Case C NR Rent INT

Scripts/mxl/mxl_wtp_space_NR_caseC_RentINT_X.R

+#### 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 Rent INT X",
+  modelDescr = "MXL wtp space NR Case C Rent INT X",
+  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_rent_NR = 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_rent_vid1 = 0,
+              mu_rent_vid2 = 0,
+              mu_rent_no_info = 0,
+              mu_rent_info_nv1 = 0,
+              mu_rent_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,
+              sig_ASC_sq = 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", "draws_asc"),
+  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)
+  randcoeff[["b_ASC_sq"]] = ASC_sq + sig_ASC_sq * draws_asc
+  
+  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 + mu_rent_vid1 *Dummy_Video_1 + mu_rent_vid2 * Dummy_Video_2 + mu_rent_no_info * Dummy_no_info +
+                     mu_rent_info_nv1 * Dummy_Info_nv1 + mu_rent_info_nv2 * Dummy_Info_nv2 + mu_rent_NR * Z_Mean_NR) *
+                             (b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 + 
+                              mu_nat_NR * Naturalness_1 * Z_Mean_NR + mu_wd_NR * 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 * WalkingDistance_1 *Dummy_Video_1 + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+                            +  mu_walking_info_nv1 * WalkingDistance_1 *Dummy_Info_nv1 + mu_walking_vid2 * WalkingDistance_1 * Dummy_Video_2
+                            +  mu_walking_info_nv2 * WalkingDistance_1 *Dummy_Info_nv2- Rent_1)
+  
+  V[['alt2']] = -(b_mu_rent + mu_rent_vid1 *Dummy_Video_1 + mu_rent_vid2 * Dummy_Video_2 + mu_rent_no_info * Dummy_no_info +
+                     mu_rent_info_nv1 * Dummy_Info_nv1 + mu_rent_info_nv2 * Dummy_Info_nv2 + mu_rent_NR * Z_Mean_NR)* 
+                             (b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2 + 
+                              mu_nat_NR * Naturalness_2 * Z_Mean_NR + mu_wd_NR * 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 * WalkingDistance_2 *Dummy_Video_1 + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+                            +  mu_walking_info_nv1 * WalkingDistance_2 *Dummy_Info_nv1 + mu_walking_vid2 * WalkingDistance_2 * Dummy_Video_2
+                            +  mu_walking_info_nv2 * WalkingDistance_2 *Dummy_Info_nv2 - Rent_2)
+  
+  V[['alt3']] = -(b_mu_rent + mu_rent_vid1 *Dummy_Video_1 + mu_rent_vid2 * Dummy_Video_2 + mu_rent_no_info * Dummy_no_info +
+                     mu_rent_info_nv1 * Dummy_Info_nv1 + mu_rent_info_nv2 * Dummy_Info_nv2 + mu_rent_NR * Z_Mean_NR) *
+                              (b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3 +
+                              mu_asc_NR  * Z_Mean_NR + mu_nat_NR * Naturalness_3 * Z_Mean_NR +
+                              mu_wd_NR * 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 * WalkingDistance_3 *Dummy_Video_1 + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
+                            +  mu_walking_info_nv1 * WalkingDistance_3 *Dummy_Info_nv1 + mu_walking_vid2 * WalkingDistance_3 * Dummy_Video_2
+                            +  mu_walking_info_nv2 * 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_rentINTX = 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_rentINTX)
+
+