Errors when estimating a latent variable model
Posted: 19 May 2023, 10:24
Dear Stephane and David,
Thank you very much for inventing the Apollo package and creating the forum allowing our users to communicate.
Recently, I was trying to run an mnl model containing latent variables, but the code encountered the following error.
Overview of choices for model component indic_Benefit3:
1 2 3 4 5
Times chosen 24.00 176.00 608.00 1456.00 1448.00
Percentage chosen overall 0.65 4.74 16.38 39.22 39.01
Overview of choices for MNL model component choice:
V_bike1 V_bike2 V_bike3 V_bus V_taxi V_sharecar
Times available 3712.00 3712.00 3712.00 3712.00 3712.00 3712.00
Times chosen 587.00 814.00 117.00 545.00 187.00 235.00
Percentage chosen overall 15.81 21.93 3.15 14.68 5.04 6.33
Percentage chosen when available 15.81 21.93 3.15 14.68 5.04 6.33
V_car V_walk
Times available 3712.00 3712.00
Times chosen 655.00 572.00
Percentage chosen overall 17.65 15.41
Percentage chosen when available 17.65 15.41
Error in apollo_avgInterDraws(P, apollo_inputs, functionality) :
No Inter-individuals draws to average over!
My question is
Why is this situation happening?
I have attached my R code below.
Thank you very much.
Ren.
Thank you very much for inventing the Apollo package and creating the forum allowing our users to communicate.
Recently, I was trying to run an mnl model containing latent variables, but the code encountered the following error.
Overview of choices for model component indic_Benefit3:
1 2 3 4 5
Times chosen 24.00 176.00 608.00 1456.00 1448.00
Percentage chosen overall 0.65 4.74 16.38 39.22 39.01
Overview of choices for MNL model component choice:
V_bike1 V_bike2 V_bike3 V_bus V_taxi V_sharecar
Times available 3712.00 3712.00 3712.00 3712.00 3712.00 3712.00
Times chosen 587.00 814.00 117.00 545.00 187.00 235.00
Percentage chosen overall 15.81 21.93 3.15 14.68 5.04 6.33
Percentage chosen when available 15.81 21.93 3.15 14.68 5.04 6.33
V_car V_walk
Times available 3712.00 3712.00
Times chosen 655.00 572.00
Percentage chosen overall 17.65 15.41
Percentage chosen when available 17.65 15.41
Error in apollo_avgInterDraws(P, apollo_inputs, functionality) :
No Inter-individuals draws to average over!
My question is
Why is this situation happening?
I have attached my R code below.
Thank you very much.
Ren.
Code: Select all
# ################################################################# #
#### LOAD LIBRARY AND DEFINE CORE SETTINGS ####
# ################################################################# #
### Clear memory
rm(list = ls())
### Load Apollo library
library(apollo)
### Initialise code
apollo_initialise()
### Set core controls
apollo_control = list(
modelName = "Hybrid_with_OL",
modelDescr = "Hybrid choice model on drug choice data, using ordered measurement model for indicators",
indivID = "id",
mixing = TRUE,
nCores = 4,
outputDirectory = "output"
)
# ################################################################# #
#### LOAD DATA AND APPLY ANY TRANSFORMATIONS ####
# ################################################################# #
### Loading data from package
### if data is to be loaded from a file (e.g. called data.csv),
### the code would be: database = read.csv("data.csv",header=TRUE)
database = read.csv("huizong.csv",header=TRUE)
#database = apollo_drugChoiceData
### for data dictionary, use ?apollo_drugChoiceData
# ################################################################# #
#### ANALYSIS OF CHOICES ####
# ################################################################# #
### Illustration of how to use apollo_choiceAnalysis with user-defined alternatives.
### This is useful in cases where the alternatives in the data differ
### across tasks. The same approach can then also be used with unlabelled data
# ################################################################# #
#### DEFINE MODEL PARAMETERS ####
# ################################################################# #
### Vector of parameters, including any that are kept fixed in estimation
apollo_beta = c(
lambda = 1,
lambda_at1=0,
lambda_at2=0,
lambda_at3=0,
gamma_occupation =0,
gamma_age_35_less =0,
gamma_male = 0,
gamma_highEducation =0,
gamma_high_income = 0,
gamma_more_car = 0,
gamma_no_car = 0,
gamma_more_bike =0,
gamma_no_bike = 0,
gamma_occupation1 =0,
gamma_age_35_less1 =0,
gamma_male1 = 0,
gamma_highEducation1 =0,
gamma_high_income1 = 0,
gamma_more_car1 = 0,
gamma_no_car1 = 0,
gamma_more_bike1 =0,
gamma_no_bike1 = 0,
gamma_occupation2 =0,
gamma_age_35_less2 =0,
gamma_male2 = 0,
gamma_highEducation2 =0,
gamma_high_income2 = 0,
gamma_more_car2 = 0,
gamma_no_car2 = 0,
gamma_more_bike2 =0,
gamma_no_bike2 = 0,
zeta_Availability1 = 1,
zeta_Tangibles1 = 1,
zeta_Benefit1 = 1,
zeta_Availability2 = 1,
zeta_Tangibles2 = 1,
zeta_Benefit2 = 1,
zeta_Availability3 = 1,
zeta_Tangibles3 = 1,
zeta_Benefit3 = 1,
zeta_Availability4 = 1,
zeta_Tangibles4 = 1,
tau_Availability_1 =-2,
tau_Availability_2 =-1,
tau_Availability_3 = 1,
tau_Availability_4 = 2,
tau_Tangibles_1 =-2,
tau_Tangibles_2 =-1,
tau_Tangibles_3 = 1,
tau_Tangibles_4 = 2,
tau_Benefit_1 =-2,
tau_Benefit_2 =-1,
tau_Benefit_3 = 1,
tau_Benefit_4 = 2,
ASC_bike1 = 0,
ASC_bike2 = 0,
ASC_bike3 = 0,
ASC_bus = 0,
ASC_taxi = 1,
ASC_sharecar = 0,
ASC_car = 0,
ASC_walk =0 ,
Beta_travel_time= 0,
Beta_travel_time1= 0,
Beta_travel_time2= 0,
Beta_travel_time3= 0,
Beta_travel_time4= 0,
Beta_travel_time5= 0,
Beta_travel_time6= 0,
Beta_travel_time7= 0,
Beta_travel_purpose= 0,
Beta_travel_purpose1= 0,
Beta_travel_purpose2= 0,
Beta_travel_purpose3=0,
Beta_travel_purpose4=0,
Beta_travel_purpose5= 0,
Beta_travel_purpose6= 0,
Beta_travel_purpose7= 0,
Beta_travel_purpose_income= 0,
Beta_travel_purpose_income1= 0,
Beta_travel_purpose_income2= 0,
Beta_travel_purpose_income3=0,
Beta_travel_purpose_income4=0,
Beta_travel_purpose_income5= 0,
Beta_travel_purpose_income6= 0,
Beta_travel_purpose_income7= 0,
Beta_travel_purpose_education= 0,
Beta_travel_purpose_education1= 0,
Beta_travel_purpose_education2= 0,
Beta_travel_purpose_education3=0,
Beta_travel_purpose_education4=0,
Beta_travel_purpose_education5= 0,
Beta_travel_purpose_education6= 0,
Beta_travel_purpose_education7= 0,
Beta_weather= 0,
Beta_weather1=0,
Beta_weather2= 0,
Beta_weather3= 0,
Beta_weather4=0,
Beta_weather5= 0,
Beta_weather6= 0,
Beta_weather7= 0,
Beta_weather_male= 0,
Beta_weather_male1=0,
Beta_weather_male2= 0,
Beta_weather_male3= 0,
Beta_weather_male4=0,
Beta_weather_male5= 0,
Beta_weather_male6= 0,
Beta_weather_male7= 0,
Beta_bike_waittime=0,
Beta_bike_intime= 0,
Beta_bike1_travel_time=0,
Beta_bike1_cost= 0,
Beta_bike2_walkt_time=0,
Beta_bike2_intime= 0,
Beta_bike2_travel_time= 0,
Beta_bike2_cost= 0,
Beta_bike3_walktime= 0,
Beta_bike3_intime= 0,
Beta_bike3_travel_time=0,
Beta_bike3_cost=0,
Beta_bus_waittime= 0,
Beta_bus_intime= 0,
Beta_bus_traveltime= 0,
Beta_bus_cost= 0,
Beta_Taxi_waittime= 0,
Beta_Taxi_intime= 0,
Beta_Taxi_traveltime=0,
Beta_Taxi_cost= 0,
Beta_share_car_waittime= 0,
Beta_share_car_intime= 0,
Beta_share_car_travel_time=0,
Beta_share_car_cost=0,
Beta_carpostcar=0,
Beta_car_traveltime= 0,
Beta_walk_time=0
)
### 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("ASC_walk")
# ################################################################# #
#### DEFINE RANDOM COMPONENTS ####
# ################################################################# #
### Set parameters for generating draws
apollo_draws = list(
##
interDrawsType="mlhs",
interNDraws=100,
interUnifDraws=c(),
interNormDraws=c("eta","eta1","eta2") ,
##
intraDrawsType="",
intraNDraws=0,
intraUnifDraws=c(),
intraNormDraws=c()
)
### Create random parameters 结构方程
apollo_randCoeff=function(apollo_beta, apollo_inputs){
randcoeff = list()
randcoeff[["LV"]] = gamma_high_income * ( income == 0 )
+ gamma_highEducation * (edu == 0)
+ gamma_occupation * (occupation == 3)
+ gamma_age_35_less * (age == 1)
+ gamma_male * (gender == 1)
+ gamma_more_car* (own_car>=2)
+ gamma_no_car* (own_car==0)
+ gamma_more_bike* (own_bike >= 2)
+ gamma_no_bike* (own_bike == 1)
+ eta
randcoeff[["LV1"]] = gamma_high_income1 * ( income == 0 )
+ gamma_highEducation1 * (edu == 0)
+ gamma_occupation1 * (occupation == 3)
+ gamma_age_35_less1 * (age == 1)
+ gamma_male1 * (gender == 1)
+ gamma_more_car1* (own_car>=2)
+ gamma_no_car1* (own_car==0)
+ gamma_more_bike1* (own_bike >= 2)
+ gamma_no_bike1* (own_bike == 1)
+ eta1
randcoeff[["LV2"]] = gamma_high_income2 * ( income == 0 )
+ gamma_highEducation2 * (edu == 0)
+ gamma_occupation2 * (occupation == 3)
+ gamma_age_35_less2 * (age == 1)
+ gamma_male2 * (gender == 1)
+ gamma_more_car2* (own_car>=2)
+ gamma_no_car2* (own_car==0)
+ gamma_more_bike2* (own_bike >= 2)
+ gamma_no_bike2* (own_bike == 1)
+ eta2
return(randcoeff)
}
# ################################################################# #
#### GROUP AND VALIDATE INPUTS ####
# ################################################################# #
apollo_inputs = apollo_validateInputs()
# ################################################################# #
#### DEFINE MODEL AND LIKELIHOOD FUNCTION 测量方程 ####
# ################################################################# #
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()
### Likelihood of indicators
##
ol_settings1 = list(outcomeOrdered = A3,
V = zeta_Availability1*LV,
tau = list(tau_Availability_1, tau_Availability_2, tau_Availability_3, tau_Availability_4),
componentName = "indic_Availability1")
ol_settings2 = list(outcomeOrdered = A2,
V = zeta_Availability2*LV,
tau = list(tau_Availability_1, tau_Availability_2, tau_Availability_3, tau_Availability_4),
componentName = "indic_Availability2")
ol_settings3 = list(outcomeOrdered = A21,
V = zeta_Availability3*LV,
tau = list(tau_Availability_1, tau_Availability_2, tau_Availability_3, tau_Availability_4),
componentName = "indic_Availability3")
ol_settings4 = list(outcomeOrdered = A16,
V = zeta_Availability4*LV,
tau = list(tau_Availability_1, tau_Availability_2, tau_Availability_3, tau_Availability_4),
componentName = "indic_Availability4")
ol_settings5 = list(outcomeOrdered = A10,
V = zeta_Tangibles1*LV1,
tau = list(tau_Tangibles_1, tau_Tangibles_2, tau_Tangibles_3, tau_Tangibles_4),
componentName = "indic_Tangibles1")
ol_settings6 = list(outcomeOrdered = A11,
V = zeta_Tangibles2*LV1,
tau = list(tau_Tangibles_1, tau_Tangibles_2, tau_Tangibles_3, tau_Tangibles_4),
componentName = "indic_Tangibles2")
ol_settings7 = list(outcomeOrdered = A4,
V = zeta_Tangibles3*LV1,
tau = list(tau_Tangibles_1, tau_Tangibles_2, tau_Tangibles_3, tau_Tangibles_4),
componentName = "indic_Tangibles3")
ol_settings8 = list(outcomeOrdered = A17,
V = zeta_Tangibles4*LV1,
tau = list(tau_Tangibles_1, tau_Tangibles_2, tau_Tangibles_3, tau_Tangibles_4),
componentName = "indic_Tangibles4")
ol_settings9 = list(outcomeOrdered = A22,
V = zeta_Benefit1*LV2,
tau = list(tau_Benefit_1, tau_Benefit_2, tau_Benefit_3, tau_Benefit_4),
componentName = "indic_Benefit1")
ol_settings10 = list(outcomeOrdered = A24,
V = zeta_Benefit2*LV2,
tau = list(tau_Benefit_1, tau_Benefit_2, tau_Benefit_3, tau_Benefit_4),
componentName = "indic_Benefit2")
ol_settings11 = list(outcomeOrdered = A18,
V = zeta_Benefit3*LV2,
tau = list(tau_Benefit_1, tau_Benefit_2, tau_Benefit_3, tau_Benefit_4),
componentName = "indic_Benefit3")
P[["indic_Availability1"]]= apollo_ol(ol_settings1, functionality)
P[["indic_Availability2"]]= apollo_ol(ol_settings2, functionality)
P[["indic_Availability3"]]= apollo_ol(ol_settings3, functionality)
P[["indic_Availability4"]] = apollo_ol(ol_settings4, functionality)
P[["indic_Tangibles1"]] = apollo_ol(ol_settings5, functionality)
P[["indic_Tangibles2"]] = apollo_ol(ol_settings6, functionality)
P[["indic_Tangibles3"]] = apollo_ol(ol_settings7, functionality)
P[["indic_Tangibles4"]] = apollo_ol(ol_settings8, functionality)
P[["indic_Benefit1"]] = apollo_ol(ol_settings9, functionality)
P[["indic_Benefit2"]] = apollo_ol(ol_settings10, functionality)
P[["indic_Benefit3"]] = apollo_ol(ol_settings11, functionality)
### Likelihood of choices
### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
V = list()
V[["V_bike1"]]= (ASC_bike1 + Beta_travel_purpose * (travel_purpose == 1)
+ Beta_weather * (weather == 1)
+ (travel_time == 1) * Beta_travel_time
+ Beta_bike_waittime * bike_waittime
+ Beta_bike_intime * bike_intime
+ Beta_bike1_travel_time * bike1_travel_time
+ Beta_bike1_cost * bike1_cost
+ Beta_travel_purpose_income * (travel_purpose == 1) * (income == 1)
+ Beta_weather_male * (weather == 1) * (gender == 1)
+ lambda_at1*LV+lambda_at2*LV1 + lambda_at3 * LV2
)
V[["V_bike2"]] = (ASC_bike2 + Beta_travel_purpose1 * (travel_purpose == 1)
+ Beta_weather1 * (weather == 1)
+ (travel_time == 1) * Beta_travel_time1
+ Beta_bike2_walkt_time * bike2_walkt_time
+ Beta_bike2_intime * bike2_intime
+ Beta_bike2_travel_time * bike2_travel_time
+ Beta_bike2_cost * bike2_cost
+ Beta_travel_purpose_income1 * (travel_purpose == 1) * (income == 1)
+ Beta_weather_male1 * (weather == 1) * (gender == 1)
+ lambda*LV)
V[["V_bike3"]] = (ASC_bike3 + Beta_travel_purpose2 * (travel_purpose == 1)
+ (travel_time == 1) * Beta_travel_time2
+ Beta_weather2 * (weather == 1)
+ Beta_bike3_walktime * bike3_walktime
+ Beta_bike3_intime * bike3_intime
+ Beta_bike3_travel_time * bike3_travel_time
+ Beta_bike3_cost * bike3_cost
+ Beta_travel_purpose_income2 * (travel_purpose == 1) * (income == 1)
+ Beta_weather_male2 * (weather == 1) * (gender == 1)
+ lambda*LV)
V[["V_bus"]]= (ASC_bus + Beta_travel_purpose3 * (travel_purpose==1) + Beta_weather3 * (weather==1)
+ (travel_time==1) * Beta_travel_time3
+ Beta_bus_waittime * bus_waittime
+ Beta_bus_intime * bus_intime
+ Beta_bus_traveltime * bus_traveltime
+ Beta_bus_cost * bus_cost
+ Beta_travel_purpose_income3 * (travel_purpose == 1) * (income==1)
+ Beta_weather_male3 * (weather == 1) * (gender == 1) )
V[["V_taxi"]]=(ASC_taxi + Beta_travel_purpose4 * (travel_purpose==1) + Beta_weather4 * (weather==1)
+ (travel_time==1) * Beta_travel_time4
+ Beta_Taxi_waittime * Taxi_waittime
+ Beta_Taxi_intime * Taxi_intime
+ Beta_Taxi_traveltime * Taxi_traveltime
+ Beta_Taxi_cost * Taxi_cost
+ Beta_travel_purpose_income4 * (travel_purpose == 1) * (income==1)
+ Beta_weather_male4 * (weather == 1) *(gender == 1)
)
V[["V_sharecar"]] =(ASC_sharecar + Beta_travel_purpose5 * (travel_purpose==1) + Beta_weather5 * (weather==1)
+ (travel_time==1) * Beta_travel_time5
+ Beta_share_car_waittime * share_car_waittime
+ Beta_share_car_intime * share_car_intime
+ Beta_share_car_travel_time * share_car_travel_time
+ Beta_share_car_cost * share_car_cost
+ Beta_travel_purpose_income5 * (travel_purpose == 1) * (income==1)
+ Beta_weather_male5 * (weather == 1) * (gender == 1)
)
V[["V_car"]] =(ASC_walk + Beta_travel_purpose7 * (travel_purpose==1)
+ (travel_time==1) * Beta_travel_time7
+ Beta_weather7 * (weather==1)
+ Beta_walk_time * walk_time
+ Beta_travel_purpose_income6 * (travel_purpose == 1) * (income==1)
+ Beta_weather_male7 * (weather == 1) * (gender == 1))
V[["V_walk"]] =(ASC_walk+ Beta_travel_purpose7* travel_purpose + Beta_weather7* weather +Beta_walk_time*walk_time
+Beta_travel_purpose_income6*(travel_purpose==1)*( income==1)
+Beta_weather_male7* (weather==1) * gender == 1)
### Define settings for MNL model component
mnl_settings = list(
alternatives = c(
V_bike1=1,
V_bike2=2,
V_bike3=3,
V_bus=4,
V_taxi=5,
V_sharecar=6,
V_car=7,
V_walk=8),
avail = 1,
choiceVar = choice,
utilities = V,
componentName = "choice"
)
### Compute probabilities for MNL model component
P[["choice"]] = apollo_mnl(mnl_settings, 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)
}
# ################################################################# #
#### MODEL ESTIMATION ####
# ################################################################# #
### Optional: calculate LL before model estimation
# apollo_llCalc(apollo_beta, apollo_probabilities, apollo_inputs)
### 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)
# ################################################################# #
##### POST-PROCESSING ####
# ################################################################# #
### Print outputs of additional diagnostics to new output file (remember to close file writing when complete)
apollo_sink()
# ----------------------------------------------------------------- #
#---- MODEL PREDICTIONS ----
# ----------------------------------------------------------------- #
# ----------------------------------------------------------------- #
#---- CONDITIONALS AND UNCONDITIONALS ----
# ----------------------------------------------------------------- #
conditionals <- apollo_conditionals(model,apollo_probabilities,apollo_inputs)
summary(conditionals)
unconditionals <- apollo_unconditionals(model,apollo_probabilities,apollo_inputs)
mean(unconditionals[[1]])
sd(unconditionals[[1]])
# ----------------------------------------------------------------- #
#---- switch off writing to file ----
# ----------------------------------------------------------------- #
apollo_sink()