diff --git a/Scripts/mxl/Split_samples/mxl_wtp_space_not_tr_A_NR.R b/Scripts/mxl/Split_samples/mxl_wtp_space_not_tr_A_NR.R
new file mode 100644
index 0000000000000000000000000000000000000000..b96c49bd26358c82387df6668f49398964e49a9a
--- /dev/null
+++ b/Scripts/mxl/Split_samples/mxl_wtp_space_not_tr_A_NR.R
@@ -0,0 +1,148 @@
+#### 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)) %>%
+ filter(Treatment_A == "Not_Treated")
+
+ #initialize model
+
+ apollo_initialise()
+
+
+ ### Set core controls
+ apollo_control = list(
+ modelName = "MXL_wtp Not_Treated A NR",
+ modelDescr = "MXL wtp space Not_Treated A NR",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/Split_samples"
+ )
+
+ ##### 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,
+ mu_nat_NR = 0,
+ mu_walking_NR = 0,
+ mu_asc_NR = 0,
+ ASC_sq = 0,
+ sig_natural = 15,
+ sig_walking = 2,
+ sig_rent = 2,
+ sig_ASC_sq = 0)
+
+ ### 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*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ mu_nat_NR * Naturalness_1 * Z_Mean_NR + mu_walking_NR * WalkingDistance_1 * Z_Mean_NR -
+ Rent_1)
+
+ V[['alt2']] = -b_mu_rent*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2 +
+ mu_nat_NR * Naturalness_2 * Z_Mean_NR + mu_walking_NR * WalkingDistance_2 * Z_Mean_NR -
+ Rent_2)
+
+ V[['alt3']] = -b_mu_rent*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3 +
+ mu_nat_NR * Naturalness_3 * Z_Mean_NR + mu_walking_NR * WalkingDistance_3 * Z_Mean_NR -
+ Rent_3 + mu_asc_NR * Z_Mean_NR)
+
+
+ ### 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_not_tr_a_nr = 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_not_tr_a_nr)
+
+
diff --git a/Scripts/mxl/Split_samples/mxl_wtp_space_tr_A_NR.R b/Scripts/mxl/Split_samples/mxl_wtp_space_tr_A_NR.R
new file mode 100644
index 0000000000000000000000000000000000000000..28870ec5b24710f90a3cbd184534cd9b597873dc
--- /dev/null
+++ b/Scripts/mxl/Split_samples/mxl_wtp_space_tr_A_NR.R
@@ -0,0 +1,149 @@
+#### 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)) %>%
+ filter(Treatment_A == "Treated")
+
+ #initialize model
+
+ apollo_initialise()
+
+
+ ### Set core controls
+ apollo_control = list(
+ modelName = "MXL_wtp Treated A NR",
+ modelDescr = "MXL wtp space Treated A NR",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/Split_samples"
+ )
+
+ ##### 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_walking_NR = 0,
+ mu_asc_NR = 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*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ mu_nat_NR * Naturalness_1 * Z_Mean_NR + mu_walking_NR * WalkingDistance_1 * Z_Mean_NR -
+ Rent_1)
+
+ V[['alt2']] = -b_mu_rent*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2 +
+ mu_nat_NR * Naturalness_2 * Z_Mean_NR + mu_walking_NR * WalkingDistance_2 * Z_Mean_NR -
+ Rent_2)
+
+ V[['alt3']] = -b_mu_rent*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3 +
+ mu_nat_NR * Naturalness_3 * Z_Mean_NR + mu_walking_NR * WalkingDistance_3 * Z_Mean_NR -
+ Rent_3 + mu_asc_NR * Z_Mean_NR)
+
+
+
+ ### 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_tr_a_nr = 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_tr_a_nr)
+
+
diff --git a/Scripts/mxl/Split_samples/mxl_wtp_space_vol_treat.R b/Scripts/mxl/Split_samples/mxl_wtp_space_vol_treat.R
new file mode 100644
index 0000000000000000000000000000000000000000..8177fbe1d15e9860278bf60372ff976de482cb67
--- /dev/null
+++ b/Scripts/mxl/Split_samples/mxl_wtp_space_vol_treat.R
@@ -0,0 +1,136 @@
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+# Test treatment effect
+
+database <- database_full %>%
+ filter(Treatment_D == "Vol. Treated")
+
+ #initialize model
+
+ apollo_initialise()
+
+
+ ### Set core controls
+ apollo_control = list(
+ modelName = "MXL_wtp Vol_Treat",
+ modelDescr = "MXL wtp space Vol_Treat",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/Split_samples"
+ )
+
+ ##### 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,
+ sig_natural = 15,
+ sig_walking = 2,
+ sig_rent = 2,
+ sig_ASC_sq = 0)
+
+ ### 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*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 -
+ Rent_1)
+
+ V[['alt2']] = -b_mu_rent*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2 -
+ Rent_2)
+
+ V[['alt3']] = -b_mu_rent*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3 -
+ 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_Vol_Treat = 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_Vol_Treat)
+
+
diff --git a/Scripts/mxl/Split_samples/mxl_wtp_space_vol_treat_NR.R b/Scripts/mxl/Split_samples/mxl_wtp_space_vol_treat_NR.R
new file mode 100644
index 0000000000000000000000000000000000000000..dfac0a3161ac0c63b5ced965063899dd063c7d1b
--- /dev/null
+++ b/Scripts/mxl/Split_samples/mxl_wtp_space_vol_treat_NR.R
@@ -0,0 +1,143 @@
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+# Test treatment effect
+
+database <- database_full %>%
+ filter(!is.na(Treatment_new)) %>%
+ filter(Treatment_D == "Vol. Treated")
+
+ #initialize model
+
+ apollo_initialise()
+
+
+ ### Set core controls
+ apollo_control = list(
+ modelName = "MXL_wtp Vol_Treat NR",
+ modelDescr = "MXL wtp space Vol_Treat NR",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/Split_samples"
+ )
+
+ ##### 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,
+ mu_nat_NR = 0,
+ mu_walking_NR = 0,
+ mu_asc_NR = 0,
+ ASC_sq = 0,
+ sig_natural = 15,
+ sig_walking = 2,
+ sig_rent = 2,
+ sig_ASC_sq = 0)
+
+ ### 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*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ mu_nat_NR * Naturalness_1 * Z_Mean_NR + mu_walking_NR * WalkingDistance_1 * Z_Mean_NR -
+ Rent_1)
+
+ V[['alt2']] = -b_mu_rent*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2 +
+ mu_nat_NR * Naturalness_2 * Z_Mean_NR + mu_walking_NR * WalkingDistance_2 * Z_Mean_NR -
+ Rent_2)
+
+ V[['alt3']] = -b_mu_rent*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3 +
+ mu_nat_NR * Naturalness_3 * Z_Mean_NR + mu_walking_NR * WalkingDistance_3 * Z_Mean_NR -
+ Rent_3 + mu_asc_NR * Z_Mean_NR)
+
+
+ ### 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_Vol_Treat_NR = 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_Vol_Treat_NR)
+
+