diff --git a/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Control_Not_Pred.R b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Control_Not_Pred.R
new file mode 100644
index 0000000000000000000000000000000000000000..07ee9eb0b7d519a190714dd68a347e5f46f714ac
--- /dev/null
+++ b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Control_Not_Pred.R
@@ -0,0 +1,156 @@
+
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+data_predictions1 <- readRDS("Data/predictions.RDS")
+data_predictions2 <- readRDS("Data/predictions_labeled.RDS")
+
+data_predictions <- bind_rows(data_predictions1, data_predictions2)
+
+database <- left_join(database_full, data_predictions, by="id")
+
+
+
+database <- database %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0)) %>%
+ mutate(Dummy_Treated_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Treated_Not_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 0 ~1, TRUE~0)) %>%
+ mutate(Dummy_Control_Not_Pred = case_when(Treatment_new == 6 & PredictedGroup == 0 ~1, TRUE~0),
+ Dummy_Opt_Treat_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Opt_Treat_Not_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 0 ~1, TRUE~0))
+table(database$Treatment)
+database<- filter(database,Dummy_Control_Not_Pred==1 )
+
+
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Control_Not_Pred",
+ modelDescr = "MXL_wtp_Control_Not_Pred",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/prediction/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
+
+model = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(model)
+
+apollo_modelOutput(model)
+
diff --git a/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Control_Pred.R b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Control_Pred.R
new file mode 100644
index 0000000000000000000000000000000000000000..1f1b6c04a747dfdd0c51238e8d5884e906e78511
--- /dev/null
+++ b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Control_Pred.R
@@ -0,0 +1,157 @@
+
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+data_predictions1 <- readRDS("Data/predictions.RDS")
+data_predictions2 <- readRDS("Data/predictions_labeled.RDS")
+
+data_predictions <- bind_rows(data_predictions1, data_predictions2)
+
+database <- left_join(database_full, data_predictions, by="id")
+
+
+
+database <- database %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0)) %>%
+ mutate(Dummy_Treated_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Treated_Not_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 0 ~1, TRUE~0)) %>%
+ mutate(Dummy_Control_Not_Pred = case_when(Treatment_new == 6 & PredictedGroup == 0 ~1, TRUE~0),
+ Dummy_Opt_Treat_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Opt_Treat_Not_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 0 ~1, TRUE~0))
+table(database$Treatment)
+database<- filter(database,Dummy_Control_Not_Pred==0 & Dummy_Treated_Pred==0 &
+ Dummy_Treated_Not_Pred == 0 & Dummy_Opt_Treat_Pred==0 &
+ Dummy_Opt_Treat_Not_Pred == 0)
+
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Control_Pred",
+ modelDescr = "MXL_wtp_Control_Pred",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/prediction/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
+
+model = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(model)
+
+apollo_modelOutput(model)
+
diff --git a/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Opt_Not_Pred.R b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Opt_Not_Pred.R
new file mode 100644
index 0000000000000000000000000000000000000000..484d1fd2c6bb631559313859713e7272a4bdd336
--- /dev/null
+++ b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Opt_Not_Pred.R
@@ -0,0 +1,156 @@
+
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+data_predictions1 <- readRDS("Data/predictions.RDS")
+data_predictions2 <- readRDS("Data/predictions_labeled.RDS")
+
+data_predictions <- bind_rows(data_predictions1, data_predictions2)
+
+database <- left_join(database_full, data_predictions, by="id")
+
+
+
+database <- database %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0)) %>%
+ mutate(Dummy_Treated_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Treated_Not_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 0 ~1, TRUE~0)) %>%
+ mutate(Dummy_Control_Not_Pred = case_when(Treatment_new == 6 & PredictedGroup == 0 ~1, TRUE~0),
+ Dummy_Opt_Treat_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Opt_Treat_Not_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 0 ~1, TRUE~0))
+table(database$Treatment)
+database<- filter(database,Dummy_Opt_Treat_Not_Pred==1 )
+
+
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Opt_Not_Pred",
+ modelDescr = "MXL_wtp_Opt_Not_Pred",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/prediction/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
+
+model = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(model)
+
+apollo_modelOutput(model)
+
diff --git a/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Opt_Pred.R b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Opt_Pred.R
new file mode 100644
index 0000000000000000000000000000000000000000..0785b9d988270f61cc91e9529932869e2ae71a6e
--- /dev/null
+++ b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Opt_Pred.R
@@ -0,0 +1,156 @@
+
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+data_predictions1 <- readRDS("Data/predictions.RDS")
+data_predictions2 <- readRDS("Data/predictions_labeled.RDS")
+
+data_predictions <- bind_rows(data_predictions1, data_predictions2)
+
+database <- left_join(database_full, data_predictions, by="id")
+
+
+
+database <- database %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0)) %>%
+ mutate(Dummy_Treated_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Treated_Not_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 0 ~1, TRUE~0)) %>%
+ mutate(Dummy_Control_Not_Pred = case_when(Treatment_new == 6 & PredictedGroup == 0 ~1, TRUE~0),
+ Dummy_Opt_Treat_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Opt_Treat_Not_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 0 ~1, TRUE~0))
+table(database$Treatment)
+database<- filter(database,Dummy_Opt_Treat_Pred==1 )
+
+
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Opt_Pred",
+ modelDescr = "MXL_wtp_Opt_Pred",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/prediction/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
+
+model = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(model)
+
+apollo_modelOutput(model)
+
diff --git a/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Treatment_Not_Pred.R b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Treatment_Not_Pred.R
new file mode 100644
index 0000000000000000000000000000000000000000..03667eaf757cf9ed466c2a5b1671e6944f8ec800
--- /dev/null
+++ b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Treatment_Not_Pred.R
@@ -0,0 +1,156 @@
+
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+data_predictions1 <- readRDS("Data/predictions.RDS")
+data_predictions2 <- readRDS("Data/predictions_labeled.RDS")
+
+data_predictions <- bind_rows(data_predictions1, data_predictions2)
+
+database <- left_join(database_full, data_predictions, by="id")
+
+
+
+database <- database %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0)) %>%
+ mutate(Dummy_Treated_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Treated_Not_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 0 ~1, TRUE~0)) %>%
+ mutate(Dummy_Control_Not_Pred = case_when(Treatment_new == 6 & PredictedGroup == 0 ~1, TRUE~0),
+ Dummy_Opt_Treat_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Opt_Treat_Not_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 0 ~1, TRUE~0))
+table(database$Treatment)
+database<- filter(database,Dummy_Treated_Not_Pred==1 )
+
+
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Treated_Not_Pred",
+ modelDescr = "MXL_wtp_Treated_Not_Pred",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/prediction/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
+
+model = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(model)
+
+apollo_modelOutput(model)
+
diff --git a/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Treatment_Pred.R b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Treatment_Pred.R
new file mode 100644
index 0000000000000000000000000000000000000000..76b11f4fc54e4f86f0a6debe1532935b1565bfcd
--- /dev/null
+++ b/Scripts/mxl/Prediction models/Split_samples/mxl_wtp_space_caseM_Treatment_Pred.R
@@ -0,0 +1,156 @@
+
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+data_predictions1 <- readRDS("Data/predictions.RDS")
+data_predictions2 <- readRDS("Data/predictions_labeled.RDS")
+
+data_predictions <- bind_rows(data_predictions1, data_predictions2)
+
+database <- left_join(database_full, data_predictions, by="id")
+
+
+
+database <- database %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
+ Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0)) %>%
+ mutate(Dummy_Treated_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Treated_Not_Pred = case_when(Dummy_Treated == 1 & PredictedGroup == 0 ~1, TRUE~0)) %>%
+ mutate(Dummy_Control_Not_Pred = case_when(Treatment_new == 6 & PredictedGroup == 0 ~1, TRUE~0),
+ Dummy_Opt_Treat_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 1 ~1, TRUE~0),
+ Dummy_Opt_Treat_Not_Pred = case_when(Treatment_A == "Vol_Treated" & PredictedGroup == 0 ~1, TRUE~0))
+table(database$Treatment)
+database<- filter(database,Dummy_Treated_Pred==1 )
+
+
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Treated_Pred",
+ modelDescr = "MXL_wtp_Treated_Pred",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl/prediction/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
+
+model = apollo_estimate(apollo_beta, apollo_fixed,
+ apollo_probabilities, apollo_inputs,
+ estimate_settings=list(maxIterations=400,
+ estimationRoutine="bfgs",
+ hessianRoutine="analytic"))
+
+
+
+# ################################################################# #
+#### MODEL OUTPUTS ##
+# ################################################################# #
+apollo_saveOutput(model)
+
+apollo_modelOutput(model)
+
diff --git a/Scripts/visualize_distr_spli.R b/Scripts/visualize_distr_spli.R
new file mode 100644
index 0000000000000000000000000000000000000000..e39949caa4bacb1d93ee429b8774cd4347094536
--- /dev/null
+++ b/Scripts/visualize_distr_spli.R
@@ -0,0 +1,55 @@
+# Load models
+MXL_wtp_Control_Not_Pred_model <- readRDS("C:/nextcloud/Hot_Topic_Cool_Choices/Estimation_results/mxl/prediction/Split_samples/MXL_wtp_Control_Not_Pred_model.rds")
+MXL_wtp_Control_Pred_model <- readRDS("C:/nextcloud/Hot_Topic_Cool_Choices/Estimation_results/mxl/prediction/Split_samples/MXL_wtp_Control_Pred_model.rds")
+MXL_wtp_Opt_Not_Pred_model <- readRDS("C:/nextcloud/Hot_Topic_Cool_Choices/Estimation_results/mxl/prediction/Split_samples/MXL_wtp_Opt_Not_Pred_model.rds")
+MXL_wtp_Opt_Pred_model <- readRDS("C:/nextcloud/Hot_Topic_Cool_Choices/Estimation_results/mxl/prediction/Split_samples/MXL_wtp_Opt_Pred_model.rds")
+MXL_wtp_Treated_Not_Pred_model <- readRDS("C:/nextcloud/Hot_Topic_Cool_Choices/Estimation_results/mxl/prediction/Split_samples/MXL_wtp_Treated_Not_Pred_model.rds")
+MXL_wtp_Treated_Pred_model <- readRDS("C:/nextcloud/Hot_Topic_Cool_Choices/Estimation_results/mxl/prediction/Split_samples/MXL_wtp_Treated_Pred_model.rds")
+
+# List of models
+models <- list(
+ "Treated Not Pred" = MXL_wtp_Treated_Not_Pred_model,
+ "Control Not Pred" = MXL_wtp_Control_Not_Pred_model,
+ "Control Pred" = MXL_wtp_Control_Pred_model,
+ "Opt Not Pred" = MXL_wtp_Opt_Not_Pred_model,
+ "Opt Pred" = MXL_wtp_Opt_Pred_model,
+ "Treated Pred" = MXL_wtp_Treated_Pred_model
+)
+
+# Define the x-range for plotting (1000 values)
+x_range <- seq(-10, 100, length.out = 1000)
+
+# Define colors and line types for each model
+colors <- c("blue", "red", "green", "purple", "orange", "brown")
+line_types <- 1:6
+
+# Initialize the plot with the first model
+model <- models[["Treated Not Pred"]]
+coef <- summary(model)$estimate
+mean <- coef["mu_natural", "Estimate"]
+sd <- coef["sig_natural", "Estimate"]
+y <- dnorm(x_range, mean, sd)
+
+plot(x_range, y, type = "l", col = colors[1], lwd = 2, lty = line_types[1],
+ ylim = c(0, max(y)), xlab = "x", ylab = "Density",
+ main = "Normal Distributions from Multiple MXL Models")
+
+# Loop through the remaining models and add their distributions to the plot
+for (i in 2:length(models)) {
+ model <- models[[i]]
+
+ # Extract mean and standard deviation for 'mu_natural' and 'sig_natural'
+ coef <- summary(model)$estimate
+ mean <- coef["mu_natural", "Estimate"]
+ sd <- coef["sig_natural", "Estimate"]
+
+ # Compute the normal distribution based on extracted mean and sd
+ y <- dnorm(x_range, mean, sd)
+
+ # Add the distribution to the existing plot
+ lines(x_range, y, col = colors[i], lwd = 2, lty = line_types[i])
+}
+
+# Add a legend
+legend("topright", inset = c(-0.25, 0), legend = names(models), col = colors,
+ lty = line_types, lwd = 2, xpd = TRUE)