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/Hybrid choice model_Esitmation Issues

Ask questions about how to estimate models and how to change your settings for estimation.
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dhaka515
Posts: 3
Joined: 02 Feb 2021, 20:47

/Hybrid choice model_Esitmation Issues

Post by dhaka515 »

Hi,
I am trying to run hybrid choice model for a choice experiment data. I followed your example code and manual to set up my model. Unfortunately, I keep getting error message "Log-likelihood calculation fails at values close to the starting values!
In addition: Warning message:
In log(P[[j]]) : NaNs produced"

When I tried "apollo_beta=apollo_searchStart(apollo_beta, apollo_fixed,apollo_probabilities, apollo_inputs)" to find initial values of beta, I get the following error message:
"Testing probability function (apollo_probabilities)
Error: node stack overflow
Execution halted"

Could you please give me suggestions what I can do to address these? If required, I can share some description of the data and code. Thank you.
dpalma
Posts: 190
Joined: 24 Apr 2020, 17:54

Re: /Hybrid choice model_Esitmation Issues

Post by dpalma »

Hi,

Could you please share your code? It is hard to diagnose without looking at the actual code.

Cheers
David
dhaka515
Posts: 3
Joined: 02 Feb 2021, 20:47

Re: /Hybrid choice model_Esitmation Issues

Post by dhaka515 »

Hi,

Thank you very much for the reply. Please see my code below:


### Description of variables
#Choice variable: binary: Status quo (SQ or S), Alternatives (Alt or A)
#Attributes: WeaAbsLin SpeAbsLin NatAbsLin Levy
#Explanatory variables: HHsize, IncCat_Mid, IncCat_High
#Indicator variable: attitude_Epl (rank 1 to 7)


### Clear memory


rm(list = ls())

#### LOAD DATA AND APPLY ANY TRANSFORMATIONS ####
# ################################################################# #



### Load Apollo library
#install.packages("apollo", repos = "http://cran.us.r-project.org")
library(apollo)

### Initialise code
apollo_initialise()

### Set core controls
apollo_control = list(
modelName = "hybrid_model_WTP",
modelDescr = "Hybrid choice model on LCF_WTP",
indivID = "id",
mixing = TRUE,
nCores = 25
)

load("database.RData")

# ################################################################# #



# ################################################################# #
#### ANALYSIS OF CHOICES ####
# ################################################################# #


choiceAnalysis_settings <- list(
alternatives = c(SQ="S",Alt="A"),
avail = list(SQ=1, Alt=1),
choiceVar = database$Choice,
explanators = database[,c("HHsize", "IncCat_Mid", "IncCat_High" )]
)
apollo_choiceAnalysis(choiceAnalysis_settings, apollo_control, database)



# ################################################################# #
#### DEFINE MODEL PARAMETERS ####
# ################################################################# #

### Vector of parameters, including any that are kept fixed in estimation
### Vector of parameters, including any that are kept fixed in estimation
apollo_beta <- c(b_Wea = 0,
b_Spe = 0,
b_Nat = 0,
b_Levy = 0,
b_IncMid = 0,
b_IncHigh = 0,
gamma_Wea_HHsize = 0,gamma_Spe_HHsize = 0,
gamma_Nat_HHsize = 0,
gamma_Wea_MidI = 0,gamma_Spe_MidI = 0,
gamma_Nat_MidI = 0,
gamma_Wea_HighI = 0,gamma_Spe_HighI = 0,
gamma_Nat_HighI = 0,
gamma_LV_HHsize = 0, gamma_LV_MidI = 0,
gamma_LV_HighI = 0,
mu_log_asc = 0,
sigma_log_asc = 1,lambda= 1,
zeta_Epl=1,
tau_Epl_1 =-3, tau_Epl_2 =-3,
tau_Epl_3 = -1, tau_Epl_4 = 1,tau_Epl_5 = 2,
tau_Epl_6 = 3
)
apollo_fixed <- c("mu_log_asc", "sigma_log_asc")


# ################################################################# #
#### DEFINE RANDOM COMPONENTS ####
# ################################################################# #

### Set parameters for generating draws
apollo_draws = list(
interDrawsType="halton",
interNDraws=500,
interNormDraws=c("draws_asc_inter","eta")
)

### Create random parameters
apollo_randCoeff <- function(apollo_beta, apollo_inputs) {
randcoef <- list()
randcoef[['asc']] <- mu_log_asc + b_IncMid * (IncCat=='Mid') + b_IncHigh * (IncCat=='High')-sigma_log_asc*draws_asc_inter
randcoef[["LV"]] = gamma_LV_HHsize*HHsize + gamma_LV_MidI*IncCat_Mid+ gamma_LV_HighI*IncCat_High + eta

return(randcoef)
}



# ################################################################# #
#### GROUP AND VALIDATE INPUTS ####
# ################################################################# #

apollo_inputs = apollo_validateInputs()

# ################################################################# #
#### DEFINE MODEL AND LIKELIHOOD FUNCTION ####
# ################################################################# #
#options(expressions=500000) ## avoid "node stack overflow" error
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
asc_SQ_value = 0
asc_Alt_value = asc

### Likelihood of choices
### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
V <- list()
V[['SQ']] <- asc_SQ_value +
b_Wea * WeaAbsLin_S + b_Spe * SpeAbsLin_S + b_Nat * NatAbsLin_S+ lambda*LV
V[['Alt']] <- asc_Alt_value +
b_Wea * WeaAbsLin_A + b_Spe * SpeAbsLin_A + b_Nat* NatAbsLin_A + b_Levy * Lev_A + lambda*LV


### Define settings for MNL model component

mnl_settings = list(
alternatives = c(SQ="S",Alt="A"),
avail = list(SQ=1, Alt=1),
choiceVar = Choice,
V = V
)


### initial values of beta: when I use this I find error message "node stack overflow"

#apollo_beta=apollo_searchStart(apollo_beta, apollo_fixed,apollo_probabilities, apollo_inputs)
#apollo_beta=apollo_bootstrap(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs)


### Compute probabilities for MNL model component
P[["Choice"]] = apollo_mnl(mnl_settings, functionality)

### Likelihood of indicators
ol_settings1 = list(outcomeOrdered=attitude_Epl,
V=zeta_Epl*LV,
tau=c(tau_Epl_1, tau_Epl_2, tau_Epl_3, tau_Epl_4,tau_Epl_5,tau_Epl_6),
rows=(task==1))


P[["indic_Epl"]] = apollo_ol(ol_settings1, 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)
}

# ################################################################# #
#### CHECK FOR COMPUTATIONAL REQUIREMENTS ####
# ################################################################# #

speedTest_settings=list(
nDrawsTry = c(250, 500, 1000),
nCoresTry = 1:3,
nRep = 10
)

apollo_speedTest(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs, speedTest_settings)

# ################################################################# #
#### MODEL ESTIMATION ####
# ################################################################# #

### 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)
dpalma
Posts: 190
Joined: 24 Apr 2020, 17:54

Re: /Hybrid choice model_Esitmation Issues

Post by dpalma »

Hi,

It seems the starting value of the OL thresholds are invalid. You have:
tau_Epl_1 =-3, tau_Epl_2 =-3,
tau_Epl_3 = -1, tau_Epl_4 = 1,tau_Epl_5 = 2,
tau_Epl_6 = 3

But they need to be all different and increasing, so if you change it to:
tau_Epl_1 =-3, tau_Epl_2 =-2,
tau_Epl_3 = -1, tau_Epl_4 = 1,tau_Epl_5 = 2,
tau_Epl_6 = 3

It should work.

Also, if you define the thresholds as a list instead of a vector, the model might run faster, as follows.
ol_settings1 = list(outcomeOrdered=attitude_Epl,
V=zeta_Epl*LV,
tau=list(tau_Epl_1, tau_Epl_2, tau_Epl_3, tau_Epl_4,tau_Epl_5,tau_Epl_6),
rows=(task==1))

Best
David
dhaka515
Posts: 3
Joined: 02 Feb 2021, 20:47

Re: /Hybrid choice model_Esitmation Issues

Post by dhaka515 »

Hi David,

It works. Thank you very much for your help.

Zabid
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