Choice Data in R

Package design

Examples

Empirical data

library("choicedata")travel_mode_choice#> # A tibble: 840 × 8#>    individual mode  choice  wait  cost travel income  size#>         <int> <chr>  <int> <int> <int>  <int>  <int> <int>#>  1          1 plane      0    69    59    100     35     1#>  2          1 train      0    34    31    372     35     1#>  3          1 bus        0    35    25    417     35     1#>  4          1 car        1     0    10    180     35     1#>  5          2 plane      0    64    58     68     30     2#>  6          2 train      0    44    31    354     30     2#>  7          2 bus        0    53    25    399     30     2#>  8          2 car        1     0    11    255     30     2#>  9          3 plane      0    69   115    125     40     1#> 10          3 train      0    34    98    892     40     1#> # ℹ 830 more rowslong_to_wide(data_frame=travel_mode_choice,column_alternative="mode",column_decider="individual")#> # A tibble: 210 × 16#>    individual income  size wait_plane wait_train wait_bus wait_car cost_plane#>         <int>  <int> <int>      <int>      <int>    <int>    <int>      <int>#>  1          1     35     1         69         34       35        0         59#>  2          2     30     2         64         44       53        0         58#>  3          3     40     1         69         34       35        0        115#>  4          4     70     3         64         44       53        0         49#>  5          5     45     2         64         44       53        0         60#>  6          6     20     1         69         40       35        0         59#>  7          7     45     1         45         34       35        0        148#>  8          8     12     1         69         34       35        0        121#>  9          9     40     1         69         34       35        0         59#> 10         10     70     2         69         34       35        0         58#> # ℹ 200 more rows#> # ℹ 8 more variables: cost_train <int>, cost_bus <int>, cost_car <int>,#> #   travel_plane <int>, travel_train <int>, travel_bus <int>, travel_car <int>,#> #   choice <chr>mode_effects<- choice_effects(choice_formula= choice_formula(formula=choice~cost|income+size|travel+wait,error_term="probit"  ),choice_alternatives= choice_alternatives(J=4,alternatives= unique(travel_mode_choice$mode)  ))mode_data<- choice_data(data_frame=travel_mode_choice,format="long",column_choice="choice",column_decider="individual",column_alternative="mode",column_ac_covariates= c("income","size"),column_as_covariates= c("wait","cost","travel"))design_matrices<- design_matrices(mode_data,mode_effects)design_matrices[[1]]#>       cost income_car income_plane income_train size_car size_plane size_train#> bus     25          0            0            0        0          0          0#> car     10         35            0            0        1          0          0#> plane   59          0           35            0        0          1          0#> train   31          0            0           35        0          0          1#>       ASC_car ASC_plane ASC_train travel_bus travel_car travel_plane#> bus         0         0         0        417          0            0#> car         1         0         0          0        180            0#> plane       0         1         0          0          0          100#> train       0         0         1          0          0            0#>       travel_train wait_bus wait_car wait_plane wait_train#> bus              0       35        0          0          0#> car              0        0        0          0          0#> plane            0        0        0         69          0#> train          372        0        0          0         34

Simulated choice

library("choicedata")set.seed(1)sim_effects<- choice_effects(choice_formula= choice_formula(formula=choice~A|B|C,error_term="logit"  ),choice_alternatives= choice_alternatives(J=3,alternatives= c("A","B","C")  ))sim_parameters<- generate_choice_parameters(sim_effects)(sim_data<- generate_choice_data(choice_effects=sim_effects,choice_identifiers= generate_choice_identifiers(N=200),choice_parameters=sim_parameters,choice_type="ranked"))#> # A tibble: 200 × 13#>    deciderID occasionID choice        B choice_A choice_B choice_C     A_A#>  * <chr>     <chr>      <chr>     <dbl>    <int>    <int>    <int>   <dbl>#>  1 1         1          B       1.12           2        1        3  0.576#>  2 2         1          B       0.782          3        1        2 -0.0449#>  3 3         1          B      -0.478          2        1        3  0.0746#>  4 4         1          B      -0.415          3        1        2  0.418#>  5 5         1          B       0.697          2        1        3 -0.394#>  6 6         1          B       0.881          2        1        3  0.557#>  7 7         1          B      -0.367          3        1        2  0.398#>  8 8         1          B       0.0280         3        1        2 -1.04#>  9 9         1          C       0.476          3        2        1 -0.743#> 10 10        1          B       0.00111        2        1        3 -0.710#> # ℹ 190 more rows#> # ℹ 5 more variables: A_B <dbl>, A_C <dbl>, C_A <dbl>, C_B <dbl>, C_C <dbl>sim_likelihood<- choice_likelihood(choice_data=sim_data,choice_effects=sim_effects)objective<-sim_likelihood$objectivetrue_vector<- switch_parameter_space(choice_parameters=sim_parameters,choice_effects=sim_effects)fit<-stats::optim(par=stats::rnorm(length(true_vector)),fn=function(par) {    objective(choice_parameters=par,logarithm=TRUE,negative=TRUE)  })estimated_parameters<- switch_parameter_space(choice_parameters=fit$par,choice_effects=sim_effects)data.frame(dgp=true_vector,estimated=fit$par)#>               dgp   estimated#> beta_1 -1.9810209 -2.04179163#> beta_2  0.5807312  0.04789289#> beta_3 -2.6424897 -3.05406623#> beta_4  5.0447208  4.82873952#> beta_5  1.0419951  0.99061141#> beta_6 -2.5945488 -2.75745549#> beta_7  1.5413860  1.21554512#> beta_8  2.3347877  1.96729481

Installation

install.packages("choicedata")

Related work

Contact

References