clogit no protest

Scripts/clogit/split/clogit_caseA_Control_no_protest.R

+
+#### 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,Treatment==3  )
+database<- filter(database,count_choosen_3!=10 )
+
+
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_A_Control",
+  modelDescr = "clogit_wtp_Case_A_Control",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+

Scripts/clogit/split/clogit_caseA_Opt_Treatment_no_protest.R

+
+#### 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,Treatment==2  )
+database<- filter(database,count_choosen_3!=10 )
+
+
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_A_Opt_Treatment",
+  modelDescr = "clogit_wtp_Case_A_Opt_Treatment",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+

Scripts/clogit/split/clogit_caseA_Treated_no_protest.R

+
+#### 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,Treatment==1  )
+database<- filter(database,count_choosen_3!=10 )
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_A_Treated",
+  modelDescr = "clogit_wtp_Case_A_Treated",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+

Scripts/clogit/split/clogit_caseM_Control_Not_Pred_no_protest.R

+
+#### 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  )
+database<- filter(database,count_choosen_3!=10 )
+
+
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_M_Control_Not_Pred",
+  modelDescr = "clogit_wtp_Case_M_Control_Not_Pred",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+

Scripts/clogit/split/clogit_caseM_Control_Pred_no_protest.R

+
+#### 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)
+database<- filter(database,count_choosen_3!=10 )
+
+
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_M_Control_Pred",
+  modelDescr = "clogit_wtp_Case_M_Control_Pred",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+

Scripts/clogit/split/clogit_caseM_Opt_Treat_Not_Pred_no_protest.R

+
+#### 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  )
+database<- filter(database,count_choosen_3!=10 )
+
+
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_M_Opt_Not_Treat_Pred",
+  modelDescr = "clogit_wtp_Case_M_Opt_Not_Treat_Pred",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+

Scripts/clogit/split/clogit_caseM_Opt_Treat_Pred_no_protest.R

+
+#### 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  )
+database<- filter(database,count_choosen_3!=10 )
+
+
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_M_Opt_Treat_Pred",
+  modelDescr = "clogit_wtp_Case_M_Opt_Treat_Pred",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+

Scripts/clogit/split/clogit_caseM_Treated_Not_Pred_no_protest.R

+
+#### 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  )
+database<- filter(database,count_choosen_3!=10 )
+
+
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_M_Not_Treated_Pred",
+  modelDescr = "clogit_wtp_Case_M_Not_Treated_Pred",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+

Scripts/clogit/split/clogit_caseM_Treated_Pred_no_protest.R

+
+#### 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  )
+database<- filter(database,count_choosen_3!=10 )
+
+
+
+#initialize model 
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+  modelName  = "clogit_wtp_Case_M_Treated_Pred",
+  modelDescr = "clogit_wtp_Case_M_Treated_Pred",
+  indivID    ="id",
+  mixing     = FALSE,
+  HB= FALSE,
+  nCores     = n_cores, 
+  outputDirectory = "Estimation_results/clogit/split/no_protest"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(b_natural = 15,
+              b_walking = -1,
+              b_rent = 0,
+              b_ASC_sq = 0
+)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+
+### 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_rent *(b_natural*Naturalness_1 + b_walking*WalkingDistance_1 - Rent_1)
+  
+  V[['alt2']] =  -b_rent *(b_natural*Naturalness_2 + b_walking*WalkingDistance_2 - Rent_2)
+  
+  V[['alt3']] =  -b_rent *(b_ASC_sq +b_natural*Naturalness_3 + b_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)
+  
+  
+  
+  ### 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)
+