diff --git a/Scripts/mxl/mxl_wtp_space_caseD.R b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
similarity index 71%
rename from Scripts/mxl/mxl_wtp_space_caseD.R
rename to Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
index d32875f8946cbe36e65e3d2496362ca43693977c..612c5147efe4a31b13964b4ad4d2bb2687a67d70 100644
--- a/Scripts/mxl/mxl_wtp_space_caseD.R
+++ b/Scripts/mxl/mxl_wtp_space_caseD_RentINT_X.R
@@ -23,8 +23,8 @@ apollo_initialise()
### Set core controls
apollo_control = list(
- modelName = "MXL_wtp_Case_D",
- modelDescr = "MXL wtp space Case D",
+ modelName = "MXL_wtp_Case_D_X",
+ modelDescr = "MXL wtp space Case D Interactions",
indivID ="id",
mixing = TRUE,
HB= FALSE,
@@ -42,15 +42,27 @@ apollo_beta=c(mu_natural = 15,
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,
@@ -100,25 +112,44 @@ apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimat
# 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)*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ 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 * QFIncome)*
+ (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 - Rent_1)
+ + 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 * QFIncome * Naturalness_1 + mu_walking_NR * Z_Mean_NR * WalkingDistance_1
+ + mu_walking_Age * Age_mean * WalkingDistance_1 + mu_walking_Income * QFIncome * 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)*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2
+ 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 * QFIncome)*
+ (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- Rent_2)
+ + 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 * QFIncome * Naturalness_2 + mu_walking_NR * Z_Mean_NR * WalkingDistance_2
+ + mu_walking_Age * Age_mean * WalkingDistance_2 + mu_walking_Income * QFIncome * 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)*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3
+ 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 * QFIncome)*
+ (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 - Rent_3)
+ + mu_ASC_sq_no_info * Dummy_no_info
+ + mu_ASC_NR * Z_Mean_NR + mu_ASC_Age * Age_mean + mu_ASC_Income * QFIncome
+ + mu_nat_NR * Z_Mean_NR *Naturalness_3 + mu_nat_Age * Age_mean * Naturalness_3
+ + mu_nat_Income * QFIncome * Naturalness_3 + mu_walking_NR * Z_Mean_NR * WalkingDistance_3
+ + mu_walking_Age * Age_mean * WalkingDistance_3 + mu_walking_Income * QFIncome * WalkingDistance_3
+ - Rent_3)
### Define settings for MNL model component
@@ -151,7 +182,7 @@ apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimat
# ################################################################# #
# estimate model with bfgs algorithm
-mxl_wtp_case_c = apollo_estimate(apollo_beta, apollo_fixed,
+mxl_wtp_case_d_rentX = apollo_estimate(apollo_beta, apollo_fixed,
apollo_probabilities, apollo_inputs,
estimate_settings=list(maxIterations=400,
estimationRoutine="bfgs",
@@ -162,6 +193,6 @@ mxl_wtp_case_c = apollo_estimate(apollo_beta, apollo_fixed,
# ################################################################# #
#### MODEL OUTPUTS ##
# ################################################################# #
-apollo_saveOutput(mxl_wtp_case_c)
+apollo_saveOutput(mxl_wtp_case_d_rentX)
diff --git a/Scripts/mxl/mxl_wtp_space_caseD_rentINT.R b/Scripts/mxl/mxl_wtp_space_caseD_rentINT.R
index 7d5699c85140299317eaf49291ecf34c45a50c92..d32875f8946cbe36e65e3d2496362ca43693977c 100644
--- a/Scripts/mxl/mxl_wtp_space_caseD_rentINT.R
+++ b/Scripts/mxl/mxl_wtp_space_caseD_rentINT.R
@@ -3,147 +3,165 @@
library(apollo) # Load apollo package
-database <- database_full %>% filter(!is.na(Treatment_A)) %>%
- mutate(Dummy_Choice_Info = case_when(Treatment_new == 1 ~ 1, Treatment_new == 4 ~ 1, Treatment_new == 5 ~ 1, TRUE ~ 0),
- Dummy_Choice_No_Info = case_when(Treatment_new == 2 ~ 1, Treatment_new == 3 ~ 1, TRUE ~ 0))
- #initialize model
-
- apollo_initialise()
-
-
- ### Set core controls
- apollo_control = list(
- modelName = "MXL_wtp Case D Rent Int",
- modelDescr = "MXL_wtp Case D Rent Int",
- indivID ="id",
- mixing = TRUE,
- HB= FALSE,
- nCores = n_cores,
- outputDirectory = "Estimation_results/mxl"
- )
-
- ##### Define model parameters depending on your attributes and model specification! ####
- # set values to 0 for conditional logit model
-
- apollo_beta=c(mu_natural = 15,
- mu_walking = -1,
- mu_rent = -2,
- ASC_sq = 0,
- mu_rent_I = 0,
- mu_nat_I = 0,
- mu_wd_I= 0,
- mu_asc_I = 0,
- mu_rent_NI = 0,
- mu_nat_NI = 0,
- mu_wd_NI= 0,
- mu_asc_NI = 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)
- }
+# 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",
+ modelDescr = "MXL wtp space Case D",
+ 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_rent_treated = 0,
+ mu_rent_vol_treated = 0,
+ mu_rent_no_info = 0,
+ mu_nat_treated =0,
+ mu_nat_vol_treated = 0,
+ mu_nat_no_info = 0,
+ mu_walking_treated =0,
+ mu_walking_vol_treated = 0,
+ mu_walking_no_info = 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
- ### 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_I*Dummy_Choice_Info + mu_rent_NI*Dummy_Choice_No_Info)*(b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
- mu_nat_I * Naturalness_1 * Dummy_Choice_Info + mu_wd_I * WalkingDistance_1 * Dummy_Choice_Info+
- mu_nat_NI * Naturalness_1 * Dummy_Choice_No_Info + mu_wd_NI * WalkingDistance_1 * Dummy_Choice_No_Info
- - Rent_1)
-
- V[['alt2']] = -(b_mu_rent + mu_rent_I*Dummy_Choice_Info + mu_rent_NI*Dummy_Choice_No_Info)*(b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2 +
- mu_nat_I * Naturalness_2 * Dummy_Choice_Info + mu_wd_I * WalkingDistance_2 * Dummy_Choice_Info +
- mu_nat_NI * Naturalness_2 * Dummy_Choice_No_Info + mu_wd_NI * WalkingDistance_2 * Dummy_Choice_No_Info
- - Rent_2)
-
- V[['alt3']] = -(b_mu_rent + mu_rent_I*Dummy_Choice_Info + mu_rent_NI*Dummy_Choice_No_Info)*(b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3 + mu_asc_I * Dummy_Choice_Info +
- mu_asc_NI * Dummy_Choice_No_Info +mu_nat_I * Naturalness_3 * Dummy_Choice_Info + mu_wd_I * WalkingDistance_3 * Dummy_Choice_Info +
- mu_nat_NI * Naturalness_3 * Dummy_Choice_No_Info + mu_wd_NI * WalkingDistance_3 * Dummy_Choice_No_Info
- - 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)
+ 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)*(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 - 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)*(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- 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)*(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 - 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
- ### 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_rentINT = apollo_estimate(apollo_beta, apollo_fixed,
- apollo_probabilities, apollo_inputs,
- estimate_settings=list(maxIterations=400,
- estimationRoutine="bfgs",
- hessianRoutine="analytic"))
+ ### 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)
- # ################################################################# #
- #### MODEL OUTPUTS ##
- # ################################################################# #
- apollo_saveOutput(mxl_wtp_case_d_rentINT)
+ ### 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_c = 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_c)