simulateDCE
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
-
Test different designs in terms of statistical power, efficiency and unbiasedness
-
To test the effects of deviations from RUM, e.g. heuristics, on model performance for different designs.
-
In teaching, using simulated data is useful, if you want to know the data generating process. It helps to demonstrate Maximum likelihood and choice models, knowing exactly what you should expect.
-
You can use simulation in pre-registration to justify your sample size and design choice.
-
Before data collection, you can use simulated data to estimate the models you plan to use in the actual analysis. You can thus make sure, you can estimate all effects for given sample sizes.
Installation
You can install the development version of simulateDCE from gitlab. You
need to install the remotes package first. The current version is
alpha and there is no version on cran:
# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?
install.packages("remotes")
remotes::install_gitlab(repo = "dj44vuri/simulateDCE" , host = "https://git.idiv.de")Example
This is a basic example which shows you how to solve a common problem:
library(simulateDCE)
library(rlang)
print("lests")
#> [1] "lests"
#set.seed(22233)
# Designpath indicates the folder where all designs that should be simulated are stored. Can be either ngd files (for NGENE) or Robjects for spdesign)
designpath<- system.file("extdata","SE_DRIVE" ,package = "simulateDCE")
# on your computer, it would be something like
# designpath <- "c:/myfancyDCE/Designs"
# 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
decisiongroups=c(0,0.7,1)
# set the values of the parameters you want to use in the simulation
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),
alt2.x3= expr(alt2.x3/10)
)
#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 =
list(
v1 =V.1~ bpreis * alt1.x1 + blade*alt1.x2 + bwarte*alt1.x3 ,
v2 =V.2~ bpreis * alt2.x1 + blade*alt2.x2 + bwarte*alt2.x3
)
,
u2 = list( v1 =V.1~ bpreis * alt1.x1 ,
v2 =V.2~ bpreis * alt2.x1)
)
# specify the designtype "ngene" or "spdesign"
destype="ngene"
#lets go
sedrive <- simulateDCE::sim_all()
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
#> $u1$v1
#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
#>
#> $u1$v2
#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
#>
#>
#> $u2
#> $u2$v1
#> V.1 ~ bpreis * alt1.x1
#>
#> $u2$v2
#> V.2 ~ bpreis * alt2.x1
#>
#>
#> Utility function used for Logit estimation with mixl:
#>
#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
#> 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
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 7 80 2.5
#> 2 1 19 20 2.5
#> 3 1 30 20 10.0
#> 4 1 32 40 20.0
#> 5 1 39 40 20.0
#> 6 1 48 60 5.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 10.0 60 20.0 10 1
#> 2 5.0 60 2.5 0 1
#> 3 5.0 80 5.0 10 1
#> 4 2.5 80 2.5 0 1
#> 5 0.0 80 10.0 10 1
#> 6 2.5 20 5.0 10 1
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.800 2.8927045
#> 2 1 -0.275 -0.775 2.1129458
#> 3 1 -0.800 -0.950 -0.3070059
#> 4 1 -1.750 -0.975 0.2125815
#> 5 1 -1.800 -1.300 0.5101632
#> 6 1 -0.900 -0.350 -0.9494807
#> e_2 U_1 U_2 CHOICE
#> 1 0.09958433 2.117705 -1.700416 1
#> 2 3.47451776 1.837946 2.699518 2
#> 3 -0.28860974 -1.107006 -1.238610 1
#> 4 3.65240491 -1.537418 2.677405 2
#> 5 -0.14448942 -1.289837 -1.444489 1
#> 6 -1.04296995 -1.849481 -1.392970 2
#>
#>
#> This is Run number 1
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 7 80 2.5
#> 2 1 19 20 2.5
#> 3 1 30 20 10.0
#> 4 1 32 40 20.0
#> 5 1 39 40 20.0
#> 6 1 48 60 5.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 10.0 60 20.0 10 1
#> 2 5.0 60 2.5 0 1
#> 3 5.0 80 5.0 10 1
#> 4 2.5 80 2.5 0 1
#> 5 0.0 80 10.0 10 1
#> 6 2.5 20 5.0 10 1
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.800 -0.06362638
#> 2 1 -0.275 -0.775 -0.81571577
#> 3 1 -0.800 -0.950 -1.09388352
#> 4 1 -1.750 -0.975 0.28996875
#> 5 1 -1.800 -1.300 1.03059224
#> 6 1 -0.900 -0.350 -1.10504379
#> e_2 U_1 U_2 CHOICE
#> 1 0.1958595 -0.8386264 -1.6041405 1
#> 2 0.1028995 -1.0907158 -0.6721005 2
#> 3 0.7165451 -1.8938835 -0.2334549 2
#> 4 1.4748351 -1.4600313 0.4998351 2
#> 5 4.5718398 -0.7694078 3.2718398 2
#> 6 0.8766732 -2.0050438 0.5266732 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
#> -860.0 -1147.5 532.5
#> initial value 998.131940
#> iter 2 value 988.178813
#> iter 3 value 959.683236
#> iter 4 value 959.648380
#> iter 5 value 955.999179
#> iter 6 value 955.979330
#> iter 7 value 955.979295
#> iter 7 value 955.979295
#> iter 7 value 955.979295
#> final value 955.979295
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 7 80 2.5
#> 2 1 19 20 2.5
#> 3 1 30 20 10.0
#> 4 1 32 40 20.0
#> 5 1 39 40 20.0
#> 6 1 48 60 5.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 10.0 60 20.0 10 1
#> 2 5.0 60 2.5 0 1
#> 3 5.0 80 5.0 10 1
#> 4 2.5 80 2.5 0 1
#> 5 0.0 80 10.0 10 1
#> 6 2.5 20 5.0 10 1
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.800 -0.8816771
#> 2 1 -0.275 -0.775 0.9004269
#> 3 1 -0.800 -0.950 -0.3108731
#> 4 1 -1.750 -0.975 -0.7695269
#> 5 1 -1.800 -1.300 2.8853455
#> 6 1 -0.900 -0.350 -0.1098324
#> e_2 U_1 U_2
#> 1 0.6516580 -1.6566771 -1.14834197
#> 2 0.4584193 0.6254269 -0.31658066
#> 3 1.2184928 -1.1108731 0.26849278
#> 4 -0.1660211 -2.5195269 -1.14102109
#> 5 -0.5943992 1.0853455 -1.89439922
#> 6 0.3193140 -1.0098324 -0.03068595
#> CHOICE
#> 1 2
#> 2 1
#> 3 2
#> 4 2
#> 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
#> 120 -655 295
#> initial value 998.131940
#> iter 2 value 994.305298
#> iter 3 value 990.053293
#> iter 4 value 989.940656
#> iter 5 value 987.629292
#> iter 6 value 987.628992
#> iter 6 value 987.628991
#> iter 6 value 987.628991
#> final value 987.628991
#> converged
#>
#>
#> ================ ==== === ===== ==== ===== ===== ===== ====
#> \ vars n mean sd min max range se
#> ================ ==== === ===== ==== ===== ===== ===== ====
#> est_bpreis 1 2 -0.01 0.01 -0.01 0.00 0.01 0.00
#> est_blade 2 2 -0.04 0.02 -0.06 -0.02 0.03 0.02
#> est_bwarte 3 2 0.02 0.00 0.02 0.03 0.01 0.00
#> rob_pval0_bpreis 4 2 0.04 0.06 0.00 0.09 0.09 0.04
#> rob_pval0_blade 5 2 0.00 0.00 0.00 0.00 0.00 0.00
#> rob_pval0_bwarte 6 2 0.04 0.03 0.02 0.06 0.04 0.02
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE TRUE
#> 50 50
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
#> $u1$v1
#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
#>
#> $u1$v2
#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
#>
#>
#> $u2
#> $u2$v1
#> V.1 ~ bpreis * alt1.x1
#>
#> $u2$v2
#> V.2 ~ bpreis * alt2.x1
#>
#>
#> Utility function used for Logit estimation with mixl:
#>
#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
#> 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
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 12 60 2.5
#> 2 1 16 20 10.0
#> 3 1 17 20 20.0
#> 4 1 25 60 5.0
#> 5 1 29 20 5.0
#> 6 1 32 40 10.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0.0 20 20.0 10 1
#> 2 5.0 40 5.0 0 1
#> 3 0.0 80 10.0 10 1
#> 4 10.0 20 20.0 5 1
#> 5 10.0 80 5.0 0 1
#> 6 2.5 80 2.5 5 1
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.400 1.20580231
#> 2 1 -0.800 -0.750 -0.72752412
#> 3 1 -1.600 -1.300 -0.05762304
#> 4 1 -0.750 -1.500 -0.83547157
#> 5 1 -0.350 -1.150 3.85444600
#> 6 1 -1.050 -0.875 1.64701776
#> e_2 U_1 U_2
#> 1 -0.28691332 0.4308023 -1.6869133
#> 2 0.06648158 -1.5275241 -0.6835184
#> 3 1.68916541 -1.6576230 0.3891654
#> 4 0.40357792 -1.5854716 -1.0964221
#> 5 0.13880669 3.5044460 -1.0111933
#> 6 1.09745093 0.5970178 0.2224509
#> CHOICE
#> 1 1
#> 2 2
#> 3 2
#> 4 2
#> 5 1
#> 6 1
#>
#>
#> This is Run number 1
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 12 60 2.5
#> 2 1 16 20 10.0
#> 3 1 17 20 20.0
#> 4 1 25 60 5.0
#> 5 1 29 20 5.0
#> 6 1 32 40 10.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0.0 20 20.0 10 1
#> 2 5.0 40 5.0 0 1
#> 3 0.0 80 10.0 10 1
#> 4 10.0 20 20.0 5 1
#> 5 10.0 80 5.0 0 1
#> 6 2.5 80 2.5 5 1
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.400 -0.09932726
#> 2 1 -0.800 -0.750 2.18018219
#> 3 1 -1.600 -1.300 1.30134429
#> 4 1 -0.750 -1.500 1.55197796
#> 5 1 -0.350 -1.150 0.07874983
#> 6 1 -1.050 -0.875 -1.06565108
#> e_2 U_1 U_2
#> 1 2.2497903 -0.8743273 0.84979034
#> 2 0.3329742 1.3801822 -0.41702578
#> 3 0.9046182 -0.2986557 -0.39538182
#> 4 -1.2414809 0.8019780 -2.74148090
#> 5 -0.8624243 -0.2712502 -2.01242427
#> 6 0.9398788 -2.1156511 0.06487882
#> CHOICE
#> 1 2
#> 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
#> -340 -1095 305
#> initial value 998.131940
#> iter 2 value 984.073383
#> iter 3 value 978.081615
#> iter 4 value 977.767304
#> iter 5 value 971.033395
#> iter 6 value 971.027390
#> iter 6 value 971.027385
#> iter 6 value 971.027385
#> final value 971.027385
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 12 60 2.5
#> 2 1 16 20 10.0
#> 3 1 17 20 20.0
#> 4 1 25 60 5.0
#> 5 1 29 20 5.0
#> 6 1 32 40 10.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0.0 20 20.0 10 1
#> 2 5.0 40 5.0 0 1
#> 3 0.0 80 10.0 10 1
#> 4 10.0 20 20.0 5 1
#> 5 10.0 80 5.0 0 1
#> 6 2.5 80 2.5 5 1
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.400 0.44334136
#> 2 1 -0.800 -0.750 -0.43185157
#> 3 1 -1.600 -1.300 -0.09584172
#> 4 1 -0.750 -1.500 2.74658736
#> 5 1 -0.350 -1.150 -0.51575280
#> 6 1 -1.050 -0.875 -0.33088933
#> e_2 U_1 U_2 CHOICE
#> 1 0.3975165 -0.3316586 -1.0024835 1
#> 2 1.4211569 -1.2318516 0.6711569 2
#> 3 1.0034880 -1.6958417 -0.2965120 2
#> 4 0.8780181 1.9965874 -0.6219819 1
#> 5 0.9818505 -0.8657528 -0.1681495 2
#> 6 1.7042698 -1.3808893 0.8292698 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
#> -280 -905 345
#> initial value 998.131940
#> iter 2 value 988.003109
#> iter 3 value 983.732741
#> iter 4 value 983.724196
#> iter 5 value 979.048736
#> iter 6 value 979.044949
#> iter 6 value 979.044947
#> iter 6 value 979.044947
#> final value 979.044947
#> 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 -0.04 0.01 -0.05 -0.04 0.01 0.01
#> est_bwarte 3 2 0.01 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 0.50 0.41 0.21 0.79 0.58 0.29
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
#> 100
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
#> $u1$v1
#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
#>
#> $u1$v2
#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
#>
#>
#> $u2
#> $u2$v1
#> V.1 ~ bpreis * alt1.x1
#>
#> $u2$v2
#> V.2 ~ bpreis * alt2.x1
#>
#>
#> Utility function used for Logit estimation with mixl:
#>
#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
#> 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
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 3 80 5.0
#> 2 1 5 60 2.5
#> 3 1 10 80 2.5
#> 4 1 34 80 2.5
#> 5 1 37 40 5.0
#> 6 1 39 20 20.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0.0 20 5.0 10.0 1
#> 2 5.0 20 20.0 5.0 1
#> 3 2.5 20 20.0 0.0 1
#> 4 5.0 60 5.0 5.0 1
#> 5 10.0 60 5.0 2.5 1
#> 6 2.5 60 2.5 2.5 1
#> group V_1 V_2 e_1
#> 1 1 -1.150 -0.350 -0.32663211
#> 2 1 -0.675 -1.500 -0.04162689
#> 3 1 -0.925 -1.600 -0.52492188
#> 4 1 -0.875 -0.850 -1.14189023
#> 5 1 -0.550 -0.900 0.19650068
#> 6 1 -1.550 -0.725 2.74825383
#> e_2 U_1 U_2 CHOICE
#> 1 0.2288010 -1.4766321 -0.1211990 2
#> 2 1.0875948 -0.7166269 -0.4124052 2
#> 3 0.1472598 -1.4499219 -1.4527402 1
#> 4 0.5765191 -2.0168902 -0.2734809 2
#> 5 -0.5803934 -0.3534993 -1.4803934 1
#> 6 -0.8761884 1.1982538 -1.6011884 1
#>
#>
#> This is Run number 1
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 3 80 5.0
#> 2 1 5 60 2.5
#> 3 1 10 80 2.5
#> 4 1 34 80 2.5
#> 5 1 37 40 5.0
#> 6 1 39 20 20.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0.0 20 5.0 10.0 1
#> 2 5.0 20 20.0 5.0 1
#> 3 2.5 20 20.0 0.0 1
#> 4 5.0 60 5.0 5.0 1
#> 5 10.0 60 5.0 2.5 1
#> 6 2.5 60 2.5 2.5 1
#> group V_1 V_2 e_1
#> 1 1 -1.150 -0.350 0.9214793
#> 2 1 -0.675 -1.500 -0.7937151
#> 3 1 -0.925 -1.600 0.5612728
#> 4 1 -0.875 -0.850 2.9230889
#> 5 1 -0.550 -0.900 0.1761764
#> 6 1 -1.550 -0.725 1.0340286
#> e_2 U_1 U_2
#> 1 0.09295071 -0.2285207 -0.25704929
#> 2 -0.18278050 -1.4687151 -1.68278050
#> 3 -0.24595450 -0.3637272 -1.84595450
#> 4 -0.74954312 2.0480889 -1.59954312
#> 5 -0.52864852 -0.3738236 -1.42864852
#> 6 0.69916199 -0.5159714 -0.02583801
#> 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
#> -2640.0 -1060.0 662.5
#> initial value 998.131940
#> iter 2 value 987.031183
#> iter 3 value 957.685378
#> iter 4 value 957.680370
#> iter 5 value 954.925156
#> iter 6 value 945.725076
#> iter 7 value 945.695285
#> iter 8 value 945.695175
#> iter 8 value 945.695175
#> final value 945.695175
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 3 80 5.0
#> 2 1 5 60 2.5
#> 3 1 10 80 2.5
#> 4 1 34 80 2.5
#> 5 1 37 40 5.0
#> 6 1 39 20 20.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0.0 20 5.0 10.0 1
#> 2 5.0 20 20.0 5.0 1
#> 3 2.5 20 20.0 0.0 1
#> 4 5.0 60 5.0 5.0 1
#> 5 10.0 60 5.0 2.5 1
#> 6 2.5 60 2.5 2.5 1
#> group V_1 V_2 e_1
#> 1 1 -1.150 -0.350 -0.8218428
#> 2 1 -0.675 -1.500 0.4133131
#> 3 1 -0.925 -1.600 0.4824588
#> 4 1 -0.875 -0.850 -1.2658097
#> 5 1 -0.550 -0.900 -0.6930574
#> 6 1 -1.550 -0.725 -0.6815915
#> e_2 U_1 U_2 CHOICE
#> 1 -0.6493651 -1.9718428 -0.9993651 2
#> 2 0.8461510 -0.2616869 -0.6538490 1
#> 3 0.3849732 -0.4425412 -1.2150268 1
#> 4 -0.2971578 -2.1408097 -1.1471578 2
#> 5 -0.8024491 -1.2430574 -1.7024491 1
#> 6 -0.4752339 -2.2315915 -1.2002339 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
#> -1320.0 -1027.5 537.5
#> initial value 998.131940
#> iter 2 value 992.731937
#> iter 3 value 967.306984
#> iter 4 value 967.287995
#> iter 5 value 964.318376
#> iter 6 value 964.313823
#> iter 6 value 964.313820
#> iter 6 value 964.313820
#> final value 964.313820
#> 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 0.01 -0.06 -0.05 0.01 0.01
#> est_bwarte 3 2 0.02 0.00 0.02 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 0.06 0.01 0.06 0.07 0.01 0.01
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
#> 100
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
#> $u1$v1
#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
#>
#> $u1$v2
#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
#>
#>
#> $u2
#> $u2$v1
#> V.1 ~ bpreis * alt1.x1
#>
#> $u2$v2
#> V.2 ~ bpreis * alt2.x1
#>
#>
#> Utility function used for Logit estimation with mixl:
#>
#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
#> 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
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 9 80 5.0
#> 2 1 12 60 2.5
#> 3 1 13 20 20.0
#> 4 1 70 80 5.0
#> 5 1 71 60 20.0
#> 6 1 73 60 10.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0 60 20.0 10.0 1
#> 2 10 40 20.0 0.0 1
#> 3 10 80 2.5 0.0 1
#> 4 10 20 20.0 2.5 1
#> 5 10 80 10.0 0.0 1
#> 6 0 40 20.0 10.0 1
#> group V_1 V_2 e_1
#> 1 1 -1.150 -1.800 0.4772651
#> 2 1 -0.575 -1.800 -1.0611813
#> 3 1 -1.400 -0.975 -0.4549814
#> 4 1 -0.950 -1.550 1.0741179
#> 5 1 -1.800 -1.500 0.6850764
#> 6 1 -1.300 -1.600 2.1581413
#> e_2 U_1 U_2
#> 1 -0.58862455 -0.6727349 -2.3886245
#> 2 1.67391615 -1.6361813 -0.1260839
#> 3 0.08433351 -1.8549814 -0.8906665
#> 4 0.16471135 0.1241179 -1.3852887
#> 5 -0.80503749 -1.1149236 -2.3050375
#> 6 -0.78193942 0.8581413 -2.3819394
#> CHOICE
#> 1 1
#> 2 2
#> 3 2
#> 4 1
#> 5 1
#> 6 1
#>
#>
#> This is Run number 1
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 9 80 5.0
#> 2 1 12 60 2.5
#> 3 1 13 20 20.0
#> 4 1 70 80 5.0
#> 5 1 71 60 20.0
#> 6 1 73 60 10.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0 60 20.0 10.0 1
#> 2 10 40 20.0 0.0 1
#> 3 10 80 2.5 0.0 1
#> 4 10 20 20.0 2.5 1
#> 5 10 80 10.0 0.0 1
#> 6 0 40 20.0 10.0 1
#> group V_1 V_2 e_1
#> 1 1 -1.150 -1.800 -0.284096565
#> 2 1 -0.575 -1.800 -0.020855208
#> 3 1 -1.400 -0.975 2.808193631
#> 4 1 -0.950 -1.550 1.512635398
#> 5 1 -1.800 -1.500 -0.869856696
#> 6 1 -1.300 -1.600 0.001496538
#> e_2 U_1 U_2 CHOICE
#> 1 3.7852439 -1.4340966 1.9852439 2
#> 2 2.5441347 -0.5958552 0.7441347 2
#> 3 -0.1408644 1.4081936 -1.1158644 1
#> 4 -0.2739250 0.5626354 -1.8239250 1
#> 5 -0.2920285 -2.6698567 -1.7920285 2
#> 6 0.9243727 -1.2985035 -0.6756273 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
#> -2400 -3680 1320
#> initial value 998.131940
#> iter 2 value 956.785003
#> iter 3 value 912.039295
#> iter 4 value 911.870417
#> iter 5 value 885.881709
#> iter 6 value 885.187568
#> iter 7 value 885.171492
#> iter 8 value 885.171476
#> iter 8 value 885.171476
#> final value 885.171476
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation alt1_x1 alt1_x2
#> 1 1 9 80 5.0
#> 2 1 12 60 2.5
#> 3 1 13 20 20.0
#> 4 1 70 80 5.0
#> 5 1 71 60 20.0
#> 6 1 73 60 10.0
#> alt1_x3 alt2_x1 alt2_x2 alt2_x3 Block
#> 1 0 60 20.0 10.0 1
#> 2 10 40 20.0 0.0 1
#> 3 10 80 2.5 0.0 1
#> 4 10 20 20.0 2.5 1
#> 5 10 80 10.0 0.0 1
#> 6 0 40 20.0 10.0 1
#> group V_1 V_2 e_1
#> 1 1 -1.150 -1.800 0.6645192
#> 2 1 -0.575 -1.800 -0.8450051
#> 3 1 -1.400 -0.975 0.1125148
#> 4 1 -0.950 -1.550 1.0543183
#> 5 1 -1.800 -1.500 1.1168013
#> 6 1 -1.300 -1.600 -0.1311416
#> e_2 U_1 U_2 CHOICE
#> 1 2.3304233 -0.4854808 0.5304233 2
#> 2 0.2022020 -1.4200051 -1.5977980 1
#> 3 -0.1148274 -1.2874852 -1.0898274 2
#> 4 -1.3880265 0.1043183 -2.9380265 1
#> 5 0.1356148 -0.6831987 -1.3643852 1
#> 6 0.9455601 -1.4311416 -0.6544399 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
#> -3200.0 -2932.5 1142.5
#> initial value 998.131940
#> iter 2 value 965.989359
#> iter 3 value 962.943975
#> iter 4 value 962.790350
#> iter 5 value 915.909913
#> iter 6 value 915.781694
#> iter 7 value 915.780836
#> iter 7 value 915.780833
#> iter 7 value 915.780833
#> final value 915.780833
#> 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 0.01 -0.05 -0.04 0.01 0.00
#> est_bwarte 3 2 0.02 0.00 0.02 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 0.01 0.02 0.00 0.03 0.03 0.01
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> TRUE
#> 100
#> Utility function used in simulation, ie the true utility:
#>
#> $u1
#> $u1$v1
#> V.1 ~ bpreis * alt1.x1 + blade * alt1.x2 + bwarte * alt1.x3
#>
#> $u1$v2
#> V.2 ~ bpreis * alt2.x1 + blade * alt2.x2 + bwarte * alt2.x3
#>
#>
#> $u2
#> $u2$v1
#> V.1 ~ bpreis * alt1.x1
#>
#> $u2$v2
#> V.2 ~ bpreis * alt2.x1
#>
#>
#> Utility function used for Logit estimation with mixl:
#>
#> [1] "U_1 = @bpreis *$alt1_x1 + @blade *$alt1_x2 + @bwarte *$alt1_x3 ;U_2 = @bpreis *$alt2_x1 + @blade *$alt2_x2 + @bwarte *$alt2_x3 ;"
#> New names:
#> • `Choice situation` ->
#> `Choice.situation`
#>
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation Block alt1_x1
#> 1 1 1 1 80
#> 2 1 2 1 60
#> 3 1 3 1 60
#> 4 1 4 1 20
#> 5 1 5 1 40
#> 6 1 6 1 60
#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
#> 1 20 2.5 20.0 10 5
#> 2 40 5.0 10.0 5 10
#> 3 20 20.0 20.0 0 10
#> 4 80 20.0 2.5 0 10
#> 5 80 10.0 5.0 10 5
#> 6 80 5.0 2.5 0 0
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.500 0.53504757
#> 2 1 -0.850 -0.900 -0.93293876
#> 3 1 -2.000 -1.400 -1.97083982
#> 4 1 -1.600 -0.775 -0.09847358
#> 5 1 -0.900 -1.050 -0.91059496
#> 6 1 -0.950 -0.975 -0.27261150
#> e_2 U_1 U_2 CHOICE
#> 1 0.9131705 -0.2399524 -0.5868295 1
#> 2 -1.5528907 -1.7829388 -2.4528907 1
#> 3 -0.2159494 -3.9708398 -1.6159494 2
#> 4 0.1685500 -1.6984736 -0.6064500 2
#> 5 1.6256604 -1.8105950 0.5756604 2
#> 6 1.5055143 -1.2226115 0.5305143 2
#>
#>
#> This is Run number 1
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation Block alt1_x1
#> 1 1 1 1 80
#> 2 1 2 1 60
#> 3 1 3 1 60
#> 4 1 4 1 20
#> 5 1 5 1 40
#> 6 1 6 1 60
#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
#> 1 20 2.5 20.0 10 5
#> 2 40 5.0 10.0 5 10
#> 3 20 20.0 20.0 0 10
#> 4 80 20.0 2.5 0 10
#> 5 80 10.0 5.0 10 5
#> 6 80 5.0 2.5 0 0
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.500 -0.2361754
#> 2 1 -0.850 -0.900 1.2985628
#> 3 1 -2.000 -1.400 2.6517108
#> 4 1 -1.600 -0.775 -0.3215271
#> 5 1 -0.900 -1.050 -1.1880836
#> 6 1 -0.950 -0.975 0.9386790
#> e_2 U_1 U_2
#> 1 -0.2249671 -1.01117540 -1.72496708
#> 2 0.4231642 0.44856278 -0.47683584
#> 3 0.4632492 0.65171082 -0.93675077
#> 4 0.6960098 -1.92152712 -0.07899021
#> 5 1.0360301 -2.08808358 -0.01396992
#> 6 -0.1024565 -0.01132103 -1.07745654
#> CHOICE
#> 1 1
#> 2 1
#> 3 1
#> 4 2
#> 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
#> -140.0 -935.0 332.5
#> initial value 998.131940
#> iter 2 value 978.973745
#> iter 3 value 978.139237
#> iter 4 value 978.053388
#> iter 5 value 974.539684
#> iter 6 value 974.530921
#> iter 6 value 974.530913
#> iter 6 value 974.530913
#> final value 974.530913
#> converged
#> This is Run number 2
#> does sou_gis exist: FALSE
#>
#> dataset final_set exists: FALSE
#>
#> decisiongroups exists: TRUE
#> 1 2
#> 1007 433
#>
#> data has been made
#>
#> First few observations
#> ID Choice_situation Block alt1_x1
#> 1 1 1 1 80
#> 2 1 2 1 60
#> 3 1 3 1 60
#> 4 1 4 1 20
#> 5 1 5 1 40
#> 6 1 6 1 60
#> alt2_x1 alt1_x2 alt2_x2 alt1_x3 alt2_x3
#> 1 20 2.5 20.0 10 5
#> 2 40 5.0 10.0 5 10
#> 3 20 20.0 20.0 0 10
#> 4 80 20.0 2.5 0 10
#> 5 80 10.0 5.0 10 5
#> 6 80 5.0 2.5 0 0
#> group V_1 V_2 e_1
#> 1 1 -0.775 -1.500 0.2982044
#> 2 1 -0.850 -0.900 3.4745400
#> 3 1 -2.000 -1.400 3.5031943
#> 4 1 -1.600 -0.775 0.8386792
#> 5 1 -0.900 -1.050 1.8279937
#> 6 1 -0.950 -0.975 -1.1295965
#> e_2 U_1 U_2
#> 1 0.85521723 -0.4767956 -0.6447828
#> 2 2.20601106 2.6245400 1.3060111
#> 3 -0.03275998 1.5031943 -1.4327600
#> 4 0.87875516 -0.7613208 0.1037552
#> 5 -0.45114524 0.9279937 -1.5011452
#> 6 -0.63521469 -2.0795965 -1.6102147
#> CHOICE
#> 1 1
#> 2 1
#> 3 1
#> 4 2
#> 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
#> -660.0 -925.0 442.5
#> initial value 998.131940
#> iter 2 value 990.452175
#> iter 3 value 972.395315
#> iter 4 value 972.382101
#> iter 5 value 968.290249
#> iter 6 value 968.286828
#> iter 6 value 968.286823
#> iter 6 value 968.286823
#> final value 968.286823
#> 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 -0.05 0.00 0.00
#> est_bwarte 3 2 0.01 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 0.20 0.13 0.10 0.29 0.19 0.09
#> ================ ==== === ===== ==== ===== ===== ===== ====
#>
#> FALSE
#> 100
#> 34.002 sec elapsed
#> $tic
#> elapsed
#> 672.76
#>
#> $toc
#> elapsed
#> 706.762
#>
#> $msg
#> logical(0)
#>
#> $callback_msg
#> [1] "34.002 sec elapsed"
