Error in Latent Class Nested Logit Model- NAN
Posted: 15 Aug 2022, 19:00
Dear all,
Apologies if this is in the manual, but I've been searching all day and I can't find it.
I want to create a model to analyze heterogeneity between classes and substitution between choices by using latent class analysis with nested logit model. But I am having some problems running this model (There are many "NANs" in the results). I copied my codes and results below, I appreciate if you can have a look and advise what could be the issue?
male(binary: 1 / 0)
Thanks and regards,
Chunyu
Apologies if this is in the manual, but I've been searching all day and I can't find it.
I want to create a model to analyze heterogeneity between classes and substitution between choices by using latent class analysis with nested logit model. But I am having some problems running this model (There are many "NANs" in the results). I copied my codes and results below, I appreciate if you can have a look and advise what could be the issue?
male(binary: 1 / 0)
Thanks and regards,
Chunyu
Code: Select all
[### Clear memory
rm(list = ls())
### Load Apollo library
library(apollo)
### Initialise code
apollo_initialise()
apollo_control = list(
modelName ="Apollo_example_20_LC-NL",
modelDescr ="LC model with class allocation model",
indivID ="ID",
nCores = 4
)
database = read.csv
database = subset(database,database$SP==1)
## scaled variables
apollo_beta = c(
asc_DF_c1 = 0.6,
asc_HF_c1 = 0.2,
b_cost_c1 = 0,
b_rat_c1 = 0,
b_trf_c1 = 0,
b_wat_c1 = 0,
b_ser_P_c1 = 0,
b_ser_A_c1 = 0,
b_ser_G_c1 = 0,
b_envi_P_c1 = 0,
b_envi_A_c1 = 0,
b_envi_G_c1 = 0,
lambda_A_c1 = 1,
lambda_B_c1 = 0.5,
delta_c1 = 0,
gamma_male_c1 = 0,
asc_DF_c2 = 0.3,
asc_HF_c2 = 0.2,
b_cost_c2 = 0,
b_rat_c2 = 0,
b_trf_c2 = 0,
b_wat_c2 = 0,
b_ser_P_c2 = 0,
b_ser_A_c2 = 0,
b_ser_G_c2 = 0,
b_envi_P_c2 = 0,
b_envi_A_c2 = 0,
b_envi_G_c2 = 0,
lambda_A_c2 = 1,
lambda_B_c2 = 0.5,
delta_c2 = 0,
gamma_male_c2 = 0,
asc_DF_c3 = 0,
asc_HF_c3 = 0,
b_cost_c3 = 0,
b_rat_c3 = 0,
b_trf_c3 = 0,
b_wat_c3 = 0,
b_ser_P_c3 = 0,
b_ser_A_c3 = 0,
b_ser_G_c3 = 0,
b_envi_P_c3 = 0,
b_envi_A_c3 = 0,
b_envi_G_c3 = 0,
lambda_A_c3 = 1,
lambda_B_c3 = 0.5,
delta_c3 = 0,
gamma_male_c3 = 0
)
apollo_fixed = c("delta_c3","lambda_A_c1","lambda_A_c2","lambda_A_c3","gamma_male_c3")
## Define latent class components
apollo_lcPars = function(apollo_beta, apollo_inputs){
lcpars = list()
lcpars[["b_cost"]] = list(b_cost_c1,b_cost_c2,b_cost_c3)
lcpars[["b_rat"]] = list(b_rat_c1,b_rat_c2,b_rat_c3)
lcpars[["b_trf"]] = list(b_trf_c1,b_trf_c2,b_trf_c3)
lcpars[["b_wat"]] = list(b_wat_c1,b_wat_c2,b_wat_c3)
lcpars[["b_ser_P"]] = list(b_ser_P_c1,b_ser_P_c2,b_ser_P_c3)
lcpars[["b_ser_A"]] = list(b_ser_A_c1,b_ser_A_c2,b_ser_A_c3)
lcpars[["b_ser_G"]] = list(b_ser_G_c1,b_ser_G_c2,b_ser_G_c3)
lcpars[["b_envi_P"]] = list(b_envi_P_c1,b_envi_P_c2,b_envi_P_c3)
lcpars[["b_envi_A"]] = list(b_envi_A_c1,b_envi_A_c2,b_envi_A_c3)
lcpars[["b_envi_G"]] = list(b_envi_G_c1,b_envi_G_c2,b_envi_G_c3)
lcpars[["asc_DF"]] = list(asc_DF_c1,asc_DF_c2,asc_DF_c3)
lcpars[["asc_HF"]] = list(asc_HF_c1,asc_HF_c2,asc_HF_c3)
#Utilities of class allocation model
V = list()
V[["class_1"]] = delta_c1 + gamma_male_c1*male
V[["class_2"]] = delta_c2 + gamma_male_c2*male
V[["class_3"]] = delta_c3 + gamma_male_c3*male
classAlloc_settings = list(
classes = c(class_1=1, class_2=2, class_3=3),
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()
### Specify nests for NL model
nlNests = list()
nlNests[[1]] = list(root=1, A_c1=lambda_A_c1, B_c1 = lambda_B_c1)
nlNests[[2]] = list(root=1, A_c2=lambda_A_c2, B_c2 = lambda_B_c2)
nlNests[[3]] = list(root=1, A_c3=lambda_A_c3, B_c3 = lambda_B_c3)
nlStructure = list()
nlStructure[[1]] = list()
nlStructure[[2]] = list()
nlStructure[[3]] = list()
nlStructure[[1]][["root"]] = c("A_c1","B_c1")
nlStructure[[2]][["root"]] = c("A_c2","B_c2")
nlStructure[[3]][["root"]] = c("A_c3","B_c3")
nlStructure[[1]][["A_c1"]] = c("HF")
nlStructure[[1]][["B_c1"]] = c("DF","FF")
nlStructure[[2]][["A_c2"]] = c("HF")
nlStructure[[2]][["B_c2"]] = c("DF","FF")
nlStructure[[3]][["A_c3"]] = c("HF")
nlStructure[[3]][["B_c3"]] = c("DF","FF")
nl_settings <- list(
alternatives = c(FF=1, DF=2, HF=3),
avail = list(FF=av_FF, DF=av_DF, HF=av_HF),
choiceVar = choice,
nlNests = nlNests,
nlStructure = nlStructure
)
### Loop over classes
s=1
while(s<=3){
### Compute class-specific utilities
V=list()
V[['FF']] = b_cost[[s]] * cost_FF + b_rat[[s]] * rating_FF + b_trf[[s]] * trf_FF + b_wat[[s]] * wat_FF + b_ser_P[[s]] * (service_FF==1) + b_ser_A[[s]] * (service_FF==2) + b_ser_G[[s]] * (service_FF==3) + b_envi_P[[s]] * (envi_FF==1) + b_envi_A[[s]] * (envi_FF==2) + b_envi_G[[s]] * (envi_FF==3)
V[['DF']] = asc_DF[[s]] + b_cost[[s]] * cost_DF + b_rat[[s]] * rating_DF + b_trf[[s]] * trf_DF + b_wat[[s]] * wat_DF + b_ser_P[[s]] * (service_DF==1) + b_ser_A[[s]] * (service_DF==2) + b_ser_G[[s]] * (service_DF==3) + b_envi_P[[s]] * (envi_DF==1) + b_envi_A[[s]] * (envi_DF==2) + b_envi_G[[s]] * (envi_DF==3)
V[['HF']] = asc_HF[[s]] + b_cost[[s]] * cost_HF + b_rat[[s]] * rating_HF + b_trf[[s]] * trf_HF + b_wat[[s]] * wat_HF + b_ser_P[[s]] * (service_HF==1) + b_ser_A[[s]] * (service_HF==2) + b_ser_G[[s]] * (service_HF==3) + b_envi_P[[s]] * (envi_HF==1) + b_envi_A[[s]] * (envi_HF==2) + b_envi_G[[s]] * (envi_HF==3)
nl_settings$V = V
nl_settings$nlNests = nlNests[[s]]
nl_settings$nlStructure = nlStructure[[s]]
P[[s]] = apollo_nl(nl_settings, functionality)
P[[s]] = apollo_panelProd(P[[s]],apollo_inputs,functionality)
s=s+1
}
lc_settings = list(inClassProb = P, classProb=pi_values)
P[["model"]] = apollo_lc(lc_settings,apollo_inputs,functionality)
P = apollo_prepareProb(P,apollo_inputs,functionality)
}
model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities,apollo_inputs)
apollo_modelOutput(model,modelOutput_settings = list(printPVal=TRUE))]
[code][### Clear memory
rm(list = ls())
### Load Apollo library
library(apollo)
### Initialise code
apollo_initialise()
apollo_control = list(
modelName ="Apollo_example_20_LC-NL",
modelDescr ="LC model with class allocation model",
indivID ="ID",
nCores = 4
)
database = read.csv
database = subset(database,database$SP==1)
## scaled variables
apollo_beta = c(
asc_DF_c1 = 0.6,
asc_HF_c1 = 0.2,
b_cost_c1 = 0,
b_rat_c1 = 0,
b_trf_c1 = 0,
b_wat_c1 = 0,
b_ser_P_c1 = 0,
b_ser_A_c1 = 0,
b_ser_G_c1 = 0,
b_envi_P_c1 = 0,
b_envi_A_c1 = 0,
b_envi_G_c1 = 0,
lambda_A_c1 = 1,
lambda_B_c1 = 0.5,
delta_c1 = 0,
gamma_male_c1 = 0,
asc_DF_c2 = 0.3,
asc_HF_c2 = 0.2,
b_cost_c2 = 0,
b_rat_c2 = 0,
b_trf_c2 = 0,
b_wat_c2 = 0,
b_ser_P_c2 = 0,
b_ser_A_c2 = 0,
b_ser_G_c2 = 0,
b_envi_P_c2 = 0,
b_envi_A_c2 = 0,
b_envi_G_c2 = 0,
lambda_A_c2 = 1,
lambda_B_c2 = 0.5,
delta_c2 = 0,
gamma_male_c2 = 0,
asc_DF_c3 = 0,
asc_HF_c3 = 0,
b_cost_c3 = 0,
b_rat_c3 = 0,
b_trf_c3 = 0,
b_wat_c3 = 0,
b_ser_P_c3 = 0,
b_ser_A_c3 = 0,
b_ser_G_c3 = 0,
b_envi_P_c3 = 0,
b_envi_A_c3 = 0,
b_envi_G_c3 = 0,
lambda_A_c3 = 1,
lambda_B_c3 = 0.5,
delta_c3 = 0,
gamma_male_c3 = 0
)
apollo_fixed = c("delta_c3","lambda_A_c1","lambda_A_c2","lambda_A_c3","gamma_male_c3")
## Define latent class components
apollo_lcPars = function(apollo_beta, apollo_inputs){
lcpars = list()
lcpars[["b_cost"]] = list(b_cost_c1,b_cost_c2,b_cost_c3)
lcpars[["b_rat"]] = list(b_rat_c1,b_rat_c2,b_rat_c3)
lcpars[["b_trf"]] = list(b_trf_c1,b_trf_c2,b_trf_c3)
lcpars[["b_wat"]] = list(b_wat_c1,b_wat_c2,b_wat_c3)
lcpars[["b_ser_P"]] = list(b_ser_P_c1,b_ser_P_c2,b_ser_P_c3)
lcpars[["b_ser_A"]] = list(b_ser_A_c1,b_ser_A_c2,b_ser_A_c3)
lcpars[["b_ser_G"]] = list(b_ser_G_c1,b_ser_G_c2,b_ser_G_c3)
lcpars[["b_envi_P"]] = list(b_envi_P_c1,b_envi_P_c2,b_envi_P_c3)
lcpars[["b_envi_A"]] = list(b_envi_A_c1,b_envi_A_c2,b_envi_A_c3)
lcpars[["b_envi_G"]] = list(b_envi_G_c1,b_envi_G_c2,b_envi_G_c3)
lcpars[["asc_DF"]] = list(asc_DF_c1,asc_DF_c2,asc_DF_c3)
lcpars[["asc_HF"]] = list(asc_HF_c1,asc_HF_c2,asc_HF_c3)
#Utilities of class allocation model
V = list()
V[["class_1"]] = delta_c1 + gamma_male_c1*male
V[["class_2"]] = delta_c2 + gamma_male_c2*male
V[["class_3"]] = delta_c3 + gamma_male_c3*male
classAlloc_settings = list(
classes = c(class_1=1, class_2=2, class_3=3),
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()
### Specify nests for NL model
nlNests = list()
nlNests[[1]] = list(root=1, A_c1=lambda_A_c1, B_c1 = lambda_B_c1)
nlNests[[2]] = list(root=1, A_c2=lambda_A_c2, B_c2 = lambda_B_c2)
nlNests[[3]] = list(root=1, A_c3=lambda_A_c3, B_c3 = lambda_B_c3)
nlStructure = list()
nlStructure[[1]] = list()
nlStructure[[2]] = list()
nlStructure[[3]] = list()
nlStructure[[1]][["root"]] = c("A_c1","B_c1")
nlStructure[[2]][["root"]] = c("A_c2","B_c2")
nlStructure[[3]][["root"]] = c("A_c3","B_c3")
nlStructure[[1]][["A_c1"]] = c("HF")
nlStructure[[1]][["B_c1"]] = c("DF","FF")
nlStructure[[2]][["A_c2"]] = c("HF")
nlStructure[[2]][["B_c2"]] = c("DF","FF")
nlStructure[[3]][["A_c3"]] = c("HF")
nlStructure[[3]][["B_c3"]] = c("DF","FF")
nl_settings <- list(
alternatives = c(FF=1, DF=2, HF=3),
avail = list(FF=av_FF, DF=av_DF, HF=av_HF),
choiceVar = choice,
nlNests = nlNests,
nlStructure = nlStructure
)
### Loop over classes
s=1
while(s<=3){
### Compute class-specific utilities
V=list()
V[['FF']] = b_cost[[s]] * cost_FF + b_rat[[s]] * rating_FF + b_trf[[s]] * trf_FF + b_wat[[s]] * wat_FF + b_ser_P[[s]] * (service_FF==1) + b_ser_A[[s]] * (service_FF==2) + b_ser_G[[s]] * (service_FF==3) + b_envi_P[[s]] * (envi_FF==1) + b_envi_A[[s]] * (envi_FF==2) + b_envi_G[[s]] * (envi_FF==3)
V[['DF']] = asc_DF[[s]] + b_cost[[s]] * cost_DF + b_rat[[s]] * rating_DF + b_trf[[s]] * trf_DF + b_wat[[s]] * wat_DF + b_ser_P[[s]] * (service_DF==1) + b_ser_A[[s]] * (service_DF==2) + b_ser_G[[s]] * (service_DF==3) + b_envi_P[[s]] * (envi_DF==1) + b_envi_A[[s]] * (envi_DF==2) + b_envi_G[[s]] * (envi_DF==3)
V[['HF']] = asc_HF[[s]] + b_cost[[s]] * cost_HF + b_rat[[s]] * rating_HF + b_trf[[s]] * trf_HF + b_wat[[s]] * wat_HF + b_ser_P[[s]] * (service_HF==1) + b_ser_A[[s]] * (service_HF==2) + b_ser_G[[s]] * (service_HF==3) + b_envi_P[[s]] * (envi_HF==1) + b_envi_A[[s]] * (envi_HF==2) + b_envi_G[[s]] * (envi_HF==3)
nl_settings$V = V
nl_settings$nlNests = nlNests[[s]]
nl_settings$nlStructure = nlStructure[[s]]
P[[s]] = apollo_nl(nl_settings, functionality)
P[[s]] = apollo_panelProd(P[[s]],apollo_inputs,functionality)
s=s+1
}
lc_settings = list(inClassProb = P, classProb=pi_values)
P[["model"]] = apollo_lc(lc_settings,apollo_inputs,functionality)
P = apollo_prepareProb(P,apollo_inputs,functionality)
}
model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities,apollo_inputs)
apollo_modelOutput(model,modelOutput_settings = list(printPVal=TRUE))]
Here are the results:
Estimates:
Estimate s.e. t.rat.(0) p(1-sided) Rob.s.e. Rob.t.rat.(0) p(1-sided)
asc_DF_c1 1.710023 0.489840 3.4910 2.4063e-04 0.864450 1.978163 0.023955
asc_HF_c1 0.493633 0.525809 0.9388 0.173915 0.954244 0.517303 0.302472
b_cost_c1 -0.003616 0.001289 -2.8056 0.002511 0.002178 -1.659864 0.048471
b_rat_c1 0.349926 0.069816 5.0121 2.691e-07 0.078963 4.431536 4.678e-06
b_trf_c1 -0.013241 0.005173 -2.5599 0.005235 0.006316 -2.096606 0.018014
b_wat_c1 2.5326e-04 0.002349 0.1078 0.457065 0.002556 0.099083 0.460536
b_ser_P_c1 -0.505495 NaN NaN NaN 201.318644 -0.002511 0.498998
b_ser_A_c1 0.077520 NaN NaN NaN 201.327375 3.8505e-04 0.499846
b_ser_G_c1 0.428057 NaN NaN NaN 201.324333 0.002126 0.499152
b_envi_P_c1 -0.463142 NaN NaN NaN 349.031535 -0.001327 0.499471
b_envi_A_c1 0.098144 NaN NaN NaN 349.048410 2.8118e-04 0.499888
b_envi_G_c1 0.364986 NaN NaN NaN 349.047981 0.001046 0.499583
lambda_A_c1 1.000000 NA NA NA NA NA NA
lambda_B_c1 0.993324 0.233039 4.2625 1.011e-05 0.405029 2.452478 0.007094
delta_c1 0.480116 0.132377 3.6269 1.4343e-04 0.213453 2.249282 0.012247
gamma_male_c1 0.013893 0.129081 0.1076 0.457144 0.131087 0.105984 0.457797
asc_DF_c2 0.871215 NaN NaN NaN 0.066689 13.063835 0.000000
asc_HF_c2 3.266650 0.247016 13.2244 0.000000 0.495385 6.594160 2.138e-11
b_cost_c2 -0.002672 0.003892 -0.6865 0.246198 0.004126 -0.647652 0.258605
b_rat_c2 0.685840 0.044301 15.4814 0.000000 0.037486 18.295842 0.000000
b_trf_c2 -0.039416 0.012486 -3.1567 7.9772e-04 0.024307 -1.621609 0.052444
b_wat_c2 -0.024293 0.001721 -14.1172 0.000000 0.003929 -6.183880 3.127e-10
b_ser_P_c2 0.181757 NaN NaN NaN 0.192844 0.942511 0.172966
b_ser_A_c2 -0.592098 0.060240 -9.8289 0.000000 0.042651 -13.882493 0.000000
b_ser_G_c2 0.410372 NaN NaN NaN 0.341213 1.202684 0.114549
b_envi_P_c2 0.658738 0.287279 2.2930 0.010923 0.454321 1.449941 0.073537
b_envi_A_c2 -0.098275 NaN NaN NaN 0.159265 -0.617054 0.268600
b_envi_G_c2 -0.560458 0.062303 -8.9957 0.000000 0.032823 -17.075240 0.000000
lambda_A_c2 1.000000 NA NA NA NA NA NA
lambda_B_c2 0.011233 0.074413 0.1510 0.440006 0.060352 0.186124 0.426174
delta_c2 -2.103835 0.217278 -9.6827 0.000000 0.264715 -7.947543 9.992e-16
gamma_male_c2 0.609546 0.248769 2.4502 0.007138 0.260063 2.343834 0.009543
asc_DF_c3 1.022109 0.124333 8.2207 1.110e-16 0.137877 7.413170 6.162e-14
asc_HF_c3 0.291593 0.212175 1.3743 0.084673 0.208485 1.398633 0.080961
b_cost_c3 -0.012277 0.001364 -9.0035 0.000000 0.001417 -8.666229 0.000000
b_rat_c3 -0.179689 0.102541 -1.7524 0.039856 0.125441 -1.432456 0.076007
b_trf_c3 0.027069 0.005231 5.1745 1.143e-07 0.006688 4.047479 2.589e-05
b_wat_c3 0.003137 0.003445 0.9107 0.181240 0.003646 0.860412 0.194781
b_ser_P_c3 0.644819 NaN NaN NaN 2186.712165 2.9488e-04 0.499882
b_ser_A_c3 -0.292178 NaN NaN NaN 2186.703442 -1.3362e-04 0.499947
b_ser_G_c3 -0.352634 NaN NaN NaN 2186.701975 -1.6126e-04 0.499936
b_envi_P_c3 -0.269307 NaN NaN NaN 155.783219 -0.001729 0.499310
b_envi_A_c3 0.255086 NaN NaN NaN 155.790631 0.001637 0.499347
b_envi_G_c3 0.014237 NaN NaN NaN 155.792497 9.139e-05 0.499964
lambda_A_c3 1.000000 NA NA NA NA NA NA
lambda_B_c3 0.456305 0.111485 4.0930 2.130e-05 0.150940 3.023090 0.001251
delta_c3 0.000000 NA NA NA NA NA NA
gamma_male_c3 0.000000 NA NA NA NA NA NA
Nesting structure for NL model component 1:
Nest: root (1)
|-Nest: A_c1 (1)
| '-Alternative: HF
'-Nest: B_c1 (0.9933)
|-Alternative: DF
'-Alternative: FF
Nesting structure for NL model component 2:
Nest: root (1)
|-Nest: A_c2 (1)
| '-Alternative: HF
'-Nest: B_c2 (0.0112)
|-Alternative: DF
'-Alternative: FF
Nesting structure for NL model component 3:
Nest: root (1)
|-Nest: A_c3 (1)
| '-Alternative: HF
'-Nest: B_c3 (0.4563)
|-Alternative: DF
'-Alternative: FF
Summary of class allocation for LC model component :
Mean prob.
Class_1 0.72163
Class_2 0.09766
Class_3 0.18070
Overview of choices for NL model component 1:
FF DF HF
Times available 9456.00 9456.00 9456.0
Times chosen 1943.00 5111.00 2402.0
Percentage chosen overall 20.55 54.05 25.4
Percentage chosen when available 20.55 54.05 25.4
Overview of choices for NL model component 2:
FF DF HF
Times available 9456.00 9456.00 9456.0
Times chosen 1943.00 5111.00 2402.0
Percentage chosen overall 20.55 54.05 25.4
Percentage chosen when available 20.55 54.05 25.4
Overview of choices for NL model component 3:
FF DF HF
Times available 9456.00 9456.00 9456.0
Times chosen 1943.00 5111.00 2402.0
Percentage chosen overall 20.55 54.05 25.4
Percentage chosen when available 20.55 54.05 25.4