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mxl_wtp_space_NR_caseA.R

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    mxl_wtp_space_NR_caseA.R 6.02 KiB
    #### Apollo standard script #####
    
    library(apollo) # Load apollo package 
    
    
    
    database <- database_full %>% filter(!is.na(Treatment_A)) %>% 
      mutate(Dummy_Treated = case_when(Treatment_A == "Treated" ~ 1, TRUE ~ 0),
             Dummy_Vol_Treated = case_when(Treatment_A == "Vol_Treated" ~ 1, TRUE ~ 0))
    
    
      #initialize model 
      
      apollo_initialise()
      
      
      ### Set core controls
      apollo_control = list(
        modelName  = "MXL_wtp NR A",
        modelDescr = "MXL_wtp NR Case A",
        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_nat_NR = 0,
                    mu_wd_NR = 0,
                    mu_asc_NR = 0,
                    mu_nat_T = 0,
                    mu_wd_T= 0,
                    mu_asc_T = 0,
                    mu_nat_VT = 0,
                    mu_wd_VT= 0,
                    mu_asc_VT = 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*(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_T * Naturalness_1 * Dummy_Treated + mu_wd_T * WalkingDistance_1 * Dummy_Treated +
                                    mu_nat_VT * Naturalness_1 * Dummy_Vol_Treated + mu_wd_VT * WalkingDistance_1 * Dummy_Vol_Treated
                                  - Rent_1)
        
        V[['alt2']] = -b_mu_rent*(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_T * Naturalness_2 * Dummy_Treated + mu_wd_T * WalkingDistance_2 * Dummy_Treated +
                                    mu_nat_VT * Naturalness_2 * Dummy_Vol_Treated + mu_wd_VT * WalkingDistance_2 * Dummy_Vol_Treated
                                  - Rent_2)
        
        V[['alt3']] = -b_mu_rent*(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_asc_T * Dummy_Treated +
                                    mu_nat_T * Naturalness_3 * Dummy_Treated + mu_wd_T * WalkingDistance_3 * Dummy_Treated +
                                    mu_nat_VT * Naturalness_3 * Dummy_Vol_Treated + mu_wd_VT * WalkingDistance_3 * Dummy_Vol_Treated +
                                    mu_asc_VT * Dummy_Vol_Treated 
                                  - 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_a_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_a_nr)