# ################################################################# # #### 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 = "Hybrid_with_OL", modelDescr = "Hybrid choice model on drug choice data, using ordered measurement model for indicators", indivID = "ID", nCores = 4, 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_drugChoiceData ### for data dictionary, use ?apollo_drugChoiceData # ################################################################# # #### ANALYSIS OF CHOICES #### # ################################################################# # ### Illustration of how to use apollo_choiceAnalysis with user-defined alternatives. ### This is useful in cases where the alternatives in the data differ ### across tasks. The same approach can then also be used with unlabelled data choiceAnalysis_settings <- list( alternatives = c(Artemis=11, Novum=12, BestValue=21, Supermarket=22, PainAway=23), avail = with(database, list(Artemis = brand_1=="Artemis" | brand_2=="Artemis" , Novum = brand_1=="Novum" | brand_2=="Novum" , BestValue = brand_3=="BestValue" | brand_4=="BestValue" , Supermarket = brand_3=="Supermarket" | brand_4=="Supermarket", PainAway = brand_3=="PainAway" | brand_4=="PainAway" ) ), choiceVar = with(database, 11*((best==1 & brand_1=="Artemis" ) | (best==2 & brand_2=="Artemis" )) + 12*((best==1 & brand_1=="Novum" ) | (best==2 & brand_2=="Novum" )) + 21*((best==3 & brand_3=="BestValue" ) | (best==4 & brand_4=="BestValue" )) + 22*((best==3 & brand_3=="Supermarket") | (best==4 & brand_4=="Supermarket")) + 23*((best==3 & brand_3=="PainAway" ) | (best==4 & brand_4=="PainAway" ))), explanators = database[,c("regular_user","university_educated","over_50")] ) apollo_choiceAnalysis(choiceAnalysis_settings, apollo_control, database) # ################################################################# # #### DEFINE MODEL PARAMETERS #### # ################################################################# # ### Vector of parameters, including any that are kept fixed in estimation apollo_beta = c(b_brand_Artemis = 0, b_brand_Novum = 0, b_brand_BestValue = 0, b_brand_Supermarket= 0, b_brand_PainAway = 0, b_country_CH = 0, b_country_DK = 0, b_country_USA = 0, b_country_IND = 0, b_country_RUS = 0, b_country_BRA = 0, b_char_standard = 0, b_char_fast = 0, b_char_double = 0, b_risk = 0, b_price = 0, lambda = 1, gamma_reg_user = 0, gamma_university = 0, gamma_age_50 = 0, zeta_quality = 1, zeta_ingredient = 1, zeta_patent = 1, zeta_dominance = 1, tau_quality_1 =-2, tau_quality_2 =-1, tau_quality_3 = 1, tau_quality_4 = 2, tau_ingredients_1 =-2, tau_ingredients_2 =-1, tau_ingredients_3 = 1, tau_ingredients_4 = 2, tau_patent_1 =-2, tau_patent_2 =-1, tau_patent_3 = 1, tau_patent_4 = 2, tau_dominance_1 =-2, tau_dominance_2 =-1, tau_dominance_3 = 1, tau_dominance_4 = 2) ### 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("b_brand_Artemis", "b_country_USA", "b_char_standard") # ################################################################# # #### DEFINE RANDOM COMPONENTS #### # ################################################################# # ### Set parameters for generating draws apollo_draws = list( interDrawsType="halton", interNDraws=100, interUnifDraws=c(), interNormDraws=c("eta"), intraDrawsType="", intraNDraws=0, intraUnifDraws=c(), intraNormDraws=c() ) ### Create random parameters apollo_randCoeff=function(apollo_beta, apollo_inputs){ randcoeff = list() randcoeff[["LV"]] = gamma_reg_user*regular_user + gamma_university*university_educated + gamma_age_50*over_50 + eta return(randcoeff) } # ################################################################# # #### 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() ### Likelihood of indicators ol_settings1 = list(outcomeOrdered = attitude_quality, V = zeta_quality*LV, tau = list(tau_quality_1, tau_quality_2, tau_quality_3, tau_quality_4), rows = (task==1), componentName = "indic_quality") ol_settings2 = list(outcomeOrdered = attitude_ingredients, V = zeta_ingredient*LV, tau = list(tau_ingredients_1, tau_ingredients_2, tau_ingredients_3, tau_ingredients_4), rows = (task==1), componentName = "indic_ingredients") ol_settings3 = list(outcomeOrdered = attitude_patent, V = zeta_patent*LV, tau = list(tau_patent_1, tau_patent_2, tau_patent_3, tau_patent_4), rows = (task==1), componentName = "indic_patent") ol_settings4 = list(outcomeOrdered = attitude_dominance, V = zeta_dominance*LV, tau = list(tau_dominance_1, tau_dominance_2, tau_dominance_3, tau_dominance_4), rows = (task==1), componentName = "indic_dominance") P[["indic_quality"]] = apollo_ol(ol_settings1, functionality) P[["indic_ingredients"]] = apollo_ol(ol_settings2, functionality) P[["indic_patent"]] = apollo_ol(ol_settings3, functionality) P[["indic_dominance"]] = apollo_ol(ol_settings4, functionality) ### Likelihood of choices ### List of utilities: these must use the same names as in mnl_settings, order is irrelevant V = list() V[["alt1"]] = ( b_brand_Artemis*(brand_1=="Artemis") + b_brand_Novum*(brand_1=="Novum") + b_country_CH*(country_1=="Switzerland") + b_country_DK*(country_1=="Denmark") + b_country_USA*(country_1=="USA") + b_char_standard*(char_1=="standard") + b_char_fast*(char_1=="fast acting") + b_char_double*(char_1=="double strength") + b_risk*side_effects_1 + b_price*price_1 + lambda*LV ) V[["alt2"]] = ( b_brand_Artemis*(brand_2=="Artemis") + b_brand_Novum*(brand_2=="Novum") + b_country_CH*(country_2=="Switzerland") + b_country_DK*(country_2=="Denmark") + b_country_USA*(country_2=="USA") + b_char_standard*(char_2=="standard") + b_char_fast*(char_2=="fast acting") + b_char_double*(char_2=="double strength") + b_risk*side_effects_2 + b_price*price_2 + lambda*LV ) V[["alt3"]] = ( b_brand_BestValue*(brand_3=="BestValue") + b_brand_Supermarket*(brand_3=="Supermarket") + b_brand_PainAway*(brand_3=="PainAway") + b_country_USA*(country_3=="USA") + b_country_IND*(country_3=="India") + b_country_RUS*(country_3=="Russia") + b_country_BRA*(country_3=="Brazil") + b_char_standard*(char_3=="standard") + b_char_fast*(char_3=="fast acting") + b_risk*side_effects_3 + b_price*price_3 ) V[["alt4"]] = ( b_brand_BestValue*(brand_4=="BestValue") + b_brand_Supermarket*(brand_4=="Supermarket") + b_brand_PainAway*(brand_4=="PainAway") + b_country_USA*(country_4=="USA") + b_country_IND*(country_4=="India") + b_country_RUS*(country_4=="Russia") + b_country_BRA*(country_4=="Brazil") + b_char_standard*(char_4=="standard") + b_char_fast*(char_4=="fast acting") + b_risk*side_effects_4 + b_price*price_4 ) ### Define settings for MNL model component mnl_settings = list( alternatives = c(alt1=1, alt2=2, alt3=3, alt4=4), avail = list(alt1=1, alt2=1, alt3=1, alt4=1), choiceVar = best, utilities = V, componentName = "choice" ) ### Compute probabilities for MNL model component P[["choice"]] = apollo_mnl(mnl_settings, functionality) ### Likelihood of the whole model P = apollo_combineModels(P, apollo_inputs, functionality) ### Take product across observation for same individual P = apollo_panelProd(P, apollo_inputs, functionality) ### Average across inter-individual draws P = apollo_avgInterDraws(P, apollo_inputs, functionality) ### Prepare and return outputs of function P = apollo_prepareProb(P, apollo_inputs, functionality) return(P) } # ################################################################# # #### MODEL ESTIMATION #### # ################################################################# # ### Optional: calculate LL before model estimation # apollo_llCalc(apollo_beta, apollo_probabilities, apollo_inputs) ### Estimate model 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 ---- # ----------------------------------------------------------------- # forecast <- apollo_prediction(model, apollo_probabilities, apollo_inputs, prediction_settings=list(modelComponent="indic_quality")) # ----------------------------------------------------------------- # #---- CONDITIONALS AND UNCONDITIONALS ---- # ----------------------------------------------------------------- # conditionals <- apollo_conditionals(model,apollo_probabilities,apollo_inputs) summary(conditionals) unconditionals <- apollo_unconditionals(model,apollo_probabilities,apollo_inputs) mean(unconditionals[[1]]) sd(unconditionals[[1]]) # ----------------------------------------------------------------- # #---- switch off writing to file ---- # ----------------------------------------------------------------- # apollo_sink()