ApolloChoiceModelling forum

Hi,

I am fitting a latent class model on DCE data with dual response using the apollo_combineModels function (one model with forced choice and one model with additional opt-out option). When I run the model, the output returns "not applicable" for the Rho-squares and the "NA" for the BIC. The output does return values for the LL and AIC. When I run both models separately, the Rho-squares and BIC are estimated.

Below is the code I used and some of the output. Can you help to figure out why the Rho-squares and BIC are not applicable?

Thank you in advance!

Iris

############################### 3 classes ######################################
rm(list = ls())
gc()

#load package
library(apollo)

#detach variables
apollo_initialise()

#Set core controls
apollo_control = list(
modelName = "LCA_DCE_dualresponse_3classes_syn",
modelDescr = "LCA model with dual response 3 classes synthetic data",
indivID = "ID",
nCores = 4,
outputDirectory = "Resultaten"
)

database <- readRDS("Data/database.rds") %>% arrange(ID)

#Define parameters and starting values
apollo_beta <- c(
asc_1 = 0,
asc_2 = 0,
asc_3 = 0,
beta_benefits_a = 0,
beta_benefits_b = 0,
beta_benefits_c = 0,
beta_equal_a = 0,
beta_equal_b = 0,
beta_equal_c = 0,
beta_high_a = 0,
beta_high_b = 0,
beta_high_c = 0,
beta_low_a = 0,
beta_low_b = 0,
beta_low_c = 0,
beta_cost_a = 0,
beta_cost_b = 0,
beta_cost_c = 0,
beta_type_a = 0,
beta_type_b = 0,
beta_type_c = 0,
delta_a = 0,
delta_b = 0,
delta_c = 0,
mu_forced = 1,
mu_optout = 1
)

#Define which parameters to keep fixed at starting values
#Set beta_equal as reference for the categorical income attribute (consisting of beta_equal, beta_low, beta_high)
apollo_fixed = c("beta_equal_a", "beta_equal_b", "beta_equal_c",
"delta_a", "asc_1", "mu_forced")

#Define function apollo_lcPars (used by the LCA model)
apollo_lcPars = function(apollo_beta, apollo_inputs) {
lcpars = list()
lcpars[["beta_benefits"]] = list(beta_benefits_a, beta_benefits_b, beta_benefits_c)
lcpars[["beta_cost"]] = list(beta_cost_a, beta_cost_b, beta_cost_c)
lcpars[["beta_type"]] = list(beta_type_a, beta_type_b, beta_type_c)
lcpars[["beta_equal"]] = list(beta_equal_a, beta_equal_b, beta_equal_c)
lcpars[["beta_low"]] = list(beta_low_a, beta_low_b, beta_low_c)
lcpars[["beta_high"]] = list(beta_high_a, beta_high_b,beta_high_c)

#Utilities of class allocation model
V = list()
V[["class_a"]] = delta_a
V[["class_b"]] = delta_b
V[["class_c"]] = delta_c

#Settings for class allocation models
classAlloc_settings = list(
classes = c(class_a = 1, class_b = 2, class_c = 3),
utilities = V
)
lcpars[["pi_values"]] = apollo_classAlloc(classAlloc_settings)
return(lcpars)

}

apollo_inputs = apollo_validateInputs()

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()
P_temp = list()

### Loop over classes
for(s in 1:length(pi_values)){

### Compute class-specific utilities
V=list()
V[["alt1"]] = asc_1 + beta_benefits[[s]]*benefits_1 + beta_cost[[s]]*cost_1 + beta_type[[s]]*type_1 + beta_equal[[s]]*(income_1 == 0) + beta_high[[s]]*(income_1 == 1) + beta_low[[s]]*(income_1 == 2)
V[["alt2"]] = asc_2 + beta_benefits[[s]]*benefits_2 + beta_cost[[s]]*cost_2 + beta_type[[s]]*type_2 + beta_equal[[s]]*(income_2 == 0) + beta_high[[s]]*(income_2 == 1) + beta_low[[s]]*(income_2 == 2)
V[["alt3"]] = asc_3

### Define settings for MNL model component that are generic across classes (forced)
mnl_settings_forced = list(
alternatives = c(alt1=1, alt2=2),
avail = list(alt1=1, alt2=1),
choiceVar = choice,
utilities = list(
alt1 = mu_forced*V[["alt1"]],
alt2 = mu_forced*V[["alt2"]]),
rows = (forced == 1)
)

P_temp[[paste0("forced_class_",s)]] = apollo_mnl(mnl_settings_forced, functionality)

### Define settings for MNL model component that are generic across classes (optout)
mnl_settings_optout = list(
alternatives = c(alt1=1, alt2=2, alt3=3),
avail = list(alt1=1, alt2=1, alt3=1),
choiceVar = choice,
utilities = list(
alt1 = mu_optout*V[["alt1"]],
alt2 = mu_optout*V[["alt2"]],
alt3 = mu_optout*V[["alt3"]]),
rows = (optout == 1)
)

P_temp[[paste0("optout_class_",s)]] = apollo_mnl(mnl_settings_optout, functionality)

### Combined model
P[[paste0("Class_",s)]] = apollo_combineModels(
P_temp, apollo_inputs, functionality,
components=c(paste0("forced_class_",s),paste0("optout_class_",s)),asList=FALSE)


### Take product across observation for same individual
P[[paste0("Class_",s)]] = apollo_panelProd(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)
}

### Search starting values
apollo_beta=apollo_searchStart(apollo_beta, apollo_fixed,apollo_probabilities,
apollo_inputs,searchStart_settings = list(nCandidates=10))
### model estimation
model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs)

apollo_modelOutput(model)
apollo_saveOutput(model)

########################### OUTPUT ############################################


Model name : LCA_DCE_dualresponse_3classes
Model description : LCA model with dual response 3 classes
Model run at : 2024-08-08 10:43:03.882494
Estimation method : bgw
Model diagnosis : Relative function convergence
Optimisation diagnosis : Maximum found
hessian properties : Negative definite
maximum eigenvalue : -31.25857
reciprocal of condition number : 8.64217e-06
Number of individuals : 484
Number of rows in database : 11616
Number of modelled outcomes : 0

Number of cores used : 4
Model without mixing

LL(start) : -8564.26
LL (whole model) at equal shares, LL(0) : -10406.54
LL (whole model) at observed shares, LL(C) : -10278.81
LL(final, whole model) : -8564.25
Rho-squared vs equal shares : Not applicable
Adj.Rho-squared vs equal shares : Not applicable
Rho-squared vs observed shares : Not applicable
Adj.Rho-squared vs observed shares : Not applicable
AIC : 17168.5
BIC : NA

LL(0,Class_1) : -10406.54
LL(final,Class_1) : -11325.7
LL(0,Class_2) : -10406.54
LL(final,Class_2) : -10167.25
LL(0,Class_3) : -10406.54
LL(final,Class_3) : -10721.58

Hi

I can look into this if you can share your code and data with me outside the forum

Thanks

Stephane

Hi

in your case, Apollo fails to do these calculations automatically. We will add some further flexibility in future releases. For now, adding this after estimation (and before apollo_modelOutput) will resolve it for you (countAlt is the number of alternatives):
Stephane

Perfect, thank you Stephane!

Just to be sure: in our dual-response model, the forced model has 2 alternatives and the opt-out model has 3 alternatives. Does this matter for countAlt?

Iris

ah, tricky one. I think for rho2C, I would then just use 3, as they would be generic constants