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mxl_wtp_space_NR_caseC_wdlog.R
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mxl_wtp_space_NR_caseC_wdlog.R 7.76 KiB
#### Apollo standard script #####
library(apollo) # Load apollo package
# Test treatment effect
database <- database_full %>%
filter(!is.na(Treatment_new)) %>%
mutate(Dummy_Video_1 = case_when(Treatment_new == 1 ~ 1, TRUE ~ 0),
Dummy_Video_2 = case_when(Treatment_new == 5 ~ 1, TRUE ~ 0),
Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0),
Dummy_Info_nv1 = case_when(Treatment_new == 2 ~1, TRUE~0),
Dummy_Info_nv2 = case_when(Treatment_new == 4 ~1 , TRUE~0))
#initialize model
apollo_initialise()
### Set core controls
apollo_control = list(
modelName = "MXL_wtp_NR_Case_C_wdlog",
modelDescr = "MXL wtp space NR Case C wdlog",
indivID ="id",
mixing = TRUE,
HB= FALSE,
nCores = n_cores,
outputDirectory = "Estimation_results/mxl"
)
##### Define model parameters depending on your attributes and model specification! ####
# set values to 0 for conditional logit model
apollo_beta=c(mu_natural = 15,
mu_walking = -1,
mu_rent = -2,
ASC_sq = 0,
mu_nat_NR = 0,
mu_wd_NR = 0,
mu_asc_NR = 0,
mu_ASC_sq_vid1 = 0,
mu_ASC_sq_vid2 = 0,
mu_ASC_sq_no_info = 0,
mu_ASC_sq_info_nv1 = 0,
mu_ASC_sq_info_nv2 = 0,
mu_nat_vid1 =0,
mu_nat_vid2 = 0,
mu_nat_no_info = 0,
mu_nat_info_nv1 = 0,
mu_nat_info_nv2 = 0,
mu_walking_vid1 =0,
mu_walking_vid2 = 0,
mu_walking_no_info = 0,
mu_walking_info_nv1 = 0,
mu_walking_info_nv2 = 0,
sig_natural = 15,
sig_walking = 2,
sig_rent = 2)
### specify parameters that should be kept fixed, here = none
apollo_fixed = c()
### Set parameters for generating draws, use 2000 sobol draws
apollo_draws = list(
interDrawsType = "sobol",
interNDraws = n_draws,
interUnifDraws = c(),
interNormDraws = c("draws_natural", "draws_walking", "draws_rent"), intraDrawsType = "halton",
intraNDraws = 0,
intraUnifDraws = c(),
intraNormDraws = c()
)
### Create random parameters, define distribution of the parameters
apollo_randCoeff = function(apollo_beta, apollo_inputs){
randcoeff = list()
randcoeff[["b_mu_natural"]] = mu_natural + sig_natural * draws_natural
randcoeff[["b_mu_walking"]] = mu_walking + sig_walking * draws_walking
randcoeff[["b_mu_rent"]] = -exp(mu_rent + sig_rent * draws_rent)
return(randcoeff)
}
### validate
apollo_inputs = apollo_validateInputs()
apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
### Function initialisation: do not change the following three commands
### Attach inputs and detach after function exit
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))
### Create list of probabilities P
P = list()
#### List of utilities (later integrated in mnl_settings below) ####
# Define utility functions here:
V = list()
V[['alt1']] = -b_mu_rent*(b_mu_natural * Naturalness_1 + b_mu_walking * log(WalkingDistance_1) +
mu_nat_NR * Naturalness_1 * Z_Mean_NR + mu_wd_NR * log(WalkingDistance_1) * Z_Mean_NR +
mu_nat_vid1 * Naturalness_1 *Dummy_Video_1 + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ mu_nat_info_nv1 * Naturalness_1 *Dummy_Info_nv1 + mu_nat_vid2 * Naturalness_1 * Dummy_Video_2
+ mu_nat_info_nv2 * Naturalness_1 *Dummy_Info_nv2 +
mu_walking_vid1 * log(WalkingDistance_1) *Dummy_Video_1 + mu_walking_no_info * log(WalkingDistance_1) * Dummy_no_info
+ mu_walking_info_nv1 * log(WalkingDistance_1) *Dummy_Info_nv1 + mu_walking_vid2 * log(WalkingDistance_1) * Dummy_Video_2
+ mu_walking_info_nv2 * log(WalkingDistance_1) *Dummy_Info_nv2- Rent_1)
V[['alt2']] = -b_mu_rent*(b_mu_natural * Naturalness_2 + b_mu_walking * log(WalkingDistance_2) +
mu_nat_NR * Naturalness_2 * Z_Mean_NR + mu_wd_NR * log(WalkingDistance_2) * Z_Mean_NR +
mu_nat_vid1 * Naturalness_2 *Dummy_Video_1 + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ mu_nat_info_nv1 * Naturalness_2 *Dummy_Info_nv1 + mu_nat_vid2 * Naturalness_2 * Dummy_Video_2
+ mu_nat_info_nv2 * Naturalness_2 *Dummy_Info_nv2+
mu_walking_vid1 * log(WalkingDistance_2) *Dummy_Video_1 + mu_walking_no_info * log(WalkingDistance_2) * Dummy_no_info
+ mu_walking_info_nv1 * log(WalkingDistance_2) *Dummy_Info_nv1 + mu_walking_vid2 * log(WalkingDistance_2) * Dummy_Video_2
+ mu_walking_info_nv2 * log(WalkingDistance_2) *Dummy_Info_nv2 - Rent_2)
V[['alt3']] = -b_mu_rent*(ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * log(WalkingDistance_3) +
mu_asc_NR * ASC_sq * Z_Mean_NR + mu_nat_NR * Naturalness_3 * Z_Mean_NR +
mu_wd_NR * log(WalkingDistance_3) * Z_Mean_NR +
mu_nat_vid1 * Naturalness_3 *Dummy_Video_1 + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ mu_nat_info_nv1 * Naturalness_3 *Dummy_Info_nv1 + mu_nat_vid2 * Naturalness_3 * Dummy_Video_2
+ mu_nat_info_nv2 * Naturalness_3 *Dummy_Info_nv2+
mu_walking_vid1 * log(WalkingDistance_3) *Dummy_Video_1 + mu_walking_no_info * log(WalkingDistance_3) * Dummy_no_info
+ mu_walking_info_nv1 * log(WalkingDistance_3) *Dummy_Info_nv1 + mu_walking_vid2 * log(WalkingDistance_3) * Dummy_Video_2
+ mu_walking_info_nv2 * log(WalkingDistance_3) *Dummy_Info_nv2
+ mu_ASC_sq_vid1 * Dummy_Video_1 + mu_ASC_sq_vid2 * Dummy_Video_2
+ mu_ASC_sq_no_info * Dummy_no_info + mu_ASC_sq_info_nv1 * Dummy_Info_nv1
+ mu_ASC_sq_info_nv2 * Dummy_Info_nv2 - Rent_3)
### Define settings for MNL model component
mnl_settings = list(
alternatives = c(alt1=1, alt2=2, alt3=3),
avail = 1, # all alternatives are available in every choice choiceVar = choice,
V = V#, # tell function to use list vector defined above
)
### Compute probabilities using MNL model
P[['model']] = apollo_mnl(mnl_settings, functionality)
### Take product across observation for same individual
P = apollo_panelProd(P, apollo_inputs, functionality)
### Average across inter-individual draws - nur bei Mixed Logit!
P = apollo_avgInterDraws(P, apollo_inputs, functionality)
### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)
}
# ################################################################# #
#### MODEL ESTIMATION ##
# ################################################################# #
# estimate model with bfgs algorithm
mxl_wtp_NR_case_c_wdlog = apollo_estimate(apollo_beta, apollo_fixed,
apollo_probabilities, apollo_inputs,
estimate_settings=list(maxIterations=400,
estimationRoutine="bfgs",
hessianRoutine="analytic"))
# ################################################################# #
#### MODEL OUTPUTS ##
# ################################################################# #
apollo_saveOutput(mxl_wtp_NR_case_c_wdlog)