malemu588@gmail.com wrote: ↑19 Oct 2023, 13:54 Hi Stephane,
Thanks for your time and support in this forum. I see the version I am using is 0.3.0, so not quite sure when you say the error is related to an old version of Apollo. Of course, I will try to update to the newest version (if available) and see if the error can be eliminated.
/Mohammed
anders wrote: ↑19 Oct 2023, 20:37 Thank you very much, Stephane.
I am now defining betas inside apollo_lcPars, it provides me with the same results as my previous code.
I run the following code:
It runs smoothly and I get consistent results, but when I run the unconditionals part, I get the following error message:Code: Select all
### Clear memory rm(list = ls()) gc() ### Load Apollo library #library(apollo) ### Initialise code apollo_initialise() ### Set core controls apollo_control = list( modelName = "LC-MMNL-ANA-8", modelDescr = "", indivID = "id", mixing = TRUE, nCores = 10, outputDirectory = "output" ) # ################################################################# # #### LOAD DATA AND APPLY ANY TRANSFORMATIONS #### # ################################################################# # ### Loading data from database = read.csv("apollo_data_wind_main_full.csv",header=TRUE) #Use subset database = subset(database,database$utvalg==2) # ################################################################# # #### DEFINE MODEL PARAMETERS #### # ################################################################# # ### Vector of parameters apollo_beta = c(mu_twh2_a = 2.9828, mu_twh3_a = 5.1948, mu_twh4_a = 5.9565, b_twh2_b = 0, b_twh3_b = 0, b_twh4_b = 0, mu_turb7_a = -4.7124, mu_turb14_a = -6.9625, mu_turb21_a = -7.8751, b_turb7_b = 0, b_turb14_b = 0, b_turb21_b = 0, b_cost_a = 0.1168, b_cost_b = 0, sigma_twh2_a = 0, sigma_twh3_a = 0, sigma_twh4_a = 0, sigma_turb7_a = 0, sigma_turb14_a = 0, sigma_turb21_a = 0, delta_a = 0.6696, delta_b = -1.0402, delta_c = 0.3473, delta_d = -2.0474, delta_e = 0.6300, delta_f = 1.9412, delta_g = -0.5162) ### 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_twh2_b","b_twh3_b","b_twh4_b","b_turb7_b","b_turb14_b","b_turb21_b","b_cost_b") ### Set parameters for generating draws apollo_draws = list( interDrawsType="mlhs", interNDraws=500, interNormDraws=c("xi_twh2","xi_twh3","xi_twh4","xi_turb7","xi_turb14","xi_turb21") ) ### Create random parameters apollo_randCoeff = function(apollo_beta, apollo_inputs){ randcoeff = list() randcoeff[["b_twh2_a"]] =mu_twh2_a + sigma_twh2_a * xi_twh2 randcoeff[["b_twh3_a"]] = mu_twh3_a + sigma_twh3_a * xi_twh3 randcoeff[["b_twh4_a"]] = mu_twh4_a + sigma_twh4_a * xi_twh4 randcoeff[["b_turb7_a"]] = mu_turb7_a + sigma_turb7_a * xi_turb7 randcoeff[["b_turb14_a"]] = mu_turb14_a + sigma_turb14_a * xi_turb14 randcoeff[["b_turb21_a"]] = mu_turb21_a + sigma_turb21_a * xi_turb21 return(randcoeff) } # ################################################################# # #### DEFINE LATENT CLASS COMPONENTS #### # ################################################################# # apollo_lcPars=function(apollo_beta, apollo_inputs){ lcpars = list() lcpars[["b_twh2" ]] = list(b_twh2_a, b_twh2_b) lcpars[["b_twh3" ]] = list(b_twh3_a, b_twh3_b) lcpars[["b_twh4" ]] = list(b_twh4_a, b_twh4_b) lcpars[["b_turb7" ]] = list(b_turb7_a, b_turb7_b) lcpars[["b_turb14" ]] = list(b_turb14_a, b_turb14_b) lcpars[["b_turb21" ]] = list(b_turb21_a, b_turb21_b) lcpars[["b_cost" ]] = list(b_cost_a, b_cost_b) V=list() V[["class_a"]] = delta_a V[["class_b"]] = delta_b V[["class_c"]] = delta_c V[["class_d"]] = delta_d V[["class_e"]] = delta_e V[["class_f"]] = delta_f V[["class_g"]] = delta_g V[["class_h"]] = 0 ### Settings for class allocation models classAlloc_settings = list( classes = c(class_a=1, class_b=2, class_c=3, class_d=4, class_e=5, class_f=6, class_g=7, class_h=8), 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 = choice ) ### Loop over classes for(s in 1:8){ V = list() if(s==1){ V[['alt1']] = ((b_cost_a*(feeneg_1/100) + b_twh2_a*(twh_1==20) + b_twh3_a*(twh_1==30) + b_twh4_a*(twh_1==40))) V[['alt2']] = ((b_cost_a*(feeneg_2/100) + b_twh2_a*(twh_2==20) + b_twh3_a*(twh_2==30) + b_twh4_a*(twh_2==40) + b_turb7_a*(turbines_2==700) + b_turb14_a*(turbines_2==1400) + b_turb21_a*(turbines_2==2100))) mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } if(s==2){ V[['alt1']] = ((b_cost_b*(feeneg_1/100) + b_twh2_a*(twh_1==20) + b_twh3_a*(twh_1==30) + b_twh4_a*(twh_1==40))) V[['alt2']] = ((b_cost_b*(feeneg_2/100) + b_twh2_a*(twh_2==20) + b_twh3_a*(twh_2==30) + b_twh4_a*(twh_2==40) + b_turb7_a*(turbines_2==700) + b_turb14_a*(turbines_2==1400) + b_turb21_a*(turbines_2==2100))) mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } if(s==3){ V[['alt1']] = ((b_cost_a*(feeneg_1/100) + b_twh2_a*(twh_1==20) + b_twh3_a*(twh_1==30) + b_twh4_a*(twh_1==40))) V[['alt2']] = ((b_cost_a*(feeneg_2/100) + b_twh2_a*(twh_2==20) + b_twh3_a*(twh_2==30) + b_twh4_a*(twh_2==40) + b_turb7_b*(turbines_2==700) + b_turb14_b*(turbines_2==1400) + b_turb21_b*(turbines_2==2100))) mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } if(s==4){ V[['alt1']] = ((b_cost_a*(feeneg_1/100) + b_twh2_b*(twh_1==20) + b_twh3_b*(twh_1==30) + b_twh4_b*(twh_1==40))) V[['alt2']] = ((b_cost_a*(feeneg_2/100) + b_twh2_b*(twh_2==20) + b_twh3_b*(twh_2==30) + b_twh4_b*(twh_2==40) + b_turb7_a*(turbines_2==700) + b_turb14_a*(turbines_2==1400) + b_turb21_a*(turbines_2==2100))) mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } if(s==5){ V[['alt1']] = ((b_cost_a*(feeneg_1/100) + b_twh2_b*(twh_1==20) + b_twh3_b*(twh_1==30) + b_twh4_b*(twh_1==40))) V[['alt2']] = ((b_cost_a*(feeneg_2/100) + b_twh2_b*(twh_2==20) + b_twh3_b*(twh_2==30) + b_twh4_b*(twh_2==40) + b_turb7_b*(turbines_2==700) + b_turb14_b*(turbines_2==1400) + b_turb21_b*(turbines_2==2100))) mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes #P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } if(s==6){ V[['alt1']] = ((b_cost_b*(feeneg_1/100) + b_twh2_b*(twh_1==20) + b_twh3_b*(twh_1==30) + b_twh4_b*(twh_1==40))) V[['alt2']] = ((b_cost_b*(feeneg_2/100) + b_twh2_b*(twh_2==20) + b_twh3_b*(twh_2==30) + b_twh4_b*(twh_2==40) + b_turb7_a*(turbines_2==700) + b_turb14_a*(turbines_2==1400) + b_turb21_a*(turbines_2==2100))) mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } if(s==7){ V[['alt1']] = ((b_cost_b*(feeneg_1/100) + b_twh2_a*(twh_1==20) + b_twh3_a*(twh_1==30) + b_twh4_a*(twh_1==40))) V[['alt2']] = ((b_cost_b*(feeneg_2/100) + b_twh2_a*(twh_2==20) + b_twh3_a*(twh_2==30) + b_twh4_a*(twh_2==40) + b_turb7_b*(turbines_2==700) + b_turb14_b*(turbines_2==1400) + b_turb21_b*(turbines_2==2100))) mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } if(s==8){ V[['alt1']] = ((b_cost_b*(feeneg_1/100) + b_twh2_b*(twh_1==20) + b_twh3_b*(twh_1==30) + b_twh4_b*(twh_1==40))) V[['alt2']] = ((b_cost_b*(feeneg_2/100) + b_twh2_b*(twh_2==20) + b_twh3_b*(twh_2==30) + b_twh4_b*(twh_2==40) + b_turb7_b*(turbines_2==700) + b_turb14_b*(turbines_2==1400) + b_turb21_b*(turbines_2==2100))) mnl_settings$utilities = V mnl_settings$componentName = paste0("Class_",s) ### Compute within-class choice probabilities using MNL model P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality) ### Take product across observation for same individual P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality) ### Average across inter-individual draws within classes #P[[paste0("Class_",s)]] = apollo_avgInterDraws(P[[paste0("Class_",s)]], apollo_inputs, functionality) } } ### Compute latent class model probabilities lc_settings = list(inClassProb = P, classProb=pi_values) P[["model"]] = apollo_lc(lc_settings, apollo_inputs, functionality) ### Prepare and return outputs of function P = apollo_prepareProb(P, apollo_inputs, functionality) return(P) } # ################################################################# # #### EM ESTIMATION FOR COVARIANCE MATRIX #### # ################################################################# # model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs) # ----------------------------------------------------------------- # #---- FORMATTED OUTPUT (TO SCREEN) ---- # ----------------------------------------------------------------- # apollo_saveOutput(model) unconditionals = apollo_unconditionals(model,apollo_probabilities,apollo_inputs)
I have also tried to run an alternative specification of the ANA model that provides the same result, but also the same error message when I run the apollo_unconditionals command. The alternative specification is:Error in !is.na(apollo_draws) && apollo_draws$intraNDraws == 0 :
'length = 5' in coercion to 'logical(1)'
Code: Select all
### Clear memory rm(list = ls()) gc() ### Load Apollo library #library(apollo) ### Initialise code apollo_initialise() ### Set core controls apollo_control = list( modelName = "LC-MMNL-ANA-8", modelDescr = "", indivID = "id", mixing = TRUE, nCores = 10, outputDirectory = "output" ) # ################################################################# # #### LOAD DATA AND APPLY ANY TRANSFORMATIONS #### # ################################################################# # ### Loading data from database = read.csv("apollo_data_wind_main_full.csv",header=TRUE) #Use subset database = subset(database,database$utvalg==2) # ################################################################# # #### DEFINE MODEL PARAMETERS #### # ################################################################# # ### Vector of parameters apollo_beta = c(mu_twh2_a = 2.9828, mu_twh3_a = 5.1948, mu_twh4_a = 5.9565, mu_twh2_b = 0, mu_twh3_b = 0, mu_twh4_b = 0, mu_turb7_a = -4.7124, mu_turb14_a = -6.9625, mu_turb21_a = -7.8751, mu_turb7_b = 0, mu_turb14_b = 0, mu_turb21_b = 0, mu_cost_a = 0.1168, mu_cost_b = 0, sigma_twh2_a = 0, sigma_twh3_a = 0, sigma_twh4_a = 0, sigma_turb7_a = 0, sigma_turb14_a = 0, sigma_turb21_a = 0, delta_a = 0.6696, delta_b = -1.0402, delta_c = 0.3473, delta_d = -2.0474, delta_e = 0.6300, delta_f = 1.9412, delta_g = -0.5162) ### 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("mu_twh2_b","mu_twh3_b","mu_twh4_b","mu_turb7_b","mu_turb14_b","mu_turb21_b","mu_cost_b") ### Set parameters for generating draws apollo_draws = list( interDrawsType="mlhs", interNDraws=500, interNormDraws=c("xi_twh2","xi_twh3","xi_twh4","xi_turb7","xi_turb14","xi_turb21","xi_cost","xi_twh2_b","xi_twh3_b","xi_twh4_b","xi_turb7_b","xi_turb14_b","xi_turb21_b","xi_cost_b") ) ### Create random parameters apollo_randCoeff = function(apollo_beta, apollo_inputs){ randcoeff = list() randcoeff[["b_twh2_a"]] =mu_twh2_a + sigma_twh2_a * xi_twh2 randcoeff[["b_twh3_a"]] = mu_twh3_a + sigma_twh3_a * xi_twh3 randcoeff[["b_twh4_a"]] = mu_twh4_a + sigma_twh4_a * xi_twh4 randcoeff[["b_turb7_a"]] = mu_turb7_a + sigma_turb7_a * xi_turb7 randcoeff[["b_turb14_a"]] = mu_turb14_a + sigma_turb14_a * xi_turb14 randcoeff[["b_turb21_a"]] = mu_turb21_a + sigma_turb21_a * xi_turb21 randcoeff[["b_cost_a"]] =mu_cost_a + 0 * xi_cost randcoeff[["b_twh2_b"]] =mu_twh2_b + 0 * xi_twh2_b randcoeff[["b_twh3_b"]] = mu_twh3_b + 0 * xi_twh3_b randcoeff[["b_twh4_b"]] = mu_twh4_b + 0 * xi_twh4_b randcoeff[["b_turb7_b"]] = mu_turb7_b + 0 * xi_turb7_b randcoeff[["b_turb14_b"]] = mu_turb14_b + 0 * xi_turb14_b randcoeff[["b_turb21_b"]] = mu_turb21_b + 0 * xi_turb21_b randcoeff[["b_cost_b"]] =mu_cost_b + 0 * xi_cost_b return(randcoeff) } # ################################################################# # #### DEFINE LATENT CLASS COMPONENTS #### # ################################################################# # apollo_lcPars=function(apollo_beta, apollo_inputs){ lcpars = list() lcpars[["b_twh2" ]] = list(b_twh2_a, b_twh2_b) lcpars[["b_twh3" ]] = list(b_twh3_a, b_twh3_b) lcpars[["b_twh4" ]] = list(b_twh4_a, b_twh4_b) lcpars[["b_turb7" ]] = list(b_turb7_a, b_turb7_b) lcpars[["b_turb14" ]] = list(b_turb14_a, b_turb14_b) lcpars[["b_turb21" ]] = list(b_turb21_a, b_turb21_b) lcpars[["b_cost" ]] = list(b_cost_a, b_cost_b) V=list() V[["class_a"]] = delta_a V[["class_b"]] = delta_b V[["class_c"]] = delta_c V[["class_d"]] = delta_d V[["class_e"]] = delta_e V[["class_f"]] = delta_f V[["class_g"]] = delta_g V[["class_h"]] = 0 ### Settings for class allocation models classAlloc_settings = list( classes = c(class_a=1, class_b=2, class_c=3, class_d=4, class_e=5, class_f=6, class_g=7, class_h=8), 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 = choice ) ### Loop over classes for(s in 1:8){ V = list() if(s==1){ V[['alt1']] = ((b_cost_a*(feeneg_1/100) + b_twh2_a*(twh_1==20) + b_twh3_a*(twh_1==30) + b_twh4_a*(twh_1==40))) V[['alt2']] = ((b_cost_a*(feeneg_2/100) + b_twh2_a*(twh_2==20) + b_twh3_a*(twh_2==30) + b_twh4_a*(twh_2==40) + b_turb7_a*(turbines_2==700) + b_turb14_a*(turbines_2==1400) + b_turb21_a*(turbines_2==2100))) } if(s==2){ V[['alt1']] = ((b_cost_b*(feeneg_1/100) + b_twh2_a*(twh_1==20) + b_twh3_a*(twh_1==30) + b_twh4_a*(twh_1==40))) V[['alt2']] = ((b_cost_b*(feeneg_2/100) + b_twh2_a*(twh_2==20) + b_twh3_a*(twh_2==30) + b_twh4_a*(twh_2==40) + b_turb7_a*(turbines_2==700) + b_turb14_a*(turbines_2==1400) + b_turb21_a*(turbines_2==2100))) } if(s==3){ V[['alt1']] = ((b_cost_a*(feeneg_1/100) + b_twh2_a*(twh_1==20) + b_twh3_a*(twh_1==30) + b_twh4_a*(twh_1==40))) V[['alt2']] = ((b_cost_a*(feeneg_2/100) + b_twh2_a*(twh_2==20) + b_twh3_a*(twh_2==30) + b_twh4_a*(twh_2==40) + b_turb7_b*(turbines_2==700) + b_turb14_b*(turbines_2==1400) + b_turb21_b*(turbines_2==2100))) } if(s==4){ V[['alt1']] = ((b_cost_a*(feeneg_1/100) + b_twh2_b*(twh_1==20) + b_twh3_b*(twh_1==30) + b_twh4_b*(twh_1==40))) V[['alt2']] = ((b_cost_a*(feeneg_2/100) + b_twh2_b*(twh_2==20) + b_twh3_b*(twh_2==30) + b_twh4_b*(twh_2==40) + b_turb7_a*(turbines_2==700) + b_turb14_a*(turbines_2==1400) + b_turb21_a*(turbines_2==2100))) } if(s==5){ V[['alt1']] = ((b_cost_a*(feeneg_1/100) + b_twh2_b*(twh_1==20) + b_twh3_b*(twh_1==30) + b_twh4_b*(twh_1==40))) V[['alt2']] = ((b_cost_a*(feeneg_2/100) + b_twh2_b*(twh_2==20) + b_twh3_b*(twh_2==30) + b_twh4_b*(twh_2==40) + b_turb7_b*(turbines_2==700) + b_turb14_b*(turbines_2==1400) + b_turb21_b*(turbines_2==2100))) } if(s==6){ V[['alt1']] = ((b_cost_b*(feeneg_1/100) + b_twh2_b*(twh_1==20) + b_twh3_b*(twh_1==30) + b_twh4_b*(twh_1==40))) V[['alt2']] = ((b_cost_b*(feeneg_2/100) + b_twh2_b*(twh_2==20) + b_twh3_b*(twh_2==30) + b_twh4_b*(twh_2==40) + b_turb7_a*(turbines_2==700) + b_turb14_a*(turbines_2==1400) + b_turb21_a*(turbines_2==2100))) } if(s==7){ V[['alt1']] = ((b_cost_b*(feeneg_1/100) + b_twh2_a*(twh_1==20) + b_twh3_a*(twh_1==30) + b_twh4_a*(twh_1==40))) V[['alt2']] = ((b_cost_b*(feeneg_2/100) + b_twh2_a*(twh_2==20) + b_twh3_a*(twh_2==30) + b_twh4_a*(twh_2==40) + b_turb7_b*(turbines_2==700) + b_turb14_b*(turbines_2==1400) + b_turb21_b*(turbines_2==2100))) } if(s==8){ V[['alt1']] = ((b_cost_b*(feeneg_1/100) + b_twh2_b*(twh_1==20) + b_twh3_b*(twh_1==30) + b_twh4_b*(twh_1==40))) V[['alt2']] = ((b_cost_b*(feeneg_2/100) + b_twh2_b*(twh_2==20) + b_twh3_b*(twh_2==30) + b_twh4_b*(twh_2==40) + b_turb7_b*(turbines_2==700) + b_turb14_b*(turbines_2==1400) + b_turb21_b*(turbines_2==2100))) } 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) ### Prepare and return outputs of function P = apollo_prepareProb(P, apollo_inputs, functionality) return(P) } # ################################################################# # #### EM ESTIMATION FOR COVARIANCE MATRIX #### # ################################################################# # model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs) # ----------------------------------------------------------------- # #---- FORMATTED OUTPUT (TO SCREEN) ---- # ----------------------------------------------------------------- # apollo_saveOutput(model) unconditionals = apollo_unconditionals(model,apollo_probabilities,apollo_inputs)