diff --git a/Scripts/mxl/mxl_wtp_space_NR_caseC_RentINT_X.R b/Scripts/mxl/mxl_wtp_space_NR_caseC_RentINT_X.R
new file mode 100644
index 0000000000000000000000000000000000000000..f659244af4f120bb0f57b2c9a4e5bed19b7d1ec4
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+++ b/Scripts/mxl/mxl_wtp_space_NR_caseC_RentINT_X.R
@@ -0,0 +1,194 @@
+#### Apollo standard script #####
+
+library(apollo) # Load apollo package
+
+
+
+# Test treatment effect
+
+database <- database_full %>%
+ filter(!is.na(Treatment_new)) %>%
+ mutate(Dummy_Video_1 = case_when(Treatment_new == 1 ~ 1, TRUE ~ 0),
+ Dummy_Video_2 = case_when(Treatment_new == 5 ~ 1, TRUE ~ 0),
+ Dummy_no_info = case_when(Treatment_new == 3 ~ 1, TRUE~0),
+ Dummy_Info_nv1 = case_when(Treatment_new == 2 ~1, TRUE~0),
+ Dummy_Info_nv2 = case_when(Treatment_new == 4 ~1 , TRUE~0))
+
+#initialize model
+
+apollo_initialise()
+
+
+### Set core controls
+apollo_control = list(
+ modelName = "MXL_wtp_NR_Case_C Rent INT X",
+ modelDescr = "MXL wtp space NR Case C Rent INT X",
+ 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_NR = 0,
+ mu_nat_NR = 0,
+ mu_wd_NR = 0,
+ mu_asc_NR = 0,
+ mu_ASC_sq_vid1 = 0,
+ mu_ASC_sq_vid2 = 0,
+ mu_ASC_sq_no_info = 0,
+ mu_ASC_sq_info_nv1 = 0,
+ mu_ASC_sq_info_nv2 = 0,
+ mu_rent_vid1 = 0,
+ mu_rent_vid2 = 0,
+ mu_rent_no_info = 0,
+ mu_rent_info_nv1 = 0,
+ mu_rent_info_nv2 = 0,
+ mu_nat_vid1 =0,
+ mu_nat_vid2 = 0,
+ mu_nat_no_info = 0,
+ mu_nat_info_nv1 = 0,
+ mu_nat_info_nv2 = 0,
+ mu_walking_vid1 =0,
+ mu_walking_vid2 = 0,
+ mu_walking_no_info = 0,
+ mu_walking_info_nv1 = 0,
+ mu_walking_info_nv2 = 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_vid1 *Dummy_Video_1 + mu_rent_vid2 * Dummy_Video_2 + mu_rent_no_info * Dummy_no_info +
+ mu_rent_info_nv1 * Dummy_Info_nv1 + mu_rent_info_nv2 * Dummy_Info_nv2 + mu_rent_NR * Z_Mean_NR) *
+ (b_mu_natural * Naturalness_1 + b_mu_walking * WalkingDistance_1 +
+ mu_nat_NR * Naturalness_1 * Z_Mean_NR + mu_wd_NR * WalkingDistance_1 * Z_Mean_NR +
+ mu_nat_vid1 * Naturalness_1 *Dummy_Video_1 + mu_nat_no_info * Naturalness_1 * Dummy_no_info
+ + mu_nat_info_nv1 * Naturalness_1 *Dummy_Info_nv1 + mu_nat_vid2 * Naturalness_1 * Dummy_Video_2
+ + mu_nat_info_nv2 * Naturalness_1 *Dummy_Info_nv2 +
+ mu_walking_vid1 * WalkingDistance_1 *Dummy_Video_1 + mu_walking_no_info * WalkingDistance_1 * Dummy_no_info
+ + mu_walking_info_nv1 * WalkingDistance_1 *Dummy_Info_nv1 + mu_walking_vid2 * WalkingDistance_1 * Dummy_Video_2
+ + mu_walking_info_nv2 * WalkingDistance_1 *Dummy_Info_nv2- Rent_1)
+
+ V[['alt2']] = -(b_mu_rent + mu_rent_vid1 *Dummy_Video_1 + mu_rent_vid2 * Dummy_Video_2 + mu_rent_no_info * Dummy_no_info +
+ mu_rent_info_nv1 * Dummy_Info_nv1 + mu_rent_info_nv2 * Dummy_Info_nv2 + mu_rent_NR * Z_Mean_NR)*
+ (b_mu_natural * Naturalness_2 + b_mu_walking * WalkingDistance_2 +
+ mu_nat_NR * Naturalness_2 * Z_Mean_NR + mu_wd_NR * WalkingDistance_2 * Z_Mean_NR +
+ mu_nat_vid1 * Naturalness_2 *Dummy_Video_1 + mu_nat_no_info * Naturalness_2 * Dummy_no_info
+ + mu_nat_info_nv1 * Naturalness_2 *Dummy_Info_nv1 + mu_nat_vid2 * Naturalness_2 * Dummy_Video_2
+ + mu_nat_info_nv2 * Naturalness_2 *Dummy_Info_nv2+
+ mu_walking_vid1 * WalkingDistance_2 *Dummy_Video_1 + mu_walking_no_info * WalkingDistance_2 * Dummy_no_info
+ + mu_walking_info_nv1 * WalkingDistance_2 *Dummy_Info_nv1 + mu_walking_vid2 * WalkingDistance_2 * Dummy_Video_2
+ + mu_walking_info_nv2 * WalkingDistance_2 *Dummy_Info_nv2 - Rent_2)
+
+ V[['alt3']] = -(b_mu_rent + mu_rent_vid1 *Dummy_Video_1 + mu_rent_vid2 * Dummy_Video_2 + mu_rent_no_info * Dummy_no_info +
+ mu_rent_info_nv1 * Dummy_Info_nv1 + mu_rent_info_nv2 * Dummy_Info_nv2 + mu_rent_NR * Z_Mean_NR) *
+ (b_ASC_sq + b_mu_natural * Naturalness_3 + b_mu_walking * WalkingDistance_3 +
+ mu_asc_NR * Z_Mean_NR + mu_nat_NR * Naturalness_3 * Z_Mean_NR +
+ mu_wd_NR * WalkingDistance_3 * Z_Mean_NR +
+ mu_nat_vid1 * Naturalness_3 *Dummy_Video_1 + mu_nat_no_info * Naturalness_3 * Dummy_no_info
+ + mu_nat_info_nv1 * Naturalness_3 *Dummy_Info_nv1 + mu_nat_vid2 * Naturalness_3 * Dummy_Video_2
+ + mu_nat_info_nv2 * Naturalness_3 *Dummy_Info_nv2+
+ mu_walking_vid1 * WalkingDistance_3 *Dummy_Video_1 + mu_walking_no_info * WalkingDistance_3 * Dummy_no_info
+ + mu_walking_info_nv1 * WalkingDistance_3 *Dummy_Info_nv1 + mu_walking_vid2 * WalkingDistance_3 * Dummy_Video_2
+ + mu_walking_info_nv2 * WalkingDistance_3 *Dummy_Info_nv2
+ + mu_ASC_sq_vid1 * Dummy_Video_1 + mu_ASC_sq_vid2 * Dummy_Video_2
+ + mu_ASC_sq_no_info * Dummy_no_info + mu_ASC_sq_info_nv1 * Dummy_Info_nv1
+ + mu_ASC_sq_info_nv2 * Dummy_Info_nv2 - 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_NR_case_c_rentINTX = 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_NR_case_c_rentINTX)
+
+