Alvin
the answers to the first two are in the manual. You can use apollo_unconditionals for both of these.
Regarding the third, purchase price is an attribute of the alternatives and thus included in the within class choice model, not the class allocation model, so it will not lead to such a shift
Stephane
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post-estimation scenario analysis
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Re: post-estimation scenario analysis
Hi stephane ,
thank you for such a fast reply,
I'll go through manual for the first two questions,
regarding third question,
at estimates,
after i increase purchase price by 10%, this is the new result,
so, here as per my understanding can i say class 1 and class 2 are seperate MMNL model ? and in class 1 before applying purchase price by 10%( scenario) the split between alt1 and alt 2 were 0.62 and 0.38 respectively but after applying sceanrio split changed to alt1 = 0.63, alt2 = 0.37 which means this scenario will help in increase alt1 choosing probability by 1% . same like this for class 2, is it ? how can i interpret the result ?
sorry if im repeating my question, im finding it hard to understand what is actually happening here ? where actually the segmentation happening ?
Thank you
Alvin
thank you for such a fast reply,
I'll go through manual for the first two questions,
regarding third question,
at estimates,
Code: Select all
Aggregated prediction for model component: Class_1
at MLE Sampled mean Sampled std.dev. Quantile 0.025 Quantile 0.975
alt1 3218 3192 118.9 2967 3390
alt2 1989 2015 118.9 1817 2240
Average prediction for model component: Class_1
at MLE Sampled mean Sampled std.dev. Quantile 0.025 Quantile 0.975
alt1 0.618 0.6129 0.02283 0.5698 0.6511
alt2 0.382 0.3871 0.02283 0.3489 0.4302
Aggregated prediction for model component: Class_2
at MLE Sampled mean Sampled std.dev. Quantile 0.025 Quantile 0.975
alt1 1231 1236 170.6 974.4 1563
alt2 3976 3971 170.6 3643.8 4233
Average prediction for model component: Class_2
at MLE Sampled mean Sampled std.dev. Quantile 0.025 Quantile 0.975
alt1 0.2364 0.2373 0.03277 0.1871 0.3002
alt2 0.7636 0.7627 0.03277 0.6998 0.8129
Aggregated prediction for model component: model
at MLE Sampled mean Sampled std.dev. Quantile 0.025 Quantile 0.975
alt1 2286 2299 57.12 2204 2428
alt2 2921 2908 57.12 2779 3003
Average prediction for model component: model
at MLE Sampled mean Sampled std.dev. Quantile 0.025 Quantile 0.975
alt1 0.439 0.4415 0.01097 0.4233 0.4663
alt2 0.561 0.5585 0.01097 0.537 0.5767
Code: Select all
Prediction at user provided parameters for model component: Class_1
alt1 alt2
Aggregate 3264.89 1942.11
Average 0.63 0.37
Prediction at user provided parameters for model component: Class_2
alt1 alt2
Aggregate 1290.52 3916.48
Average 0.25 0.75
Prediction at user provided parameters for model component: model
alt1 alt2
Aggregate 2338.55 2868.45
Average 0.45 0.55
sorry if im repeating my question, im finding it hard to understand what is actually happening here ? where actually the segmentation happening ?
Thank you
Alvin
Alvin Joshua
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Re: post-estimation scenario analysis
Hi
there is a choice model in each class. When you change an attribute, the prediction changes in each class, and of course then also in the overall model, which is a weighted average across the classes.
Stephane
there is a choice model in each class. When you change an attribute, the prediction changes in each class, and of course then also in the overall model, which is a weighted average across the classes.
Stephane
Re: post-estimation scenario analysis
alvinej wrote: ↑20 May 2024, 11:32
please see the code below
hi stephane,Code: Select all
parallel::detectCores() #### LOAD LIBRARY AND DEFINE CORE SETTINGS #### # ################################################################# # ### Clear memory rm(list = ls()) ### Load Apollo library library(apollo) library(foreach) library(iterators) library(doParallel) ### Initialise code apollo_initialise() # LN-OC,CT,R_b,CF_b&T-EM BFGS_1500 ### Set core controls apollo_control = list( modelName = "MODEL_X_2000", modelDescr = "MODEL_X_2000", indivID = "Person_ID", nCores = 10, 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 = read.csv("D:\\IST\\Project\\Data\\fourWheelerFaceToFace_V3(cleaning).csv", header = TRUE) database1= read.csv("D:\\IST\\Project\\Data\\fourWheelerFaceToFace_V3(cleaning).csv", header = TRUE) ### for data dictionary, use ?apollo_swissRouteChoiceData # ################################################################# # #### DEFINE MODEL PARAMETERS #### # ################################################################# # ### Vector of parameters, including any that are kept fixed in estimation apollo_beta = c(asc_1_a = 0.329, asc_1_b = -0.682, asc_2_a = 0, asc_2_b = 0, beta_pp_a = -0.005 , beta_pp_b = -0.015 , # beta_oc_a = 0, # beta_oc_b = 0, # betaa_oc_a = -0.054, # log_oc_a_mu = 0, # # log_oc_a_sig = 0, log_oc_b_mu = -5.566, log_oc_b_sig = 3.756, #betaa_oc_b = 0, # Jn_oc_a_mu = 0, # Jn_oc_b_mu = 0, # Jn_oc_a_sig = 0, # Jn_oc_b_sig = 0, # tr_oc_p_a = 0, #tr_oc_p_b = 0, # tr_oc_q_a = 0, # tr_oc_q_b = 0, # beta_ct_a = 0, # beta_ct_b = 0, betaa_ct_a = -0.020, # log_ct_a_mu = 0, # # log_ct_a_sig = 0, log_ct_b_mu = 5.488, log_ct_b_sig =-16.790, # betaa_ct_b = 0, # tr_ct_p_a = 0, #tr_ct_p_b = 0, # tr_ct_q_a = 0, # tr_ct_q_b = 0, # betaa_r_a = 0, # beta_r_b = 0, # log_r_a_mu = 0, # log_r_a_sig = 0, # log_r_b_mu = 0, # # log_r_b_sig = 0, # tr_r_p_a = 0, # tr_r_p_b = 0, tr_r_q_a = 0.129, tr_r_q_b = 0.134, # betaa_r_b = 0, beta_cf_a = 0.112, beta_cf_b = 0.337, # log_cf_a_mu = 0, # log_cf_a_sig = 0, # log_cf_b_mu = 0, # # log_cf_b_sig = 0, # tr_cf_p_a = 0, # tr_cf_p_b = 0, # betaa_cf_a = 0, # tr_cf_q_a = 0, # tr_cf_q_b = 0, # betaa_cf_b = 0, # beta_em_a = 0, # beta_em_b = 0, # log_em_a_mu = -1.9987, # log_em_a_sig = -0.08649, # log_em_b_mu = -10.6245, # # log_em_b_sig = -0.1149, # tr_em_p_a = 0, #tr_em_p_b = 0, # tr_em_q_a = 0, # betaa_em_a = 0, # tr_em_q_b = 0, # beta_priorityLanes_a = 0.215, beta_priorityLanes_b = -0.099, beta_freeParking_a = 0.077, beta_freeParking_b = 0.240, beta_tollExemption_a = 0, beta_tollExemption_b = 0, # delta_a = -0.841, # gamma_knowledgeLow_a = 0, # gamma_knowledgeMid_a = 0, # gamma_KnowledgeHigh_a = 0, gamma_voCoded_a = 0.347, gamma_dailyDistanceLow_a =0 , gamma_dailyDistanceMid_a = -0.669, gamma_dailyDistanceHigh_a = -1.397, gamma_longDistanceLow_a = 0, gamma_longDistanceMid_a = 1.013, gamma_longDistanceHigh_a = 1.732, # gamma_techEnth_a = 0, # gamma_enviEnth_a = 0, # gamma_socIma_a = 0, # gamma_monBen_a = 0, gamma_perFee_a = -0.339, gamma_perRisk_a = -0.608, # gamma_insUti_a = 0, delta_b = 0, # gamma_knowledgeLow_b = 0, # gamma_knowledgeMid_b = 0, # gamma_KnowledgeHigh_b = 0, gamma_voCoded_b = 0, gamma_dailyDistanceLow_b = 0, gamma_dailyDistanceMid_b = 0, gamma_dailyDistanceHigh_b = 0, gamma_longDistanceLow_b = 0, gamma_longDistanceMid_b = 0, gamma_longDistanceHigh_b = 0, # gamma_techEnth_b = 0, # gamma_enviEnth_b = 0, # gamma_socIma_b = 0, # gamma_monBen_b = 0, gamma_perFee_b = 0, gamma_perRisk_b = 0 # gamma_insUti_b = 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_2_a","asc_2_b", "delta_b", "gamma_dailyDistanceLow_a", "gamma_longDistanceLow_a", # "gamma_knowledgeLow_a", # "gamma_knowledgeLow_b", # "gamma_knowledgeMid_b", # "gamma_KnowledgeHigh_b", "gamma_voCoded_b" , "gamma_dailyDistanceLow_b" , "gamma_dailyDistanceMid_b", "gamma_dailyDistanceHigh_b", "gamma_longDistanceLow_b", "gamma_longDistanceMid_b" , "gamma_longDistanceHigh_b", "beta_tollExemption_a", "beta_tollExemption_b", # # "gamma_techEnth_b", # # "gamma_enviEnth_b", # # "gamma_socIma_b", # # "gamma_monBen_b", "gamma_perFee_b", "gamma_perRisk_b" # "gamma_insUti_b" ) # ################################################################# # #### DEFINE RANDOM COMPONENTS #### # ################################################################# # ### Set parameters for generating draws apollo_draws = list( interDrawsType="mlhs", interNDraws= 2000, interUnifDraws=c("draws_ct_a","draws_r_a", "draws_r_b"), interNormDraws=c("draws_oc_a"), # "draws_oc_a", "draws_oc_b", "draws_ct_a", "draws_ct_b", "draws_r_a", "draws_r_b", "draws_cf_a", "draws_cf_b" , "draws_em_a", "draws_em_b" # "draws_oc_a", "draws_ct_a", "draws_r_a", "draws_cf_a", "draws_em_a" intraDrawsType="mlhs", intraNDraws=0, intraUnifDraws=c(), intraNormDraws=c() ) ### Create random parameters apollo_randCoeff = function(apollo_beta, apollo_inputs){ randcoeff = list() # one class lognormal randcoeff[["beta_oc_a"]] = betaa_oc_a + 0*(draws_oc_a) randcoeff[["beta_oc_b"]] = -exp(log_oc_b_mu + log_oc_b_sig*draws_oc_a) randcoeff[["beta_ct_a"]] = betaa_ct_a + 0*(draws_ct_a) randcoeff[["beta_ct_b"]] = -exp(log_ct_b_mu + log_ct_b_sig*draws_ct_a) # randcoeff[["beta_r_a"]] = (betaa_r_a + 0*draws_r_a) # randcoeff[["beta_r_b"]] = exp(log_r_b_mu + log_r_b_sig*draws_r_a) # randcoeff[["beta_cf_a"]] = (betaa_cf_a + 0*draws_cf_a) # randcoeff[["beta_cf_b"]] = exp(log_cf_b_mu + log_cf_b_sig*draws_cf_a) # randcoeff[["beta_em_a"]] = (log_em_a_mu + 0*draws_em_a) # randcoeff[["beta_em_b"]] = -exp(log_em_b_mu + log_em_b_sig*draws_em_a) # lognormal distribution # randcoeff[["beta_oc_a"]] = -exp(log_oc_a_mu + log_oc_a_sig*draws_oc_a) # randcoeff[["beta_oc_b"]] = -exp(log_oc_b_mu + log_oc_b_sig*draws_oc_a) # randcoeff[["beta_ct_a"]] = -exp(log_ct_a_mu + log_ct_a_sig*draws_ct_a) # randcoeff[["beta_ct_b"]] = -exp(log_ct_b_mu + log_ct_b_sig*draws_ct_a) # randcoeff[["beta_r_a"]] = exp(log_r_a_mu + log_r_a_sig*draws_r_a) # randcoeff[["beta_r_b"]] = exp(log_r_b_mu + log_r_b_sig*draws_r_a) # randcoeff[["beta_cf_a"]] = exp(log_cf_a_mu + log_cf_a_sig*draws_cf_a) # randcoeff[["beta_cf_b"]] = exp(log_cf_b_mu + log_cf_b_sig*draws_cf_a) # randcoeff[["beta_em_a"]] = -exp(log_em_a_mu + log_em_a_sig*draws_em_a) # randcoeff[["beta_em_b"]] = -exp(log_em_b_mu + log_em_b_sig*draws_em_a) #triangular distribution # randcoeff[["beta_oc_a"]] = tr_oc_q_a * (draws_oc_a + draws_oc_b)/2 # randcoeff[["beta_oc_b"]] = tr_oc_q_b *(draws_oc_a + draws_oc_b)/2 # randcoeff[["beta_ct_a"]] = tr_ct_q_a * (draws_ct_a + draws_ct_b)/2 # randcoeff[["beta_ct_b"]] = tr_ct_q_b *(draws_ct_a + draws_ct_b)/2 randcoeff[["beta_r_a"]] = tr_r_q_a * (draws_r_a + draws_r_b)/2 randcoeff[["beta_r_b"]] = tr_r_q_b*(draws_r_a + draws_r_b)/2 # randcoeff[["beta_cf_a"]] = tr_cf_q_a *(draws_cf_a + draws_cf_b)/2 # randcoeff[["beta_cf_b"]] = tr_cf_q_b * (draws_cf_a + draws_cf_b)/2 # randcoeff[["beta_em_a"]] = tr_em_q_a * (draws_em_a + draws_em_b)/2 # randcoeff[["beta_em_a"]] = tr_em_q_a* (draws_em_a + draws_em_b)/2 # randcoeff[["beta_em_b"]] = tr_em_q_b * (draws_em_a + draws_em_b)/2 #Johnson's distribution # randcoeff[['beta_oc_a']] = exp(Jn_oc_a_mu + Jn_oc_a_sig*draws_oc_a)/(1 + exp(Jn_oc_a_mu + Jn_oc_a_sig*draws_oc_a)) # randcoeff[['beta_oc_b']] = exp(Jn_oc_b_mu + Jn_oc_b_sig*draws_oc_a)/(1 + exp(Jn_oc_b_mu + Jn_oc_b_sig*draws_oc_a)) return(randcoeff) } # ################################################################# # #### DEFINE LATENT CLASS COMPONENTS #### # ################################################################# # apollo_lcPars = function(apollo_beta, apollo_inputs){ lcpars = list() lcpars[["asc_1"]] = list(asc_1_a, asc_1_b) lcpars[["asc_2"]] = list(asc_2_a, asc_2_b) lcpars[["beta_pp"]] = list(beta_pp_a, beta_pp_b) lcpars[["beta_oc"]] = list(beta_oc_a, beta_oc_b) lcpars[["beta_ct"]] = list(beta_ct_a, beta_ct_b) lcpars[["beta_r"]] = list(beta_r_a, beta_r_b) lcpars[["beta_cf"]] = list(beta_cf_a, beta_cf_b) # lcpars[["beta_em"]] = list(beta_em_a, beta_em_b) lcpars[["beta_priorityLanes"]] = list(beta_priorityLanes_a, beta_priorityLanes_b) lcpars[["beta_freeParking"]] = list(beta_freeParking_a, beta_freeParking_b) lcpars[["beta_tollExemption"]] = list(beta_tollExemption_a, beta_tollExemption_b) V=list() V[["class_a"]] = delta_a + gamma_voCoded_a*evoCoded + gamma_dailyDistanceLow_a*dailyDistanceLow + gamma_dailyDistanceMid_a*dailyDistanceMiddle+ gamma_dailyDistanceHigh_a*dailyDistanceHigh + gamma_longDistanceLow_a*longDistanceLow + gamma_longDistanceMid_a*longDistanceMiddle+ gamma_longDistanceHigh_a*longDistanceHigh + gamma_perFee_a*perFeeML + gamma_perRisk_a*perRiskML #+ gamma_perFee_a*perFeeML + gamma_perRisk_a*perRiskML #+ +gamma_voCoded_a*evoCoded #+ gamma_knowledgeLow_a*knowledgeLow + gamma_knowledgeMid_a*knowledgeMiddle + gamma_KnowledgeHigh_a*knowledgeHigh #+ gamma_monBen_a*monBenML + gamma_perFee_a*perFeeML + gamma_perRisk_a*perRiskML + gamma_insUti_a*insUtiML V[["class_b"]] = delta_b +gamma_voCoded_b*evoCoded + gamma_dailyDistanceLow_b*dailyDistanceLow + gamma_dailyDistanceMid_b*dailyDistanceMiddle+ gamma_dailyDistanceHigh_b*dailyDistanceHigh + gamma_longDistanceLow_b*longDistanceLow + gamma_longDistanceMid_b*longDistanceMiddle+ gamma_longDistanceHigh_b*longDistanceHigh + gamma_perFee_b*perFeeML + gamma_perRisk_b*perRiskML #+ gamma_perFee_b*perFeeML + gamma_perRisk_b*perRiskML #+ + gamma_voCoded_b*evoCoded #+ gamma_monBen_b*monBenML + gamma_perFee_b*perFeeML + gamma_perRisk_b*perRiskML + gamma_insUti_b*insUtiML #+ gamma_knowledgeLow_b*knowledgeLow + gamma_knowledgeMid_b*knowledgeMiddle + gamma_KnowledgeHigh_b*knowledgeHigh + gamma_voCoded_b*evoCoded classAlloc_settings = list( classes = c(class_a=1, class_b=2), utilities = V ) lcpars[["pi_values"]] = apollo_classAlloc(classAlloc_settings) return(lcpars) } # ################################################################# # #### 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() ### Define settings for MNL model component that are generic across classes mnl_settings = list( alternatives = c(alt1=1, alt2=2), avail = list(alt1=1, alt2=1), choiceVar = choiceVehicle ) ### Loop over classes for(s in 1:2){ ### Compute class-specific utilities V=list() V[["alt1"]] = asc_1[[s]] + beta_pp[[s]]*ppEVI + beta_oc[[s]]*ocEV+ beta_ct[[s]]*ctEV/60 + beta_r[[s]]*rEV/100 + beta_cf[[s]]*cfEV + beta_priorityLanes[[s]]*priorityLanes + beta_freeParking[[s]]*freeParking + beta_tollExemption[[s]]*tollExemption V[["alt2"]] = asc_2[[s]] + beta_pp[[s]]*ppCVI + beta_oc[[s]]*ocCV + beta_r[[s]]*rCV/100 mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } ### Compute latent class model probabilities lc_settings = list(inClassProb = P, classProb=pi_values) P[["model"]] = apollo_lc(lc_settings, apollo_inputs, functionality) ### Average across inter-individual draws in class allocation probabilities P[["model"]] = apollo_avgInterDraws(P[["model"]], apollo_inputs, functionality) ### Prepare and return outputs of function P = apollo_prepareProb(P, apollo_inputs, functionality) return(P) } # ################################################################# # #### MODEL ESTIMATION AND OUTPUT #### # ################################################################# # ### Estimate model model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs, estimate_settings = list(estimationRoutine="BFGS",maxIterations= 5000)) ### Show output in screen apollo_modelOutput(model) ### Save output to file(s) apollo_saveOutput(model)
if i want to calculate this in willingness to pay space what should i change ?
Alvin Joshua
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Re: post-estimation scenario analysis
Hi
You don’t need WTP space to calculate WTP. But in order to help you, I need to know what you want to calculate WTP for, i.e. what ratio of parameters?
Stephane
You don’t need WTP space to calculate WTP. But in order to help you, I need to know what you want to calculate WTP for, i.e. what ratio of parameters?
Stephane
Re: post-estimation scenario analysis
hi stephane,
i want to calculate wtp with respect to purchase price.
thank you
Alvin
i want to calculate wtp with respect to purchase price.
thank you
Alvin
Alvin Joshua
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Re: post-estimation scenario analysis
is that beta_pp? and what about the numerator?