1. Test different designs in terms of statistical power, efficiency and
unbiasedness
......@@ -36,6 +36,7 @@ need to install the `remotes` package first. The current version is
alpha and there is no version on cran:
``` r
install.packages("remotes")
remotes::install_gitlab(repo = "dj44vuri/simulateDCE" , host = "https://git.idiv.de")
```
......@@ -46,15 +47,11 @@ This is a basic example for a simulation:
``` r
rm(list=ls())
library(simulateDCE)
library(rlang)
library(formula.tools)
#>
#> Attaching package: 'formula.tools'
#> The following object is masked from 'package:rlang':
#>
#> env
library(rlang)
......@@ -69,19 +66,21 @@ nosim= 2 # number of simulations to run (about 500 is minimum)
# bpreis = -0.036
# blade = -0.034
# bwarte = -0.049
# bcoeff <- list(
# bpreis = -0.036,
# blade = -0.034,
# bwarte = -0.049)
decisiongroups=c(0,0.7,1)
# wrong parameters
#
bpreis = -0.01
blade = -0.07
bwarte = 0.02
# place b coefficients into an r list:
bcoeff = list(
bpreis = -0.01,
blade = -0.07,
bwarte = 0.02)
manipulations = list(alt1.x2= expr(alt1.x2/10),
alt1.x3= expr(alt1.x3/10),
......@@ -108,7 +107,7 @@ ul<-list( u1 =
destype="ngene"
sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
designpath = designpath, u=ul)
designpath = designpath, u=ul, bcoeff = bcoeff)
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
......@@ -145,20 +144,20 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 7 80 2.5 10.0 60 20.0 10 1
#> 2 1 19 20 2.5 5.0 60 2.5 0 1
#> 3 1 30 20 10.0 5.0 80 5.0 10 1
#> 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 -0.28937157 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 7 80 2.5 10.0 60 20.0 10 1 1 -0.775 -1.800 -0.15343271 3.6612318 -0.9284327 1.8612318
#> 2 1 19 20 2.5 5.0 60 2.5 0 1 1 -0.275 -0.775 0.47936686 -0.8086989 0.2043669 -1.5836989
#> 3 1 30 20 10.0 5.0 80 5.0 10 1 1 -0.800 -0.950 0.12634298 1.2300690 -0.6736570 0.2800690
#> 4 1 32 40 20.0 2.5 80 2.5 0 1 1 -1.750 -0.975 2.08710825 -0.1882935 0.3371082 -1.1632935
#> 5 1 39 40 20.0 0.0 80 10.0 10 1 1 -1.800 -1.300 1.05540385 3.1339326 -0.7445961 1.8339326
#> 6 1 48 60 5.0 2.5 20 5.0 10 1 1 -0.900 -0.350 0.07255885 -0.1156742 -0.8274412 -0.4656742
#> CHOICE
#> 1 2
#> 2 1
#> 3 2
#> 4 1
#> 5 2
#> 6 2
#>
#>
#> This is Run number 1
......@@ -173,36 +172,36 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 7 80 2.5 10.0 60 20.0 10 1
#> 2 1 19 20 2.5 5.0 60 2.5 0 1
#> 3 1 30 20 10.0 5.0 80 5.0 10 1
#> 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 0.4008023 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 7 80 2.5 10.0 60 20.0 10 1 1 -0.775 -1.800 -1.2775839 1.5689946 -2.052584 -0.2310054
#> 2 1 19 20 2.5 5.0 60 2.5 0 1 1 -0.275 -0.775 0.5934850 0.5453996 0.318485 -0.2296004
#> 3 1 30 20 10.0 5.0 80 5.0 10 1 1 -0.800 -0.950 -0.5127855 1.7551185 -1.312785 0.8051185
#> 4 1 32 40 20.0 2.5 80 2.5 0 1 1 -1.750 -0.975 3.4643234 1.3685812 1.714323 0.3935812
#> 5 1 39 40 20.0 0.0 80 10.0 10 1 1 -1.800 -1.300 -0.5128262 0.3011019 -2.312826 -0.9988981
#> 6 1 48 60 5.0 2.5 20 5.0 10 1 1 -0.900 -0.350 4.3343477 1.2265189 3.434348 0.8765189
#> CHOICE
#> 1 2
#> 2 1
#> 3 2
#> 4 1
#> 5 2
#> 6 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
#> 140 -1110 320
#> bpreis blade bwarte
#> -1060.0 -812.5 520.0
#> initial value 998.131940
#> 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
#> iter 2 value 994.260781
#> iter 3 value 974.092906
#> iter 4 value 973.856287
#> iter 5 value 970.270450
#> iter 6 value 970.262794
#> iter 6 value 970.262788
#> iter 6 value 970.262788
#> final value 970.262788
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
......@@ -216,52 +215,52 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 7 80 2.5 10.0 60 20.0 10 1
#> 2 1 19 20 2.5 5.0 60 2.5 0 1
#> 3 1 30 20 10.0 5.0 80 5.0 10 1
#> 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 0.93025769 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 7 80 2.5 10.0 60 20.0 10 1 1 -0.775 -1.800 0.34553136 -0.8375727 -0.4294686 -2.6375727
#> 2 1 19 20 2.5 5.0 60 2.5 0 1 1 -0.275 -0.775 -1.32361481 0.3195766 -1.5986148 -0.4554234
#> 3 1 30 20 10.0 5.0 80 5.0 10 1 1 -0.800 -0.950 0.08515524 -0.6090259 -0.7148448 -1.5590259
#> 4 1 32 40 20.0 2.5 80 2.5 0 1 1 -1.750 -0.975 -0.18021132 2.3073397 -1.9302113 1.3323397
#> 5 1 39 40 20.0 0.0 80 10.0 10 1 1 -1.800 -1.300 -0.55591900 3.4630292 -2.3559190 2.1630292
#> 6 1 48 60 5.0 2.5 20 5.0 10 1 1 -0.900 -0.350 -0.29734711 3.0420404 -1.1973471 2.6920404
#> CHOICE
#> 1 1
#> 2 2
#> 3 1
#> 4 2
#> 5 2
#> 6 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 -925 320
#> bpreis blade bwarte
#> 440.0 -1087.5 447.5
#> initial value 998.131940
#> 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
#> iter 2 value 995.094499
#> iter 3 value 974.488144
#> iter 4 value 974.227531
#> iter 5 value 971.482008
#> iter 6 value 971.477251
#> iter 6 value 971.477249
#> iter 6 value 971.477249
#> final value 971.477249
#> converged
#>
#>
#> ================ ==== === ===== ==== ===== ===== ===== ====
#> \ 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 -0.04 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
#> est_bpreis 1 2 -0.01 0.00 -0.01 0.00 0.00 0.00
#> est_blade 2 2 -0.04 0.00 -0.04 -0.04 0.00 0.00
#> est_bwarte 3 2 0.03 0.00 0.03 0.03 0.00 0.00
#> rob_pval0_bpreis 4 2 0.01 0.01 0.00 0.02 0.02 0.01
#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
#> rob_pval0_bwarte 6 2 0.34 0.02 0.33 0.36 0.03 0.01
#> rob_pval0_bwarte 6 2 0.01 0.00 0.01 0.01 0.00 0.00
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
#> 100
#> TRUE
#> 100
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
......@@ -298,20 +297,20 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 12 60 2.5 0.0 20 20.0 10 1
#> 2 1 16 20 10.0 5.0 40 5.0 0 1
#> 3 1 17 20 20.0 0.0 80 10.0 10 1
#> 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 0.5000790 -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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 12 60 2.5 0.0 20 20.0 10 1 1 -0.775 -1.400 0.63130736 -1.4347994 -0.1436926 -2.8347994
#> 2 1 16 20 10.0 5.0 40 5.0 0 1 1 -0.800 -0.750 5.09739937 0.4118885 4.2973994 -0.3381115
#> 3 1 17 20 20.0 0.0 80 10.0 10 1 1 -1.600 -1.300 0.22397799 0.4666321 -1.3760220 -0.8333679
#> 4 1 25 60 5.0 10.0 20 20.0 5 1 1 -0.750 -1.500 -0.05146482 2.2007592 -0.8014648 0.7007592
#> 5 1 29 20 5.0 10.0 80 5.0 0 1 1 -0.350 -1.150 1.57620781 4.9154679 1.2262078 3.7654679
#> 6 1 32 40 10.0 2.5 80 2.5 5 1 1 -1.050 -0.875 -0.47930823 0.7058788 -1.5293082 -0.1691212
#> CHOICE
#> 1 1
#> 2 1
#> 3 2
#> 4 2
#> 5 2
#> 6 2
#>
#>
#> This is Run number 1
......@@ -326,36 +325,37 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 12 60 2.5 0.0 20 20.0 10 1
#> 2 1 16 20 10.0 5.0 40 5.0 0 1
#> 3 1 17 20 20.0 0.0 80 10.0 10 1
#> 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 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 12 60 2.5 0.0 20 20.0 10 1 1 -0.775 -1.400 0.3494617 0.1551114 -0.42553827 -1.24488857
#> 2 1 16 20 10.0 5.0 40 5.0 0 1 1 -0.800 -0.750 1.5845207 0.5556039 0.78452066 -0.19439613
#> 3 1 17 20 20.0 0.0 80 10.0 10 1 1 -1.600 -1.300 4.1993459 -0.1612424 2.59934589 -1.46124241
#> 4 1 25 60 5.0 10.0 20 20.0 5 1 1 -0.750 -1.500 0.6527215 1.3949219 -0.09727852 -0.10507806
#> 5 1 29 20 5.0 10.0 80 5.0 0 1 1 -0.350 -1.150 2.6927356 -1.3232777 2.34273564 -2.47327770
#> 6 1 32 40 10.0 2.5 80 2.5 5 1 1 -1.050 -0.875 0.3758168 0.8556930 -0.67418318 -0.01930696
#> CHOICE
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 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
#> -1440.0 -1057.5 510.0
#> -2340.0 -857.5 510.0
#> initial value 998.131940
#> 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
#> iter 2 value 989.566757
#> iter 3 value 968.906950
#> iter 4 value 968.775516
#> iter 5 value 959.377427
#> iter 6 value 959.364632
#> iter 7 value 959.364588
#> iter 7 value 959.364588
#> iter 7 value 959.364588
#> final value 959.364588
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
......@@ -369,37 +369,37 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 12 60 2.5 0.0 20 20.0 10 1
#> 2 1 16 20 10.0 5.0 40 5.0 0 1
#> 3 1 17 20 20.0 0.0 80 10.0 10 1
#> 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 -0.8673571 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1
#> 1 1 12 60 2.5 0.0 20 20.0 10 1 1 -0.775 -1.400 -0.5067469 -4.908678e-05 -1.2817469
#> 2 1 16 20 10.0 5.0 40 5.0 0 1 1 -0.800 -0.750 2.1209149 5.835693e-01 1.3209149
#> 3 1 17 20 20.0 0.0 80 10.0 10 1 1 -1.600 -1.300 -0.3310010 7.106139e-01 -1.9310010
#> 4 1 25 60 5.0 10.0 20 20.0 5 1 1 -0.750 -1.500 1.0469501 3.053872e-01 0.2969501
#> 5 1 29 20 5.0 10.0 80 5.0 0 1 1 -0.350 -1.150 1.2182730 -6.215119e-01 0.8682730
#> 6 1 32 40 10.0 2.5 80 2.5 5 1 1 -1.050 -0.875 -0.3318808 5.251218e+00 -1.3818808
#> U_2 CHOICE
#> 1 -1.4000491 1
#> 2 -0.1664307 1
#> 3 -0.5893861 2
#> 4 -1.1946128 1
#> 5 -1.7715119 1
#> 6 4.3762180 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
#> -640.0 -1295.0 362.5
#> -2120.0 -842.5 582.5
#> initial value 998.131940
#> 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
#> iter 2 value 990.498814
#> iter 3 value 970.036278
#> iter 4 value 970.031934
#> iter 5 value 961.943463
#> iter 6 value 961.698866
#> iter 7 value 961.698562
#> iter 7 value 961.698561
#> iter 7 value 961.698561
#> final value 961.698561
#> converged
#>
#>
......@@ -407,15 +407,15 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> \ 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 -0.06 -0.05 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 0.00 -0.05 -0.05 0.00 0.00
#> est_bwarte 3 2 0.02 0.01 0.01 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 0.53 0.52 0.16 0.90 0.74 0.37
#> rob_pval0_bwarte 6 2 0.16 0.19 0.03 0.30 0.27 0.13
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
#> 100
#> FALSE TRUE
#> 50 50
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
......@@ -452,20 +452,20 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1
#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1
#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1
#> 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 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1
#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1 1 -1.150 -0.350 1.88045081 0.33059180 0.7304508
#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1 1 -0.675 -1.500 -0.08733163 -0.07195918 -0.7623316
#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1 1 -0.925 -1.600 -0.31269859 3.95512677 -1.2376986
#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1 1 -0.875 -0.850 0.20206751 -0.87018279 -0.6729325
#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1 1 -0.550 -0.900 -0.25607132 -1.21928402 -0.8060713
#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1 1 -1.550 -0.725 1.39833272 -0.08165078 -0.1516673
#> U_2 CHOICE
#> 1 -0.0194082 1
#> 2 -1.5719592 1
#> 3 2.3551268 2
#> 4 -1.7201828 1
#> 5 -2.1192840 1
#> 6 -0.8066508 1
#>
#>
#> This is Run number 1
......@@ -480,36 +480,37 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1
#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1
#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1
#> 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 -0.08786308 -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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1
#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1 1 -1.150 -0.350 -0.0940785 -0.8728874 -1.24407850
#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1 1 -0.675 -1.500 -0.6796651 -1.1297414 -1.35466507
#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1 1 -0.925 -1.600 1.7899847 0.6372528 0.86498471
#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1 1 -0.875 -0.850 0.9429192 0.7744473 0.06791921
#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1 1 -0.550 -0.900 0.1003092 2.5583115 -0.44969083
#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1 1 -1.550 -0.725 -0.3851894 0.9776369 -1.93518939
#> U_2 CHOICE
#> 1 -1.22288745 2
#> 2 -2.62974141 1
#> 3 -0.96274717 1
#> 4 -0.07555271 1
#> 5 1.65831145 2
#> 6 0.25263691 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
#> -960.0 -1017.5 245.0
#> -2300.0 -967.5 417.5
#> initial value 998.131940
#> 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
#> iter 2 value 989.783897
#> iter 3 value 967.441065
#> iter 4 value 966.807343
#> iter 5 value 957.535574
#> iter 6 value 957.518843
#> iter 7 value 957.518805
#> iter 7 value 957.518805
#> iter 7 value 957.518805
#> final value 957.518805
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
......@@ -523,37 +524,36 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1
#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1
#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1
#> 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 0.6747222 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1
#> 1 1 3 80 5.0 0.0 20 5.0 10.0 1 1 -1.150 -0.350 0.39615659 0.74248610 -0.7538434
#> 2 1 5 60 2.5 5.0 20 20.0 5.0 1 1 -0.675 -1.500 -0.17578286 0.04260786 -0.8507829
#> 3 1 10 80 2.5 2.5 20 20.0 0.0 1 1 -0.925 -1.600 0.44905385 0.79514566 -0.4759461
#> 4 1 34 80 2.5 5.0 60 5.0 5.0 1 1 -0.875 -0.850 0.27140789 4.63953174 -0.6035921
#> 5 1 37 40 5.0 10.0 60 5.0 2.5 1 1 -0.550 -0.900 -0.03370054 0.84622952 -0.5837005
#> 6 1 39 20 20.0 2.5 60 2.5 2.5 1 1 -1.550 -0.725 -0.47233862 1.05805421 -2.0223386
#> U_2 CHOICE
#> 1 0.39248610 2
#> 2 -1.45739214 1
#> 3 -0.80485434 1
#> 4 3.78953174 2
#> 5 -0.05377048 2
#> 6 0.33305421 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
#> -2920.0 -597.5 467.5
#> -1600.0 -857.5 402.5
#> initial value 998.131940
#> 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
#> iter 2 value 993.094191
#> iter 3 value 975.977284
#> iter 4 value 975.860148
#> iter 5 value 971.032264
#> iter 6 value 971.027871
#> iter 6 value 971.027867
#> iter 6 value 971.027867
#> final value 971.027867
#> converged
#>
#>
......@@ -561,11 +561,11 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> \ 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 0.01 -0.05 -0.04 0.01 0.00
#> est_bwarte 3 2 0.00 0.01 -0.01 0.01 0.01 0.01
#> est_blade 2 2 -0.05 0.01 -0.05 -0.04 0.01 0.01
#> est_bwarte 3 2 0.00 0.01 0.00 0.01 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 0.52 0.11 0.44 0.59 0.15 0.07
#> rob_pval0_bwarte 6 2 0.68 0.20 0.54 0.82 0.28 0.14
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
......@@ -606,20 +606,20 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 9 80 5.0 0 60 20.0 10.0 1
#> 2 1 12 60 2.5 10 40 20.0 0.0 1
#> 3 1 13 20 20.0 10 80 2.5 0.0 1
#> 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 0.458928485 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 9 80 5.0 0 60 20.0 10.0 1 1 -1.150 -1.800 0.8053210 -0.7760447 -0.34467900 -2.5760447
#> 2 1 12 60 2.5 10 40 20.0 0.0 1 1 -0.575 -1.800 2.4581484 -0.5422855 1.88314842 -2.3422855
#> 3 1 13 20 20.0 10 80 2.5 0.0 1 1 -1.400 -0.975 0.4806134 -1.7030310 -0.91938664 -2.6780310
#> 4 1 70 80 5.0 10 20 20.0 2.5 1 1 -0.950 -1.550 -0.8558539 1.9784273 -1.80585392 0.4284273
#> 5 1 71 60 20.0 10 80 10.0 0.0 1 1 -1.800 -1.500 1.4200481 0.8856199 -0.37995187 -0.6143801
#> 6 1 73 60 10.0 0 40 20.0 10.0 1 1 -1.300 -1.600 1.3592506 -0.1823192 0.05925063 -1.7823192
#> CHOICE
#> 1 1
#> 2 1
#> 3 1
#> 4 2
#> 5 1
#> 6 1
#>
#>
#> This is Run number 1
......@@ -634,37 +634,37 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 9 80 5.0 0 60 20.0 10.0 1
#> 2 1 12 60 2.5 10 40 20.0 0.0 1
#> 3 1 13 20 20.0 10 80 2.5 0.0 1
#> 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 -0.01463130 -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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 9 80 5.0 0 60 20.0 10.0 1 1 -1.150 -1.800 1.1057596 0.8176933 -0.04424039 -0.9823067
#> 2 1 12 60 2.5 10 40 20.0 0.0 1 1 -0.575 -1.800 1.4664332 0.2855647 0.89143319 -1.5144353
#> 3 1 13 20 20.0 10 80 2.5 0.0 1 1 -1.400 -0.975 -0.2567456 2.0307365 -1.65674558 1.0557365
#> 4 1 70 80 5.0 10 20 20.0 2.5 1 1 -0.950 -1.550 1.7936733 0.2273817 0.84367334 -1.3226183
#> 5 1 71 60 20.0 10 80 10.0 0.0 1 1 -1.800 -1.500 -0.5080847 1.8371868 -2.30808468 0.3371868
#> 6 1 73 60 10.0 0 40 20.0 10.0 1 1 -1.300 -1.600 0.2315646 0.5250324 -1.06843538 -1.0749676
#> CHOICE
#> 1 1
#> 2 1
#> 3 2
#> 4 1
#> 5 2
#> 6 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
#> -2640.0 -3282.5 1152.5
#> -2720.0 -3230.0 1477.5
#> initial value 998.131940
#> 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
#> iter 2 value 961.903346
#> iter 3 value 923.013503
#> iter 4 value 921.553693
#> iter 5 value 899.826852
#> iter 6 value 899.416093
#> iter 7 value 899.408733
#> iter 7 value 899.408728
#> iter 7 value 899.408728
#> final value 899.408728
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
......@@ -678,37 +678,37 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 1 9 80 5.0 0 60 20.0 10.0 1
#> 2 1 12 60 2.5 10 40 20.0 0.0 1
#> 3 1 13 20 20.0 10 80 2.5 0.0 1
#> 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 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
#> ID Choice_situation alt1_x1 alt1_x2 alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 9 80 5.0 0 60 20.0 10.0 1 1 -1.150 -1.800 -0.55606335 0.4418297 -1.7060633 -1.3581703
#> 2 1 12 60 2.5 10 40 20.0 0.0 1 1 -0.575 -1.800 -0.70525965 0.3030154 -1.2802596 -1.4969846
#> 3 1 13 20 20.0 10 80 2.5 0.0 1 1 -1.400 -0.975 0.76358526 1.1547805 -0.6364147 0.1797805
#> 4 1 70 80 5.0 10 20 20.0 2.5 1 1 -0.950 -1.550 -0.50057341 1.6569802 -1.4505734 0.1069802
#> 5 1 71 60 20.0 10 80 10.0 0.0 1 1 -1.800 -1.500 0.95196390 4.6640005 -0.8480361 3.1640005
#> 6 1 73 60 10.0 0 40 20.0 10.0 1 1 -1.300 -1.600 -0.07807331 0.9519585 -1.3780733 -0.6480415
#> CHOICE
#> 1 2
#> 2 1
#> 3 2
#> 4 2
#> 5 2
#> 6 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
#> -2840.0 -2792.5 980.0
#> -1480.0 -3742.5 752.5
#> initial value 998.131940
#> 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
#> iter 2 value 931.326512
#> iter 3 value 904.389362
#> iter 4 value 902.747564
#> iter 5 value 895.585146
#> iter 6 value 895.212841
#> iter 7 value 895.208094
#> iter 7 value 895.208091
#> iter 7 value 895.208091
#> final value 895.208091
#> converged
#>
#>
......@@ -716,11 +716,11 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> \ 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 0.00 0.01 0.02 0.00 0.00
#> est_blade 2 2 -0.05 0.01 -0.05 -0.04 0.01 0.01
#> est_bwarte 3 2 0.01 0.03 0.00 0.03 0.04 0.02
#> 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 0.05 0.06 0.01 0.09 0.08 0.04
#> rob_pval0_bwarte 6 2 0.28 0.40 0.00 0.57 0.57 0.28
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE TRUE
......@@ -760,20 +760,20 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
#> 1 1 1 1 80 20 2.5 20.0 10 5
#> 2 1 2 1 60 40 5.0 10.0 5 10
#> 3 1 3 1 60 20 20.0 20.0 0 10
#> 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 0.1987193 -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
#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3 group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 1 1 80 20 2.5 20.0 10 5 1 -0.775 -1.500 0.12234341 0.6503776 -0.6526566 -0.84962241
#> 2 1 2 1 60 40 5.0 10.0 5 10 1 -0.850 -0.900 -0.43360819 0.6615210 -1.2836082 -0.23847900
#> 3 1 3 1 60 20 20.0 20.0 0 10 1 -2.000 -1.400 -0.31286639 5.7827787 -2.3128664 4.38277870
#> 4 1 4 1 20 80 20.0 2.5 0 10 1 -1.600 -0.775 -0.15949911 0.6857678 -1.7594991 -0.08923219
#> 5 1 5 1 40 80 10.0 5.0 10 5 1 -0.900 -1.050 -0.05237788 1.5859039 -0.9523779 0.53590389
#> 6 1 6 1 60 80 5.0 2.5 0 0 1 -0.950 -0.975 2.34036634 0.2393918 1.3903663 -0.73560816
#> CHOICE
#> 1 1
#> 2 2
#> 3 2
#> 4 2
#> 5 2
#> 6 1
#>
#>
#> This is Run number 1
......@@ -788,36 +788,36 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
#> 1 1 1 1 80 20 2.5 20.0 10 5
#> 2 1 2 1 60 40 5.0 10.0 5 10
#> 3 1 3 1 60 20 20.0 20.0 0 10
#> 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 -0.3472514 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
#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3 group V_1 V_2 e_1 e_2 U_1
#> 1 1 1 1 80 20 2.5 20.0 10 5 1 -0.775 -1.500 1.74395858 1.6507121 0.968958575
#> 2 1 2 1 60 40 5.0 10.0 5 10 1 -0.850 -0.900 1.10763894 0.9337414 0.257638936
#> 3 1 3 1 60 20 20.0 20.0 0 10 1 -2.000 -1.400 -1.23519031 -0.7089281 -3.235190315
#> 4 1 4 1 20 80 20.0 2.5 0 10 1 -1.600 -0.775 -0.06854059 2.1896932 -1.668540589
#> 5 1 5 1 40 80 10.0 5.0 10 5 1 -0.900 -1.050 0.90927543 0.3884170 0.009275432
#> 6 1 6 1 60 80 5.0 2.5 0 0 1 -0.950 -0.975 0.57272851 0.4992305 -0.377271490
#> U_2 CHOICE
#> 1 0.15071215 1
#> 2 0.03374141 1
#> 3 -2.10892809 2
#> 4 1.41469320 2
#> 5 -0.66158301 1
#> 6 -0.47576952 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
#> -620.0 -877.5 407.5
#> -880 -830 405
#> initial value 998.131940
#> 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
#> iter 2 value 994.862696
#> iter 3 value 973.696602
#> iter 4 value 973.644209
#> iter 5 value 971.118222
#> iter 6 value 971.113908
#> iter 6 value 971.113906
#> iter 6 value 971.113906
#> final value 971.113906
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
......@@ -831,36 +831,36 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> data has been made
#>
#> First few observations
#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
#> 1 1 1 1 80 20 2.5 20.0 10 5
#> 2 1 2 1 60 40 5.0 10.0 5 10
#> 3 1 3 1 60 20 20.0 20.0 0 10
#> 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 0.5287428 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
#> ID Choice_situation Block alt1_x1 alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3 group V_1 V_2 e_1 e_2 U_1 U_2
#> 1 1 1 1 80 20 2.5 20.0 10 5 1 -0.775 -1.500 -1.1552146 1.6918663 -1.930215 0.1918663
#> 2 1 2 1 60 40 5.0 10.0 5 10 1 -0.850 -0.900 3.5255143 2.5874719 2.675514 1.6874719
#> 3 1 3 1 60 20 20.0 20.0 0 10 1 -2.000 -1.400 -0.1288440 3.2280170 -2.128844 1.8280170
#> 4 1 4 1 20 80 20.0 2.5 0 10 1 -1.600 -0.775 -0.3072974 -0.3710956 -1.907297 -1.1460956
#> 5 1 5 1 40 80 10.0 5.0 10 5 1 -0.900 -1.050 -0.6042191 -0.1952303 -1.504219 -1.2452303
#> 6 1 6 1 60 80 5.0 2.5 0 0 1 -0.950 -0.975 -0.6233011 -0.2803958 -1.573301 -1.2553958
#> CHOICE
#> 1 2
#> 2 1
#> 3 2
#> 4 2
#> 5 2
#> 6 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
#> -580.0 -1077.5 495.0
#> -1060.0 -832.5 460.0
#> initial value 998.131940
#> 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
#> iter 2 value 994.307556
#> iter 3 value 972.255010
#> iter 4 value 972.242499
#> iter 5 value 968.104609
#> iter 6 value 968.103010
#> iter 6 value 968.103008
#> iter 6 value 968.103008
#> final value 968.103008
#> converged
#>
#>
......@@ -868,29 +868,29 @@ sedrive <- sim_all(nosim = nosim, resps=resps, destype = destype,
#> \ 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 -0.06 -0.05 0.01 0.00
#> est_bwarte 3 2 0.02 0.00 0.02 0.02 0.01 0.00
#> est_blade 2 2 -0.05 0.00 -0.05 -0.05 0.00 0.00
#> est_bwarte 3 2 0.02 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 0.11 0.04 0.08 0.13 0.05 0.03
#> rob_pval0_bwarte 6 2 0.12 0.08 0.06 0.18 0.12 0.06
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
#> 100
#> 32.978 sec elapsed
#> 9.804 sec elapsed
#> $tic
#> elapsed
#> 0.8
#> elapsed
#> 10314.36
#>
#> $toc
#> elapsed
#> 33.778
#> elapsed
#> 10324.16
#>
#> $msg
#> logical(0)
#>
#> $callback_msg
#> [1] "32.978 sec elapsed"
#> [1] "9.804 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%" />
man/figures/README-example-1.png

39.7 KiB | W: | H:

man/figures/README-example-1.png

53.3 KiB | W: | H:

man/figures/README-example-1.png
man/figures/README-example-1.png
man/figures/README-example-1.png
man/figures/README-example-1.png
  • 2-up
  • Swipe
  • Onion skin
man/figures/README-example-2.png

46.5 KiB | W: | H:

man/figures/README-example-2.png

42.2 KiB | W: | H:

man/figures/README-example-2.png
man/figures/README-example-2.png
man/figures/README-example-2.png
man/figures/README-example-2.png
  • 2-up
  • Swipe
  • Onion skin
man/figures/README-example-3.png

42.3 KiB | W: | H:

man/figures/README-example-3.png

56.1 KiB | W: | H:

man/figures/README-example-3.png
man/figures/README-example-3.png
man/figures/README-example-3.png
man/figures/README-example-3.png
  • 2-up
  • Swipe
  • Onion skin
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment