# ################################################################# # #### LOAD LIBRARY AND DEFINE CORE SETTINGS #### # ################################################################# # ### Clear memory rm(list = ls()) ### Load Apollo library library(apollo) ### Initialise code apollo_initialise() ### Set core controls apollo_control = list( modelName = "HB_MMNL", modelDescr = "HB model on mode choice SP data", indivID = "ID", HB = TRUE, outputDirectory = "output" ) # ################################################################# # #### LOAD DATA AND APPLY ANY TRANSFORMATIONS #### # ################################################################# # ### Loading data from package ### if data is to be loaded from a file (e.g. called data.csv), ### the code would be: database = read.csv("data.csv",header=TRUE) database = apollo_modeChoiceData ### for data dictionary, use ?apollo_modeChoiceData ### Use only SP data database = subset(database,database$SP==1) ### Create new variable with average income database$mean_income = mean(database$income) # ################################################################# # #### DEFINE MODEL PARAMETERS #### # ################################################################# # ### Vector of parameters, including any that are kept fixed in estimation apollo_beta=c(asc_car = 0, asc_bus = 0, asc_air = 0, asc_rail = 0, asc_bus_shift_female = 0, asc_air_shift_female = 0, asc_rail_shift_female = 0, b_tt_car =-3, b_tt_bus =-3, b_tt_air =-3, b_tt_rail =-3, b_tt_shift_business = 0, b_access =-3, b_cost =-3, b_cost_shift_business = 0, cost_income_elast = 0, b_no_frills = 0, b_wifi = 0, b_food = 0) ### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta, use apollo_beta_fixed = c() if none apollo_fixed = c("asc_car","b_no_frills") # ################################################################# # #### HB settings #### # ################################################################# # apollo_HB = list( hbDist = c(asc_car = "NR", asc_bus = "N", asc_air = "N", asc_rail = "N", asc_bus_shift_female = "NR", asc_air_shift_female = "NR", asc_rail_shift_female = "NR", b_tt_car = "LN-", b_tt_bus = "LN-", b_tt_air = "LN-", b_tt_rail = "LN-", b_tt_shift_business = "NR", b_access = "LN-", b_cost = "LN-", b_cost_shift_business = "NR", cost_income_elast = "NR", b_no_frills = "NR", b_wifi = "CN+", b_food = "CN+"), gNCREP = 10000, # burn-in iterations gNEREP = 50000, # post burn-in iterations gINFOSKIP = 500) # ################################################################# # #### GROUP AND VALIDATE INPUTS #### # ################################################################# # apollo_inputs = apollo_validateInputs() # ################################################################# # #### DEFINE MODEL AND LIKELIHOOD FUNCTION #### # ################################################################# # apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){ ### 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() ### Create alternative specific constants and coefficients using interactions with socio-demographics asc_bus_value = asc_bus + asc_bus_shift_female * female asc_air_value = asc_air + asc_air_shift_female * female asc_rail_value = asc_rail + asc_rail_shift_female * female b_tt_car_value = b_tt_car + b_tt_shift_business * business b_tt_bus_value = b_tt_bus + b_tt_shift_business * business b_tt_air_value = b_tt_air + b_tt_shift_business * business b_tt_rail_value = b_tt_rail + b_tt_shift_business * business b_cost_value = ( b_cost + b_cost_shift_business * business ) * ( income / mean_income ) ^ cost_income_elast ### List of utilities: these must use the same names as in mnl_settings, order is irrelevant V = list() V=list() V[["car"]] = asc_car + b_tt_car_value * time_car + b_cost_value * cost_car V[["bus"]] = asc_bus_value + b_tt_bus_value * time_bus + b_access * access_bus + b_cost_value * cost_bus V[["air"]] = asc_air_value + b_tt_air_value * time_air + b_access * access_air + b_cost_value * cost_air + b_no_frills * ( service_air == 1 ) + b_wifi * ( service_air == 2 ) + b_food * ( service_air == 3 ) V[["rail"]] = asc_rail_value + b_tt_rail_value * time_rail + b_access * access_rail + b_cost_value * cost_rail + b_no_frills * ( service_rail == 1 ) + b_wifi * ( service_rail == 2 ) + b_food * ( service_rail == 3 ) ### Define settings for MNL model component mnl_settings = list( alternatives = c(car=1, bus=2, air=3, rail=4), avail = list(car=av_car, bus=av_bus, air=av_air, rail=av_rail), choiceVar = choice, utilities = V ) ### Compute probabilities using MNL model P[["model"]] = apollo_mnl(mnl_settings, functionality) ### Prepare and return outputs of function P = apollo_prepareProb(P, apollo_inputs, functionality) return(P) } # ################################################################# # #### MODEL ESTIMATION #### # ################################################################# # model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs) # ################################################################# # #### MODEL OUTPUTS #### # ################################################################# # # ----------------------------------------------------------------- # #---- FORMATTED OUTPUT (TO SCREEN) ---- # ----------------------------------------------------------------- # apollo_modelOutput(model) # ----------------------------------------------------------------- # #---- FORMATTED OUTPUT (TO FILE, using model name) ---- # ----------------------------------------------------------------- # apollo_saveOutput(model) # ################################################################# # ##### POST-PROCESSING #### # ################################################################# # ### Print outputs of additional diagnostics to new output file (remember to close file writing when complete) apollo_sink() # ----------------------------------------------------------------- # #---- MODEL PREDICTIONS AND ELASTICITY CALCULATIONS ---- # ----------------------------------------------------------------- # ### Use the estimated model to make predictions predictions_base = apollo_prediction(model, apollo_probabilities, apollo_inputs) ### Look at summary of the predicted choice probabilities summary(predictions_base[,3:ncol(predictions_base)]) ### Now imagine the cost for rail increases by 10% database$cost_rail = 1.01*database$cost_rail ### Rerun predictions with the new data, and save into a separate matrix apollo_inputs = apollo_validateInputs() predictions_new = apollo_prediction(model, apollo_probabilities, apollo_inputs) ### Look at summary of the predicted choice probabilities summary(predictions_new[,3:ncol(predictions_new)]) ### Return to original data database$cost_rail = 1/1.01*database$cost_rail apollo_inputs = apollo_validateInputs() ### Compute change in probabilities change=(predictions_new-predictions_base)/predictions_base ### Not interested in chosen alternative now, so drop last column change=change[,-ncol(change)] ### First two columns (change in ID and task) also not needed change=change[,-c(1,2)] ### Look at first individual change[database$ID==1,] ### And person 9, who has all 4 modes available change[database$ID==9,] ### Summary of changes (possible presence of NAs for unavailable alternatives) summary(change) ### Look at mean changes for subsets of the data, ignoring NAs colMeans(change,na.rm=TRUE) colMeans(subset(change,database$business==1),na.rm=TRUE) colMeans(subset(change,database$business==0),na.rm=TRUE) colMeans(subset(change,(database$income=quantile(database$income,0.25))|(database$income<=quantile(database$income,0.75))),na.rm=TRUE) colMeans(subset(change,(database$income>quantile(database$income,0.75))),na.rm=TRUE) ### Compute own elasticity for rail: log(sum(predictions_new[,6])/sum(predictions_base[,6]))/log(1.01) ### Compute cross-elasticities for other modes log(sum(predictions_new[,3])/sum(predictions_base[,3]))/log(1.01) log(sum(predictions_new[,4])/sum(predictions_base[,4]))/log(1.01) log(sum(predictions_new[,5])/sum(predictions_base[,5]))/log(1.01) # ----------------------------------------------------------------- # #---- switch off writing to file ---- # ----------------------------------------------------------------- # apollo_sink()