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......@@ -46,6 +46,1130 @@ remotes::install_gitlab(repo = "dj44vuri/simulateDCE" , host = "https://git.idiv
This is a basic example which shows you how to solve a common problem:
``` r
library(simulateDCE)
## basic example code
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"
```
<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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