ApolloChoiceModelling forum

Hi Stephane and David,

Hope you are doing great!

I am trying to use the Apollo package (0.2.9) to run the MNL_RP model.

Here is the error message I got from R (version 4.2.0; RStudio2022.02.1):
"Error in apollo_estimateHB(apollo_beta, apollo_fixed, apollo_probabilities, :
SPECIFICATION ISSUE - Parameter b_disc.d2 does not influence the log-likelihood of your model!"


Below is the code I used to run the analysis.

# Import

dce = read.csv(file=paste0(PATHDAT,'dce_wide.csv'), header = T, stringsAsFactors = F)

load(paste0(PATHOBJ,'List of model specifications.RData')) # l.spe
load(paste0(PATHOBJ,'List of attributes.RData')) # l.att


# Manage --------------------------------------------------

###
MODL = 'RPL_effect'
OBJT = 'RPL_effect - Model estimates_child'
SAMP = 'Overall'

### META Variables
N = 'record'
Ta = 'SP_task'
Y = "choice"
nJ = 2

###
nfac = 1000
nBURN = 300*nfac
nCONV = 30*nfac
nSKIP = 10
SEED = 12345


# Specification -----------------------------------------------------------

lD = as.list(NULL)
for (i in 1) {
###
D = dce

###
lA = l.att
S0 = l.spe[['eff0']]
S1 = l.spe[['eff1']]

### Model specification
lX = paste0("b_",S1)
lparam = rep(0,length(lX))
names(lparam) = lX

###
hbd = NULL
hbd = rep('N',length(lX))
names(hbd) = lX

###
l.uti = as.list(NULL)
l.uti = lapply(1:nJ, function(x){
a = paste0(lX,"*alt",x,'_',sub('b\\_','',lX),collapse=" +")
return(a)})

###
nN = length(unique(D[,N]))
nY = nrow(D)
nP = 2*length(hbd)
LL0 = nY*log(1/nJ)
nD = nrow(D)

###
mID = D[!duplicated(D[,'ID']),c('ID',N)]

###
lk = as.list(NULL)
lk[['lA']] = lA
lk[['S0']] = S0
lk[['S1']] = S1
lk[['D']] = D
lk[['nN']] = nN
lk[['nY']] = nY
lk[['nP']] = nP
lk[['LL0']] = LL0
lk[['hbd']] = hbd
lk[['lparam']] = lparam
lk[['l.uti']] = l.uti
lk[['mID']] = mID
lk[['nD']] = nD

lD[] = lk}

# Estimation ------------------------------------------------------------------------

###
l.res = as.list(NULL)

IDXTOT = length(lD)

for (i in IDXTOT){
###
lparam = lD[][['lparam']]
l.uti = lD[][['l.uti']]
hbd = lD[][['hbd']]
database = lD[][['D']]
nD = lD[][['nD']]
nY = lD[][['nY']]
nN = lD[][['nN']]
nP = lD[][['nP']]
LL0 = lD[][['LL0']]
mID = lD[[i]][['mID']]
S0 = lD[[i]][['S0']]
S1 = lD[[i]][['S1']]
lA = lD[[i]][['lA']]

### Initialise code
apollo_initialise()

### Set core controls
apollo_control = list(
modelName =MODL,
modelDescr =MODL,
indivID ='ID',
HB =T)

### Declare parameters
apollo_beta = lparam
apollo_fixed = c()

###
apollo_HB = list(
hbDist =hbd,
gINFOSKIP =nSKIP,
gNCREP =nBURN,
gNEREP =nCONV,
gSeed =SEED,
verbose =F,
nodiagnostics=T,
gFULLCV =F)

### VALIDATE INPUTS
apollo_inputs = apollo_validateInputs()

### DEFINE MODEL
apollo_probabilities = function(apollo_beta, apollo_inputs, functionality="estimate"){
###
apollo_attach(apollo_beta, apollo_inputs)
on.exit(apollo_detach(apollo_beta, apollo_inputs))

### Create list of probabilities P
P = list()

### Likelihood of choices
V = list()
V[['alt1']] = (b_adpro.d2 * alt1_adpro.d2 +
b_inj.d2 * alt1_inj.d2 + b_inj.d3 * alt1_inj.d3 +
b_disc.d2 * alt1_disc.d2 + b_disc.d3 * alt1_disc.d3 +
b_dos.d2 * alt1_dos.d2 + b_dos.d3 * alt1_dos.d3 +
b_visits.d2 * alt1_visits.d2 + b_visits.d3 * alt1_visits.d3)
V[['alt2']] = (b_adpro.d2 * alt2_adpro.d2 +
b_inj.d2 * alt2_inj.d2 + b_inj.d3 * alt2_inj.d3 +
b_disc.d2 * alt2_disc.d2 + b_disc.d3 * alt2_disc.d3 +
b_dos.d2 * alt2_dos.d2 + b_dos.d3 * alt2_dos.d3 +
b_visits.d2 * alt2_visits.d2 + b_visits.d3 * alt2_visits.d3)

### Define MNL settings
mnl_settings = list(
alternatives=c(alt1=1, alt2=2),
avail = list(alt1=1, alt2=1),
choiceVar = choice,
V = V)

### Compute probabilities using MNL model
P[['model']] = apollo_mnl(mnl_settings, functionality)

### Prepare and return outputs of function
P = apollo_prepareProb(P, apollo_inputs, functionality)
return(P)}

### Estimate model
fit = NULL
fit = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs)

.......



Do you have any suggestions to solve this error? Thank you very much in advance for your help

Best regards,
Dian

Hi

this would happen if there are no differences between the alternatives in the associated attribute, i.e. alt1_disc.d2 and alt2_disc.d2

Can you confirm that in your data, these do differ?

Stephane