diff --git a/Scripts/MAKE_FILE.R b/Scripts/MAKE_FILE.R
index be870b69e147a6e6cef2f88f287922bc7f55f260..a165a46abbee8521fdece9f5dbd32a626aab29cc 100644
--- a/Scripts/MAKE_FILE.R
+++ b/Scripts/MAKE_FILE.R
@@ -1,100 +1,105 @@
-rm(list=ls())
-library(tidyverse)
-library(tidylog)
-library(apollo)
-library(reshape2)
-library(xtable)
-library(stargazer)
-library(texreg)
-#test
-# Set values for estimation in Apollo
-n_draws <- 2000
-n_cores <- min(parallel::detectCores()-1, 25)
-
-# Load data
-load("Data/database_full.RData")
-load("Data/database.RData")
-
-# Data preparation
-source("Scripts/data_prep.R")
-source("Scripts/treatment.R")
-
-####### Estimate models ######
-
-### Logit
-source("Scripts/logit/chr_vol_treat.R")
-source("Scripts/logit/protesters.R")
-
-### OLS
-source("Scripts/ols/ols_time_spent.R")
-source("Scripts/ols/ols_quiz.R")
-source("Scripts/ols/ols_opt_out.R")
-source("Scripts/ols/ols_nr.R")
-source("Scripts/ols/ols_consequentiality.R")
-
-##### Conditional Logits #####
-#source("Scripts/clogit.R")
-#source("Scripts/clogit_wtp.R")
-
-##### Mixed Logit Models ######
-
-#source("Scripts/mxl/mxl_wtp_space.R")
-#source("Scripts/mxl/mxl_wtp_space_4d.R")
-#source("Scripts/mxl/mxl_wtp_space_interact_all.R")
-#source("Scripts/mxl/mxl_socio_int.R")
-#source("Scripts/mxl/mxl_treatment_time.R")
-#source("Scripts/mxl/mxl_treatment_time_interaction.R")
-#source("Scripts/mxl/mxl_treatment_time_Dummies.R")
-#source("Scripts/mxl/mxl_wtp_space_interact_everything.R")
-#source("Scripts/mxl/mxl_wtp_space_4d_interact_everything.R")
-#############################
-
-##### Load models ############
-
-mxl_wtp <- apollo_loadModel("Estimation_results/mxl/MXL_wtp")
-mxl_wtp_4d <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_4d")
-mxl_wtp_all_int <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_interact_all")
-mxl_wtp_socio <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_socio_int")
-mxl_wtp_tt <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_treatment_time")
-mxl_wtp_tt_interaction <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_treatment_time_interaction")
-mxl_wtp_time_groups <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_time_groups")
-mxl_wtp_everything <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_interact_everything")
-mxl_wtp_case_a <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case A")
-mxl_wtp_case_a_NR <- apollo_loadModel("Estimation_results/mxl/MXL_wtp NR A")
-mxl_wtp_case_b <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case B")
-mxl_wtp_case_b_NR <- apollo_loadModel("Estimation_results/mxl/MXL_wtp NR B")
-mxl_wtp_case_c <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_C")
-mxl_wtp_case_c_NR <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_NR_Case_C")
-
-# rent interactions models
-mxl_wtp_case_a_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case A Rent Int")
-mxl_wtp_case_b_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case B Rent Int")
-mxl_wtp_case_c_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_C Rent INT")
-mxl_wtp_case_d_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_D")
-
-# rent interactions models NR
-mxl_wtp_NR_case_a_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp NR A Rent INT")
-mxl_wtp_NR_case_c_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_NR_Case_C RENT INT X")
-
-# Alternative case
-case_d <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case D Rent Int")
-
-##############################
-
-# Model analysis
-source("Scripts/visualize_models.R")
-
-source("Scripts/compare_split_samples.R")
-
-source("Scripts/create_tables.R")
-
-source("Scripts/interaction_plots_presi.R")
-
-
-### Old models ###
-
-
-# # without protesters
-# mxl_wtp_case_a_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp Case A prot")
-# mxl_wtp_case_b_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp Case B prot")
+rm(list=ls())
+library(tidyverse)
+library(tidylog)
+library(apollo)
+library(reshape2)
+library(xtable)
+library(stargazer)
+library(texreg)
+#test
+# Set values for estimation in Apollo
+n_draws <- 2000
+n_cores <- min(parallel::detectCores()-1, 25)
+
+# Load data
+load("Data/database_full.RData")
+load("Data/database.RData")
+
+# Data preparation
+source("Scripts/data_prep.R")
+source("Scripts/treatment.R")
+
+####### Estimate models ######
+
+### Logit
+source("Scripts/logit/chr_vol_treat.R")
+source("Scripts/logit/protesters.R")
+
+### OLS
+source("Scripts/ols/ols_time_spent.R")
+source("Scripts/ols/ols_quiz.R")
+source("Scripts/ols/ols_opt_out.R")
+source("Scripts/ols/ols_nr.R")
+source("Scripts/ols/ols_consequentiality.R")
+
+##### Conditional Logits #####
+#source("Scripts/clogit.R")
+#source("Scripts/clogit_wtp.R")
+
+##### Mixed Logit Models ######
+
+#source("Scripts/mxl/mxl_wtp_space.R")
+#source("Scripts/mxl/mxl_wtp_space_4d.R")
+#source("Scripts/mxl/mxl_wtp_space_interact_all.R")
+#source("Scripts/mxl/mxl_socio_int.R")
+#source("Scripts/mxl/mxl_treatment_time.R")
+#source("Scripts/mxl/mxl_treatment_time_interaction.R")
+#source("Scripts/mxl/mxl_treatment_time_Dummies.R")
+#source("Scripts/mxl/mxl_wtp_space_interact_everything.R")
+#source("Scripts/mxl/mxl_wtp_space_4d_interact_everything.R")
+#############################
+
+##### Load models ############
+
+mxl_wtp <- apollo_loadModel("Estimation_results/mxl/MXL_wtp")
+mxl_wtp_4d <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_4d")
+mxl_wtp_all_int <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_interact_all")
+mxl_wtp_socio <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_socio_int")
+mxl_wtp_tt <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_treatment_time")
+mxl_wtp_tt_interaction <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_treatment_time_interaction")
+mxl_wtp_time_groups <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_time_groups")
+mxl_wtp_everything <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_interact_everything")
+mxl_wtp_case_a <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case A")
+mxl_wtp_case_a_NR <- apollo_loadModel("Estimation_results/mxl/MXL_wtp NR A")
+mxl_wtp_case_b <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case B")
+mxl_wtp_case_b_NR <- apollo_loadModel("Estimation_results/mxl/MXL_wtp NR B")
+mxl_wtp_case_c <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_C")
+mxl_wtp_case_c_NR <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_NR_Case_C")
+
+# rent interactions models
+mxl_wtp_case_a_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case A Rent Int")
+mxl_wtp_case_b_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case B Rent Int")
+mxl_wtp_case_c_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_C Rent INT")
+mxl_wtp_case_d_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_D")
+
+# rent interactions models NR
+mxl_wtp_NR_case_a_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp NR A Rent INT")
+mxl_wtp_NR_case_c_rentINT <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_NR_Case_C RENT INT X")
+
+# Alternative case
+case_d <- apollo_loadModel("Estimation_results/mxl/MXL_wtp Case D Rent Int")
+
+# New Case Text Video merged
+new_case_b <- apollo_loadModel("Estimation_results/mxl/MXL_wtp_Case_D")
+
+new_case_b_NR <- apollo_loadModel("Estimation_results/mxl/D_NR")
+
+##############################
+
+# Model analysis
+source("Scripts/visualize_models.R")
+
+source("Scripts/compare_split_samples.R")
+
+source("Scripts/create_tables.R")
+
+source("Scripts/interaction_plots_presi.R")
+
+
+### Old models ###
+
+
+# # without protesters
+# mxl_wtp_case_a_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp Case A prot")
+# mxl_wtp_case_b_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp Case B prot")
# mxl_wtp_case_c_prot <- apollo_loadModel("Estimation_results/mxl/without_protesters/MXL_wtp_Case_C prot")
\ No newline at end of file
diff --git a/Scripts/create_tables.R b/Scripts/create_tables.R
index 45600ebe9b2d13d0c9d73a73b421b557e1109bb7..266d226f2f182fa92e9d83fe612bec792d40effe 100644
--- a/Scripts/create_tables.R
+++ b/Scripts/create_tables.R
@@ -1,203 +1,252 @@
-library(choiceTools)
-
-dir.create("Tables/mxl")
-dir.create("Tables/logit")
-dir.create("Tables/ols/")
-
-
-list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Optional Treatment",
- "as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
- "as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
- "as.factor(Treatment_C)Video 2" = "Video 2", "as.factor(Treatment_D)Treated" = "Treated", "as.factor(Treatment_D)Vol. Treated" = "Vol. Treated",
- "as.factor(Treatment_D)No Info 2" = "No Info",
- "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
- "Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree")
-# OLS A
-texreg(l=list(ols_percentage_correct_control_A, ols_time_spent_control_A, ols_time_cc_control_A, conseq_model_control_A),
- custom.model.names = c("Quiz", "Interview Time", "CC Time", "Cons. Score"),
- custom.header = list("Model 1A" = 1:1, "Model 2A" = 2:2, "Model 3A" = 3:3, "Model 4A" = 4:4),
- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.note = "Notes: (i) The dependent variables examined in this regression analysis are as follows:
- Model 1A represents the percentage of correct quiz questions, Model 2A refers to net interview time,
- Model 3A denotes choice card time, and Model 4A represents consequentiality score.
- (ii) The variables included in the analysis are as follows: Treated is a dummy variable indicating membership
- in the obligatory treated group, Optional Treatment is a dummy variable indicating membership in the group with
- the choice to receive treatment or not, with the reference group being Non-Treated. NR-Index represents the z-standardized
- natural relatedness index. Female is a dummy variable denoting gender, Age has been mean-centered and measured in years,
- Income is a continuous variable indicating a transition from one income group to the next higher, and University Degree is
- a dummy variable indicating whether an individual holds a university degree; (iii) %stars and standard errors in parentheses.",
- label = "tab:olsA",
- caption = "Results of OLS regressions for Scenario Case A.",
- file="Tables/ols/ols_A.tex")
-
-# OLS D
-texreg(l=list(ols_percentage_correct_control_D, ols_time_spent_control_D, ols_time_cc_control_D, conseq_model_control_D),
- custom.model.names = c("Quiz", "Interview Time", "CC Time", "Cons. Score"),
- custom.header = list("Model 1B" = 1:1, "Model 2B" = 2:2, "Model 3B" = 3:3, "Model 4B" = 4:4),
- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.note = "Notes: (i) The dependent variables examined in this regression analysis are as follows:
- Model 1B represents the percentage of correct quiz questions, Model 2B refers to net interview time,
- Model 3B denotes choice card time, and Model 4B represents consequentiality score.
- (ii) The variables included in the analysis are as follows: Treated is a dummy variable indicating membership
- in the obligatory treated group, Vol. Treated is a dummy variable indicating the group that voluntarily chose the optional treatment,
- while No Info indicates the group that did not opt for the treatment, with the reference group being Non-Treated. NR-Index represents the z-standardized
- natural relatedness index. Female is a dummy variable denoting gender, Age has been mean-centered and measured in years,
- Income is a continuous variable indicating a transition from one income group to the next higher, and University Degree is
- a dummy variable indicating whether an individual holds a university degree; (iii) %stars and standard errors in parentheses.",
- label = "tab:olsD",
- caption = "Results of OLS regressions for Scenario Case B.",
- file="Tables/ols/ols_D.tex")
-
-# Manipulation check
-texreg(l=list(ols_percentage_correct_A, ols_percentage_correct_control_A, ols_percentage_correct_C, ols_percentage_correct_control_C),
- custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
- custom.header = list("Dependent Variable: Percentage of correct quiz statements" = 1:4),
- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.note = "%stars. Standard errors in parentheses.",
- label = "tab:mani",
- caption = "Results of OLS on percentage of correct quiz statements.",
- file="Tables/ols/manipulation.tex")
-
-
-# Net interview time
-texreg(l=list(ols_time_spent_A, ols_time_spent_control_A, ols_time_spent_C, ols_time_spent_control_C),
- custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
- custom.header = list("Dependent variable: Net interview time" = 1:4),
- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.note = "%stars. Standard errors in parentheses.",
- label = "tab:net_int",
- caption = "Results of OLS on net interview time.",
- file="Tables/ols/interviewtime.tex")
-
-
-# CC Time
-texreg(l=list(ols_time_cc_A, ols_time_cc_control_A, ols_time_cc_C, ols_time_cc_control_C),
- custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
- custom.header = list("Dependent variable: Mean choice card time" = 1:4),
- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.note = "%stars. Standard errors in parentheses.",
- label = "tab:cctime",
- caption = "Results of OLS on mean choice card time.",
- file="Tables/ols/cctime.tex")
-
-# Consequentiality
-texreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_model_control_C),
- custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
- custom.header = list("Dependent variable: Consequentiality score" = 1:4),
- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.note = "%stars. Standard errors in parentheses.",
- label = "tab:conseq",
- caption = "Results of OLS on consequentiality score.",
- file="Tables/ols/consequentiality.tex")
-
-# Opt Out
-texreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C),
- custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
- custom.header = list("Dependent variable: Number of opt-out choices" = 1:4),
- custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.note = "%stars. Standard errors in parentheses.",
- label = "tab:optout",
- caption = "Results of OLS on number of opt-out choices.",
- file="Tables/ols/optout.tex")
-
-# NR
-texreg(l=list(nr_model_treat_A),
- custom.model.names = c("OLS regression"),
- custom.header = list("Dependent variable: NR-Index" = 1),
- custom.coef.map = list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Vol. Treated",
- "as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
- "as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
- "as.factor(Treatment_C)Video 2" = "Video 2", "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
- "Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree", "Kids_Dummy" = "Children",
- "Naturalness_SQ" = "Naturalness SQ", "WalkingDistance_SQ" = "Walking Distance SQ"),
- stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.note = "%stars. Standard errors in parentheses.",
- label = "tab:nr_ols",
- caption = "Results of OLS on the NR-index.",
- file="Tables/ols/nr_ols.tex")
-
-#### Logit #####
-
-texreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="tb",
- custom.model.names = c("Logit regression"),
- custom.header = list("Dependent variable: Voluntary Information Access" = 1),
- custom.coef.map = list_ols, custom.note = "%stars. Standard errors in parentheses.",
- label = "tab:logit_vt",
- caption = "Results of logit regression on the access of optional information.",
- file="Tables/logit/chose_treatment.tex")
-
-
-##### MXL #######
-
-### Baseline case A
-case_A <- quicktexregapollo(mxl_wtp_case_a_rentINT)
-
-coef_names <- case_A@coef.names
-coef_names <- sub("^(mu_)(.*)(_T|_VT)$", "\\2\\3", coef_names)
-coef_names[4] <- "mu_ASC_sq"
-case_A@coef.names <- coef_names
-
-case_A_cols <- map(c("^mu_", "^sig_", "_T$", "_VT$"), subcoef, case_A)
-
-texreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])),
- custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
- "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
- "wd" = "Walking Distance", "asc" = "ASC SQ"),
- custom.model.names = c("Mean", "SD", "Treated", "Voluntary Treated"), custom.note = "%stars. Robust standard errors in parentheses.",
- stars = c(0.01, 0.05, 0.1), float.pos="tb",
- label = "tab:mxl_A",
- caption = "Results of mixed logit model with treatment interactions for Case A.",
- file="Tables/mxl/case_A_rent_INT.tex")
-
-### Baseline case C
-case_C <- quicktexregapollo(mxl_wtp_case_c_rentINT)
-
-coef_names <- case_C@coef.names
-coef_names <- sub("^(mu_)(.*)(1|2|info)$", "\\2\\3", coef_names)
-coef_names[4] <- "mu_ASC_sq"
-case_C@coef.names <- coef_names
-
-
-case_C_cols <- map(c("^mu_", "^sig_", "_vid1$", "_vid2$", "_nv1$", "_nv2$", "_no_info$"), subcoef, case_C)
-
-texreg(c(case_C_cols[1], remGOF(case_C_cols[2:7])),
- custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
- "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
- "wd" = "Walking Distance", "asc" = "ASC SQ",
- "ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
- custom.model.names = c("Mean", "SD", "Video 1", "Video 2", "Text 1", "Text 2", "No Info"), custom.note = "%stars. Robust standard errors in parentheses.",
- stars = c(0.01, 0.05, 0.1), float.pos="tb",
- label = "tab:mxl_C",
- caption = "Results of mixed logit model with treatment interactions for Case B.",
- file="Tables/mxl/case_C_rent_INT.tex")
-
-### Rent NR model case C
-case_C_NR <- quicktexregapollo(mxl_wtp_NR_case_c_rentINT)
-
-coef_names <- case_C_NR@coef.names
-coef_names <- sub("^(mu_)(.*)(1|2|info|NR)$", "\\2\\3", coef_names)
-coef_names[4] <- "mu_ASC_sq"
-case_C_NR@coef.names <- coef_names
-
-
-case_C_cols_NR <- map(c("^mu_", "^sig_", "_vid1$", "_vid2$", "_nv1$", "_nv2$", "_no_info$", "_NR$"), subcoef, case_C_NR)
-
-texreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])),
- custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
- "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
- "wd" = "Walking Distance", "asc" = "ASC SQ",
- "ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
- custom.model.names = c("Mean", "SD", "Video 1", "Video 2", "Text 1", "Text 2", "No Info", "NR"), custom.note = "%stars. Robust standard errors in parentheses.",
- stars = c(0.01, 0.05, 0.1), float.pos="tb",
- label = "tab:mxl_NR",
- caption = "Results of mixed logit model with treatment and NR-index interactions for Case B.",
- file="Tables/mxl/case_C_rent_INT_NR.tex")
-# Main model
-# texreg(l=list(mxl_wtp_case_a_rentINT),
-# custom.coef.map = list("mu_natural" = "Naturalness", "mu_walking" = "Walking Distance", "mu_rent" = "Rent",
-# "ASC_sq" = "ASC SQ", "sig_natural" = "Naturalness SD", "sig_walking" = "Walking Distance SD",
-# "sig_rent" = "Rent SD", "sig_ASC_sq" = "ASC SD",
-# "mu_nat_T" = "Naturalness X Treated", "mu_wd_T" = "Walking Distance X Treated", "mu_rent_T" = "Rent X Treated",
-# "mu_asc_T" = "ASC X Treated", "mu_nat_VT" = "Naturalness X Vol. Treated", "mu_wd_VT" = "Walking Distance X Vol. Treated",
-# "mu_rent_VT" = "Rent X Vol. Treated", "mu_asc_VT" = "ASC X Vol. Treated"),
-# stars = c(0.01, 0.05, 0.1), override.se = mxl_wtp_case_a_rentINT$robse, file="Tables/mxl/case_A_rent_INT.tex")
+library(choiceTools)
+
+dir.create("Tables/mxl")
+dir.create("Tables/logit")
+dir.create("Tables/ols/")
+
+
+list_ols <- list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Optional Treatment",
+ "as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
+ "as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
+ "as.factor(Treatment_C)Video 2" = "Video 2", "as.factor(Treatment_D)Treated" = "Treated", "as.factor(Treatment_D)Vol. Treated" = "Vol. Treated",
+ "as.factor(Treatment_D)No Info 2" = "No Info",
+ "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
+ "Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree")
+# OLS A
+texreg(l=list(ols_time_spent_control_A, ols_time_cc_control_A, ols_percentage_correct_control_A, conseq_model_control_A),
+ custom.model.names = c("Interview Time", "CC Time", "Quiz", "Cons. Score"),
+ custom.header = list("Model 1A" = 1:1, "Model 2A" = 2:2, "Model 3A" = 3:3, "Model 4A" = 4:4),
+ custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "Notes: (i) The dependent variables examined in this regression analysis are as follows:
+ Model 1A refers to net interview time,
+ Model 2A denotes choice card time, Model 3A represents the percentage of correct quiz questions, and Model 4A represents consequentiality score.
+ (ii) The variables included in the analysis are as follows: Treated is a dummy variable indicating membership
+ in the obligatory treated group, Optional Treatment is a dummy variable indicating membership in the group with
+ the choice to receive treatment or not, with the reference group being Non-Treated. NR-Index represents the z-standardized
+ natural relatedness index. Female is a dummy variable denoting gender, Age has been mean-centered and measured in years,
+ Income is a continuous variable indicating a transition from one income group to the next higher, and University Degree is
+ a dummy variable indicating whether an individual holds a university degree; (iii) %stars and standard errors in parentheses.",
+ label = "tab:olsA",
+ caption = "Results of OLS regressions for Scenario Case A.",
+ file="Tables/ols/ols_A.tex")
+
+# OLS D
+texreg(l=list(ols_time_spent_control_D, ols_time_cc_control_D, ols_percentage_correct_control_D, conseq_model_control_D),
+ custom.model.names = c("Interview Time", "CC Time", "Quiz", "Cons. Score"),
+ custom.header = list("Model 1B" = 1:1, "Model 2B" = 2:2, "Model 3B" = 3:3, "Model 4B" = 4:4),
+ custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "Notes: (i) The dependent variables examined in this regression analysis are as follows:
+ Model 1B refers to net interview time,
+ Model 2B denotes choice card time, Model 1B represents the percentage of correct quiz questions, and Model 4B represents consequentiality score.
+ (ii) The variables included in the analysis are as follows: Treated is a dummy variable indicating membership
+ in the obligatory treated group, Vol. Treated is a dummy variable indicating the group that voluntarily chose the optional treatment,
+ while No Info indicates the group that did not opt for the treatment, with the reference group being Non-Treated. NR-Index represents the z-standardized
+ natural relatedness index. Female is a dummy variable denoting gender, Age has been mean-centered and measured in years,
+ Income is a continuous variable indicating a transition from one income group to the next higher, and University Degree is
+ a dummy variable indicating whether an individual holds a university degree; (iii) %stars and standard errors in parentheses.",
+ label = "tab:olsD",
+ caption = "Results of OLS regressions for Scenario Case B.",
+ file="Tables/ols/ols_D.tex")
+
+# Manipulation check
+texreg(l=list(ols_percentage_correct_A, ols_percentage_correct_control_A, ols_percentage_correct_C, ols_percentage_correct_control_C),
+ custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
+ custom.header = list("Dependent Variable: Percentage of correct quiz statements" = 1:4),
+ custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "%stars. Standard errors in parentheses.",
+ label = "tab:mani",
+ caption = "Results of OLS on percentage of correct quiz statements.",
+ file="Tables/ols/manipulation.tex")
+
+
+# Net interview time
+texreg(l=list(ols_time_spent_A, ols_time_spent_control_A, ols_time_spent_C, ols_time_spent_control_C),
+ custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
+ custom.header = list("Dependent variable: Net interview time" = 1:4),
+ custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "%stars. Standard errors in parentheses.",
+ label = "tab:net_int",
+ caption = "Results of OLS on net interview time.",
+ file="Tables/ols/interviewtime.tex")
+
+
+# CC Time
+texreg(l=list(ols_time_cc_A, ols_time_cc_control_A, ols_time_cc_C, ols_time_cc_control_C),
+ custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
+ custom.header = list("Dependent variable: Mean choice card time" = 1:4),
+ custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "%stars. Standard errors in parentheses.",
+ label = "tab:cctime",
+ caption = "Results of OLS on mean choice card time.",
+ file="Tables/ols/cctime.tex")
+
+# Consequentiality
+texreg(l=list(conseq_model_A, conseq_model_control_A, conseq_model_C, conseq_model_control_C),
+ custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
+ custom.header = list("Dependent variable: Consequentiality score" = 1:4),
+ custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "%stars. Standard errors in parentheses.",
+ label = "tab:conseq",
+ caption = "Results of OLS on consequentiality score.",
+ file="Tables/ols/consequentiality.tex")
+
+# Opt Out
+texreg(l=list(ols_opt_out_A, ols_opt_out_control_A, ols_opt_out_C, ols_opt_out_control_C),
+ custom.model.names = c("Case A", "with Controls", "Case B", "with Controls"),
+ custom.header = list("Dependent variable: Number of opt-out choices" = 1:4),
+ custom.coef.map = list_ols, stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "%stars. Standard errors in parentheses.",
+ label = "tab:optout",
+ caption = "Results of OLS on number of opt-out choices.",
+ file="Tables/ols/optout.tex")
+
+# NR
+texreg(l=list(nr_model_treat_A),
+ custom.model.names = c("OLS regression"),
+ custom.header = list("Dependent variable: NR-Index" = 1),
+ custom.coef.map = list("(Intercept)" = "Intercept", "as.factor(Treatment_A)Treated" = "Treated", "as.factor(Treatment_A)Vol_Treated" = "Vol. Treated",
+ "as.factor(Treatment_C)No Info 2" = "No Info 2", "as.factor(Treatment_C)No Video 1" = "Text 1",
+ "as.factor(Treatment_C)No Video 2" = "Text 2", "as.factor(Treatment_C)Video 1" = "Video 1",
+ "as.factor(Treatment_C)Video 2" = "Video 2", "Z_Mean_NR" = "NR-Index", "as.factor(Gender)2" = "Female",
+ "Age_mean" = "Age", "QFIncome" = "Income", "Uni_degree" = "University Degree", "Kids_Dummy" = "Children",
+ "Naturalness_SQ" = "Naturalness SQ", "WalkingDistance_SQ" = "Walking Distance SQ"),
+ stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.note = "%stars. Standard errors in parentheses.",
+ label = "tab:nr_ols",
+ caption = "Results of OLS on the NR-index.",
+ file="Tables/ols/nr_ols.tex")
+
+#### Logit #####
+
+texreg(l=list(logit_choice_treat_uni), stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ custom.model.names = c("Logit regression"),
+ custom.header = list("Dependent variable: Voluntary Information Access" = 1),
+ custom.coef.map = list_ols, custom.note = "%stars. Standard errors in parentheses.",
+ label = "tab:logit_vt",
+ caption = "Results of logit regression on the access of optional information.",
+ file="Tables/logit/chose_treatment.tex")
+
+
+##### MXL #######
+
+### Baseline case A
+case_A <- quicktexregapollo(mxl_wtp_case_a_rentINT)
+
+coef_names <- case_A@coef.names
+coef_names <- sub("^(mu_)(.*)(_T|_VT)$", "\\2\\3", coef_names)
+coef_names[4] <- "mu_ASC_sq"
+case_A@coef.names <- coef_names
+
+case_A_cols <- map(c("^mu_", "^sig_", "_T$", "_VT$"), subcoef, case_A)
+
+texreg(c(case_A_cols[1], remGOF(case_A_cols[2:4])),
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
+ "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ "wd" = "Walking Distance", "asc" = "ASC SQ"),
+ custom.model.names = c("Mean", "SD", "Treated", "Optional Treatment"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
+ stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ label = "tab:mxl_A",
+ caption = "Results of mixed logit model with treatment interactions for Case A.",
+ file="Tables/mxl/case_A_rent_INT.tex")
+
+### Baseline case C
+case_C <- quicktexregapollo(mxl_wtp_case_c_rentINT)
+
+coef_names <- case_C@coef.names
+coef_names <- sub("^(mu_)(.*)(1|2|info)$", "\\2\\3", coef_names)
+coef_names[4] <- "mu_ASC_sq"
+case_C@coef.names <- coef_names
+
+
+case_C_cols <- map(c("^mu_", "^sig_", "_vid1$", "_vid2$", "_nv1$", "_nv2$", "_no_info$"), subcoef, case_C)
+
+texreg(c(case_C_cols[1], remGOF(case_C_cols[2:7])),
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
+ "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ "wd" = "Walking Distance", "asc" = "ASC SQ",
+ "ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
+ custom.model.names = c("Mean", "SD", "Video 1", "Video 2", "Text 1", "Text 2", "No Info"), custom.note = "%stars. Robust standard errors in parentheses.",
+ stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ label = "tab:mxl_C",
+ caption = "Results of mixed logit model with treatment interactions for Case B.",
+ file="Tables/mxl/case_C_rent_INT.tex")
+
+### Rent NR model case C
+case_C_NR <- quicktexregapollo(mxl_wtp_NR_case_c_rentINT)
+
+coef_names <- case_C_NR@coef.names
+coef_names <- sub("^(mu_)(.*)(1|2|info|NR)$", "\\2\\3", coef_names)
+coef_names[4] <- "mu_ASC_sq"
+case_C_NR@coef.names <- coef_names
+
+
+case_C_cols_NR <- map(c("^mu_", "^sig_", "_vid1$", "_vid2$", "_nv1$", "_nv2$", "_no_info$", "_NR$"), subcoef, case_C_NR)
+
+texreg(c(case_C_cols_NR[1], remGOF(case_C_cols_NR[2:8])),
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
+ "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ "wd" = "Walking Distance", "asc" = "ASC SQ",
+ "ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
+ custom.model.names = c("Mean", "SD", "Video 1", "Video 2", "Text 1", "Text 2", "No Info", "NR"), custom.note = "%stars. Robust standard errors in parentheses.",
+ stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ label = "tab:mxl_NR",
+ caption = "Results of mixed logit model with treatment and NR-index interactions for Case B.",
+ file="Tables/mxl/case_C_rent_INT_NR.tex")
+
+
+### New Case B
+case_B <- quicktexregapollo(mxl_wtp_case_d_rentINT)
+
+coef_names <- case_B@coef.names
+coef_names <- sub("^(mu_)(.*)(vol_treated|_treated|_no_info)$", "\\2\\3", coef_names)
+coef_names[4] <- "mu_ASC_sq"
+case_B@coef.names <- coef_names
+
+
+case_B_cols <- map(c("^mu_", "^sig_", "_treated$", "_vol_treated$","_no_info$"), subcoef, case_B)
+
+texreg(c(case_B_cols[1], remGOF(case_B_cols[2:5])),
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
+ "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ "wd" = "Walking Distance", "asc" = "ASC SQ",
+ "ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
+ custom.model.names = c("Mean", "SD", "Treated", "Vol. Treated", "No Info"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
+ stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ label = "tab:mxl_C",
+ caption = "Results of mixed logit model with treatment interactions for Case B.",
+ file="Tables/mxl/case_B_rent_INT.tex")
+
+
+
+### New Case B NR
+case_B_NR <- quicktexregapollo(new_case_b_NR)
+
+coef_names <- case_B_NR@coef.names
+coef_names <- sub("^(mu_)(.*)(vol_treated|_treated|_no_info|NR)$", "\\2\\3", coef_names)
+coef_names[4] <- "mu_ASC_sq"
+case_B_NR@coef.names <- coef_names
+
+
+case_B_cols_NR <- map(c("^mu_", "^sig_", "_treated$", "_vol_treated$","_no_info$", "_NR$"), subcoef, case_B_NR)
+
+texreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
+ custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
+ "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+ "wd" = "Walking Distance", "asc" = "ASC SQ", "ASC" ="ASC SQ",
+ "ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
+ custom.model.names = c("Mean", "SD", "Treated", "Vol. Treated", "No Info", "NR-Index"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
+ stars = c(0.01, 0.05, 0.1), float.pos="tb",
+ label = "tab:mxl_C_NR",
+ caption = "Results of mixed logit model with treatment interactions for Case B.",
+ file="Tables/mxl/case_B_rent_INT_NR.tex")
+
+
+# Main model
+# texreg(l=list(mxl_wtp_case_a_rentINT),
+# custom.coef.map = list("mu_natural" = "Naturalness", "mu_walking" = "Walking Distance", "mu_rent" = "Rent",
+# "ASC_sq" = "ASC SQ", "sig_natural" = "Naturalness SD", "sig_walking" = "Walking Distance SD",
+# "sig_rent" = "Rent SD", "sig_ASC_sq" = "ASC SD",
+# "mu_nat_T" = "Naturalness X Treated", "mu_wd_T" = "Walking Distance X Treated", "mu_rent_T" = "Rent X Treated",
+# "mu_asc_T" = "ASC X Treated", "mu_nat_VT" = "Naturalness X Vol. Treated", "mu_wd_VT" = "Walking Distance X Vol. Treated",
+# "mu_rent_VT" = "Rent X Vol. Treated", "mu_asc_VT" = "ASC X Vol. Treated"),
+# stars = c(0.01, 0.05, 0.1), override.se = mxl_wtp_case_a_rentINT$robse, file="Tables/mxl/case_A_rent_INT.tex")
diff --git a/Scripts/mxl/mxl_wtp_space_caseD_RentINT_NR.R b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_NR.R
index d0d686c41cd9b909b7619fb4928e056dfb489444..7e7b0be580ee84db7b993ce7a24b29854b631e3a 100644
--- a/Scripts/mxl/mxl_wtp_space_caseD_RentINT_NR.R
+++ b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_NR.R
@@ -1,187 +1,187 @@
-#### Apollo standard script #####
-
-library(apollo) # Load apollo package
-
-
-
-# Test treatment effect
-
-database <- database_full %>%
- filter(!is.na(Treatment_new)) %>%
- mutate(Dummy_Treated = case_when(Treatment_new == 1|Treatment_new == 2 ~ 1, TRUE ~ 0),
- Dummy_Vol_Treated = case_when(Treatment_new == 5 |Treatment_new == 4 ~ 1, TRUE ~ 0),
- Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0))
-
-table(database$Dummy_Treated)
-table(database$Dummy_Vol_Treated)
-table(database$Dummy_no_info)
-
-#initialize model
-
-apollo_initialise()
-
-
-### Set core controls
-apollo_control = list(
- modelName = "MXL_wtp_Case_D_NR",
- modelDescr = "MXL wtp space Case D NR",
- indivID ="id",
- mixing = TRUE,
- HB= FALSE,
- nCores = n_cores,
- outputDirectory = "Estimation_results/mxl"
-)
-
-##### Define model parameters depending on your attributes and model specification! ####
-# set values to 0 for conditional logit model
-
-apollo_beta=c(mu_natural = 15,
- mu_walking = -1,
- mu_rent = -2,
- ASC_sq = 0,
- mu_ASC_sq_treated = 0,
- mu_ASC_sq_vol_treated = 0,
- mu_ASC_sq_no_info = 0,
- mu_ASC_NR = 0,
- mu_rent_treated = 0,
- mu_rent_vol_treated = 0,
- mu_rent_no_info = 0,
- mu_rent_NR = 0,
- mu_nat_treated =0,
- mu_nat_vol_treated = 0,
- mu_nat_no_info = 0,
- mu_nat_NR = 0,
- mu_walking_treated =0,
- mu_walking_vol_treated = 0,
- mu_walking_no_info = 0,
- mu_walking_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 + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR)*
- (b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
- + mu_nat_treated * Naturalness_1 *Dummy_Treated + mu_nat_no_info * Naturalness_1 * Dummy_no_info
- + mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated
- + mu_walking_treated * WalkingDistance_1 *Dummy_Treated + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
- + mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated
- + mu_nat_NR * Z_Mean_NR *Naturalness_1 +
- + mu_walking_NR * Z_Mean_NR * WalkingDistance_1
- - Rent_1)
-
- V[['alt2']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR)*
- (b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
- + mu_nat_treated * Naturalness_2 *Dummy_Treated + mu_nat_no_info * Naturalness_2 * Dummy_no_info
- + mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated
- + mu_walking_treated * WalkingDistance_2 *Dummy_Treated + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
- + mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated
- + mu_nat_NR * Z_Mean_NR *Naturalness_2 +
- + mu_walking_NR * Z_Mean_NR * WalkingDistance_2
- - Rent_2)
-
- V[['alt3']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR)*
- (b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
- + mu_nat_treated * Naturalness_3 *Dummy_Treated + mu_nat_no_info * Naturalness_3 * Dummy_no_info
- + mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated
- + mu_walking_treated * WalkingDistance_3 *Dummy_Treated + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
- + mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
- + mu_ASC_sq_treated * Dummy_Treated + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
- + mu_ASC_sq_no_info * Dummy_no_info
- + mu_ASC_NR * Z_Mean_NR
- + mu_nat_NR * Z_Mean_NR *Naturalness_3
- + mu_walking_NR * Z_Mean_NR * 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_case_d_rent_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_case_d_rent_NR)
-
-
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+
+# Test treatment effect
+
+database <- database_full %>%
+ 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))
+
+table(database$Dummy_Treated)
+table(database$Dummy_Vol_Treated)
+table(database$Dummy_no_info)
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Case_D_NR",
+ modelDescr = "MXL wtp space Case D NR",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(mu_natural = 15,
+ mu_walking = -1,
+ mu_rent = -2,
+ ASC_sq = 0,
+ mu_ASC_sq_treated = 0,
+ mu_ASC_sq_vol_treated = 0,
+ mu_ASC_sq_no_info = 0,
+ mu_ASC_NR = 0,
+ mu_rent_treated = 0,
+ mu_rent_vol_treated = 0,
+ mu_rent_no_info = 0,
+ mu_rent_NR = 0,
+ mu_nat_treated =0,
+ mu_nat_vol_treated = 0,
+ mu_nat_no_info = 0,
+ mu_nat_NR = 0,
+ mu_walking_treated =0,
+ mu_walking_vol_treated = 0,
+ mu_walking_no_info = 0,
+ mu_walking_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 + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR)*
+ (b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ + mu_nat_treated * Naturalness_1 *Dummy_Treated + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_1 *Dummy_Treated + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated
+ + mu_nat_NR * Z_Mean_NR *Naturalness_1 +
+ + mu_walking_NR * Z_Mean_NR * WalkingDistance_1
+ - Rent_1)
+
+ V[['alt2']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR)*
+ (b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
+ + mu_nat_treated * Naturalness_2 *Dummy_Treated + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_2 *Dummy_Treated + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated
+ + mu_nat_NR * Z_Mean_NR *Naturalness_2 +
+ + mu_walking_NR * Z_Mean_NR * WalkingDistance_2
+ - Rent_2)
+
+ V[['alt3']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR)*
+ (b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
+ + mu_nat_treated * Naturalness_3 *Dummy_Treated + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_3 *Dummy_Treated + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
+ + mu_ASC_sq_treated * Dummy_Treated + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
+ + mu_ASC_sq_no_info * Dummy_no_info
+ + mu_ASC_NR * Z_Mean_NR
+ + mu_nat_NR * Z_Mean_NR *Naturalness_3
+ + mu_walking_NR * Z_Mean_NR * 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_case_d_rent_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_case_d_rent_NR)
+
+
diff --git a/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
index 2b16bb087a277655f659d410f95011ad3355c802..4682a96b61ce61af30f6b4756c76cd1fb295286c 100644
--- a/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
+++ b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
@@ -1,198 +1,198 @@
-#### Apollo standard script #####
-
-library(apollo) # Load apollo package
-
-
-
-# Test treatment effect
-
-database <- database_full %>%
- 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))
-
-table(database$Dummy_Treated)
-table(database$Dummy_Vol_Treated)
-table(database$Dummy_no_info)
-
-#initialize model
-
-apollo_initialise()
-
-
-### Set core controls
-apollo_control = list(
- modelName = "MXL_wtp_Case_D_X",
- modelDescr = "MXL wtp space Case D Interactions",
- indivID ="id",
- mixing = TRUE,
- HB= FALSE,
- nCores = n_cores,
- outputDirectory = "Estimation_results/mxl"
-)
-
-##### Define model parameters depending on your attributes and model specification! ####
-# set values to 0 for conditional logit model
-
-apollo_beta=c(mu_natural = 15,
- mu_walking = -1,
- mu_rent = -2,
- ASC_sq = 0,
- mu_ASC_sq_treated = 0,
- mu_ASC_sq_vol_treated = 0,
- mu_ASC_sq_no_info = 0,
- mu_ASC_NR = 0,
- mu_ASC_Age = 0,
- mu_ASC_Income = 0,
- mu_rent_treated = 0,
- mu_rent_vol_treated = 0,
- mu_rent_no_info = 0,
- mu_rent_NR = 0,
- mu_rent_Age = 0,
- mu_rent_Income = 0,
- mu_nat_treated =0,
- mu_nat_vol_treated = 0,
- mu_nat_no_info = 0,
- mu_nat_NR = 0,
- mu_nat_Age = 0,
- mu_nat_Income = 0,
- mu_walking_treated =0,
- mu_walking_vol_treated = 0,
- mu_walking_no_info = 0,
- mu_walking_NR = 0,
- mu_walking_Age = 0,
- mu_walking_Income = 0,
- sig_natural = 15,
- sig_walking = 2,
- sig_rent = 2,
- sig_ASC_sq = 2)
-
-### specify parameters that should be kept fixed, here = none
-apollo_fixed = c()
-
-### Set parameters for generating draws, use 2000 sobol draws
-apollo_draws = list(
- interDrawsType = "sobol",
- interNDraws = n_draws,
- interUnifDraws = c(),
- interNormDraws = c("draws_natural", "draws_walking", "draws_rent", "draws_asc"),
- intraDrawsType = "halton",
- intraNDraws = 0,
- intraUnifDraws = c(),
- intraNormDraws = c()
-)
-
-### Create random parameters, define distribution of the parameters
-apollo_randCoeff = function(apollo_beta, apollo_inputs){
- randcoeff = list()
-
- randcoeff[["b_mu_natural"]] = mu_natural + sig_natural * draws_natural
- randcoeff[["b_mu_walking"]] = mu_walking + sig_walking * draws_walking
- randcoeff[["b_mu_rent"]] = -exp(mu_rent + sig_rent * draws_rent)
- randcoeff[["b_ASC_sq"]] = ASC_sq + sig_ASC_sq * draws_asc
-
- return(randcoeff)
-}
-
-
-### validate
-apollo_inputs = apollo_validateInputs()
-apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
-
- ### Function initialisation: do not change the following three commands
- ### Attach inputs and detach after function exit
- apollo_attach(apollo_beta, apollo_inputs)
- on.exit(apollo_detach(apollo_beta, apollo_inputs))
-
- ### Create list of probabilities P
- P = list()
-
- #### List of utilities (later integrated in mnl_settings below) ####
- # Define utility functions here:
-
- V = list()
- V[['alt1']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
- (b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
- + mu_nat_treated * Naturalness_1 *Dummy_Treated + mu_nat_no_info * Naturalness_1 * Dummy_no_info
- + mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated
- + mu_walking_treated * WalkingDistance_1 *Dummy_Treated + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
- + mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated
- + mu_nat_NR * Z_Mean_NR *Naturalness_1 + mu_nat_Age * Age_mean * Naturalness_1
- + mu_nat_Income * Income_mean * Naturalness_1 + mu_walking_NR * Z_Mean_NR * WalkingDistance_1
- + mu_walking_Age * Age_mean * WalkingDistance_1 + mu_walking_Income * Income_mean * WalkingDistance_1
- - Rent_1)
-
- V[['alt2']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
- (b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
- + mu_nat_treated * Naturalness_2 *Dummy_Treated + mu_nat_no_info * Naturalness_2 * Dummy_no_info
- + mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated
- + mu_walking_treated * WalkingDistance_2 *Dummy_Treated + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
- + mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated
- + mu_nat_NR * Z_Mean_NR *Naturalness_2 + mu_nat_Age * Age_mean * Naturalness_2
- + mu_nat_Income * Income_mean * Naturalness_2 + mu_walking_NR * Z_Mean_NR * WalkingDistance_2
- + mu_walking_Age * Age_mean * WalkingDistance_2 + mu_walking_Income * Income_mean * WalkingDistance_2
- - Rent_2)
-
- V[['alt3']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
- + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
- (b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
- + mu_nat_treated * Naturalness_3 *Dummy_Treated + mu_nat_no_info * Naturalness_3 * Dummy_no_info
- + mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated
- + mu_walking_treated * WalkingDistance_3 *Dummy_Treated + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
- + mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
- + mu_ASC_sq_treated * Dummy_Treated + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
- + mu_ASC_sq_no_info * Dummy_no_info
- + mu_ASC_NR * Z_Mean_NR + mu_ASC_Age * Age_mean + mu_ASC_Income * Income_mean
- + mu_nat_NR * Z_Mean_NR *Naturalness_3 + mu_nat_Age * Age_mean * Naturalness_3
- + mu_nat_Income * Income_mean * Naturalness_3 + mu_walking_NR * Z_Mean_NR * WalkingDistance_3
- + mu_walking_Age * Age_mean * WalkingDistance_3 + mu_walking_Income * Income_mean * 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_case_d_rentX = 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_case_d_rentX)
-
-
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+
+# Test treatment effect
+
+database <- database_full %>%
+ 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))
+
+table(database$Dummy_Treated)
+table(database$Dummy_Vol_Treated)
+table(database$Dummy_no_info)
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_Case_D_X",
+ modelDescr = "MXL wtp space Case D Interactions",
+ indivID ="id",
+ mixing = TRUE,
+ HB= FALSE,
+ nCores = n_cores,
+ outputDirectory = "Estimation_results/mxl"
+)
+
+##### Define model parameters depending on your attributes and model specification! ####
+# set values to 0 for conditional logit model
+
+apollo_beta=c(mu_natural = 15,
+ mu_walking = -1,
+ mu_rent = -2,
+ ASC_sq = 0,
+ mu_ASC_sq_treated = 0,
+ mu_ASC_sq_vol_treated = 0,
+ mu_ASC_sq_no_info = 0,
+ mu_ASC_NR = 0,
+ mu_ASC_Age = 0,
+ mu_ASC_Income = 0,
+ mu_rent_treated = 0,
+ mu_rent_vol_treated = 0,
+ mu_rent_no_info = 0,
+ mu_rent_NR = 0,
+ mu_rent_Age = 0,
+ mu_rent_Income = 0,
+ mu_nat_treated =0,
+ mu_nat_vol_treated = 0,
+ mu_nat_no_info = 0,
+ mu_nat_NR = 0,
+ mu_nat_Age = 0,
+ mu_nat_Income = 0,
+ mu_walking_treated =0,
+ mu_walking_vol_treated = 0,
+ mu_walking_no_info = 0,
+ mu_walking_NR = 0,
+ mu_walking_Age = 0,
+ mu_walking_Income = 0,
+ sig_natural = 15,
+ sig_walking = 2,
+ sig_rent = 2,
+ sig_ASC_sq = 2)
+
+### specify parameters that should be kept fixed, here = none
+apollo_fixed = c()
+
+### Set parameters for generating draws, use 2000 sobol draws
+apollo_draws = list(
+ interDrawsType = "sobol",
+ interNDraws = n_draws,
+ interUnifDraws = c(),
+ interNormDraws = c("draws_natural", "draws_walking", "draws_rent", "draws_asc"),
+ intraDrawsType = "halton",
+ intraNDraws = 0,
+ intraUnifDraws = c(),
+ intraNormDraws = c()
+)
+
+### Create random parameters, define distribution of the parameters
+apollo_randCoeff = function(apollo_beta, apollo_inputs){
+ randcoeff = list()
+
+ randcoeff[["b_mu_natural"]] = mu_natural + sig_natural * draws_natural
+ randcoeff[["b_mu_walking"]] = mu_walking + sig_walking * draws_walking
+ randcoeff[["b_mu_rent"]] = -exp(mu_rent + sig_rent * draws_rent)
+ randcoeff[["b_ASC_sq"]] = ASC_sq + sig_ASC_sq * draws_asc
+
+ return(randcoeff)
+}
+
+
+### validate
+apollo_inputs = apollo_validateInputs()
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+ ### Function initialisation: do not change the following three commands
+ ### Attach inputs and detach after function exit
+ apollo_attach(apollo_beta, apollo_inputs)
+ on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+ ### Create list of probabilities P
+ P = list()
+
+ #### List of utilities (later integrated in mnl_settings below) ####
+ # Define utility functions here:
+
+ V = list()
+ V[['alt1']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
+ (b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ + mu_nat_treated * Naturalness_1 *Dummy_Treated + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_1 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_1 *Dummy_Treated + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_1 * Dummy_Vol_Treated
+ + mu_nat_NR * Z_Mean_NR *Naturalness_1 + mu_nat_Age * Age_mean * Naturalness_1
+ + mu_nat_Income * Income_mean * Naturalness_1 + mu_walking_NR * Z_Mean_NR * WalkingDistance_1
+ + mu_walking_Age * Age_mean * WalkingDistance_1 + mu_walking_Income * Income_mean * WalkingDistance_1
+ - Rent_1)
+
+ V[['alt2']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
+ (b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
+ + mu_nat_treated * Naturalness_2 *Dummy_Treated + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_2 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_2 *Dummy_Treated + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_2 * Dummy_Vol_Treated
+ + mu_nat_NR * Z_Mean_NR *Naturalness_2 + mu_nat_Age * Age_mean * Naturalness_2
+ + mu_nat_Income * Income_mean * Naturalness_2 + mu_walking_NR * Z_Mean_NR * WalkingDistance_2
+ + mu_walking_Age * Age_mean * WalkingDistance_2 + mu_walking_Income * Income_mean * WalkingDistance_2
+ - Rent_2)
+
+ V[['alt3']] = -(b_mu_rent + mu_rent_treated *Dummy_Treated + mu_rent_vol_treated * Dummy_Vol_Treated + mu_rent_no_info * Dummy_no_info
+ + mu_rent_NR * Z_Mean_NR + mu_rent_Age * Age_mean + mu_rent_Income * Income_mean)*
+ (b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
+ + mu_nat_treated * Naturalness_3 *Dummy_Treated + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ + mu_nat_vol_treated * Naturalness_3 * Dummy_Vol_Treated
+ + mu_walking_treated * WalkingDistance_3 *Dummy_Treated + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
+ + mu_walking_vol_treated * WalkingDistance_3 * Dummy_Vol_Treated
+ + mu_ASC_sq_treated * Dummy_Treated + mu_ASC_sq_vol_treated * Dummy_Vol_Treated
+ + mu_ASC_sq_no_info * Dummy_no_info
+ + mu_ASC_NR * Z_Mean_NR + mu_ASC_Age * Age_mean + mu_ASC_Income * Income_mean
+ + mu_nat_NR * Z_Mean_NR *Naturalness_3 + mu_nat_Age * Age_mean * Naturalness_3
+ + mu_nat_Income * Income_mean * Naturalness_3 + mu_walking_NR * Z_Mean_NR * WalkingDistance_3
+ + mu_walking_Age * Age_mean * WalkingDistance_3 + mu_walking_Income * Income_mean * 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_case_d_rentX = 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_case_d_rentX)
+
+