I'm estimating a hybrid model. The latent variable, FNS, has 9 statements and each of them is of 7-point Likert Scale. Following your sample, I wrote the code like:
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# ################################################################# #
#### 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 = "rice_Hybrid_with_OL",
modelDescr = "Hybrid choice model on rice choice data, using ordered measurement model for indicators",
indivID = "id",
nCores = 10
)
# ################################################################# #
#### 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("D:/2023/3、院长基金/0722 main data/ricedata_main.csv",header=TRUE)
# ################################################################# #
#### 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
choiceAnalysis_settings <- list(
alternatives = c(option_1=1, option_2=2, option_3=3, option_4=4),
avail = 1,
choiceVar = database$choice,
explanators = database[,c("edu","age","marry","income","gender")]
)
apollo_choiceAnalysis(choiceAnalysis_settings, apollo_control, database)
# ################################################################# #
#### DEFINE MODEL PARAMETERS ####
# ################################################################# #
### Vector of parameters, including any that are kept fixed in estimation
apollo_beta = c(asc_non_purchase = -1.52,
b_white = -0.05,
b_brown = -0.17,
b_organic = 0.57,
b_brand = 0.13,
b_lowGI = 0.99,
b_lowgluten = 0.98,
b_enhanced = 1.38,
b_price = -0.06,
lambda = 1,
gamma_ricehabit = 0,
gamma_younger = 0,
gamma_bachelor = 0,
gamma_kid = 0,
gamma_highincome = 0,
gamma_gender = 0,
zeta_FNS1 = 0,
zeta_FNS2 = 0,
zeta_FNS3 = 0,
zeta_FNS4 = 0,
zeta_FNS5 = 0,
zeta_FNS6 = 0,
zeta_FNS7 = 0,
zeta_FNS8 = 0,
zeta_FNS9 = 0,
tau_FNS1_1 = -3,
tau_FNS1_2 = -2,
tau_FNS1_3 = -1,
tau_FNS1_4 = 1,
tau_FNS1_5 = 2,
tau_FNS1_6 = 3,
tau_FNS2_1 = -3,
tau_FNS2_2 = -2,
tau_FNS2_3 = -1,
tau_FNS2_4 = 1,
tau_FNS2_5 = 2,
tau_FNS2_6 = 3,
tau_FNS3_1 = -3,
tau_FNS3_2 = -2,
tau_FNS3_3 = -1,
tau_FNS3_4 = 1,
tau_FNS3_5 = 2,
tau_FNS3_6 = 3,
tau_FNS4_1 = -3,
tau_FNS4_2 = -2,
tau_FNS4_3 = -1,
tau_FNS4_4 = 1,
tau_FNS4_5 = 2,
tau_FNS4_6 = 3,
tau_FNS5_1 = -3,
tau_FNS5_2 = -2,
tau_FNS5_3 = -1,
tau_FNS5_4 = 1,
tau_FNS5_5 = 2,
tau_FNS5_6 = 3,
tau_FNS6_1 = -3,
tau_FNS6_2 = -2,
tau_FNS6_3 = -1,
tau_FNS6_4 = 1,
tau_FNS6_5 = 2,
tau_FNS6_6 = 3,
tau_FNS7_1 = -3,
tau_FNS7_2 = -2,
tau_FNS7_3 = -1,
tau_FNS7_4 = 1,
tau_FNS7_5 = 2,
tau_FNS7_6 = 3,
tau_FNS8_1 = -3,
tau_FNS8_2 = -2,
tau_FNS8_3 = -1,
tau_FNS8_4 = 1,
tau_FNS8_5 = 2,
tau_FNS8_6 = 3,
tau_FNS9_1 = -3,
tau_FNS9_2 = -2,
tau_FNS9_3 = -1,
tau_FNS9_4 = 1,
tau_FNS9_5 = 2,
tau_FNS9_6 = 3
)
### 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()
# ################################################################# #
#### DEFINE RANDOM COMPONENTS ####
# ################################################################# #
### Set parameters for generating draws
apollo_draws = list(
interDrawsType="halton",
interNDraws=100,
interUnifDraws=c(),
interNormDraws=c("eta"),
intraDrawsType="",
intraNDraws=0,
intraUnifDraws=c(),
intraNormDraws=c()
)
### Create random parameters
apollo_randCoeff=function(apollo_beta, apollo_inputs){
randcoeff = list()
randcoeff[["LV"]] = gamma_ricehabit * (ricehabit>3) + gamma_younger * (age<2) + gamma_bachelor * edu + gamma_kid * (marry==2|marry==4) + gamma_highincome * (income>1) + gamma_gender * gender + eta
return(randcoeff)
}
# ################################################################# #
#### 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()
### Likelihood of indicators
ol_settings1 = list(outcomeOrdered = FNS1,
V = zeta_FNS1*LV,
tau = list(tau_FNS1_1, tau_FNS1_2, tau_FNS1_3, tau_FNS1_4, tau_FNS1_5, tau_FNS1_6),
rows = (task==1|task==2),
componentName = "indic_FNS1")
ol_settings2 = list(outcomeOrdered = FNS1,
V = zeta_FNS2*LV,
tau = list(tau_FNS2_1, tau_FNS2_2, tau_FNS2_3, tau_FNS2_4, tau_FNS2_5, tau_FNS2_6),
rows = (task==1|task==2),
componentName = "indic_FNS2")
ol_settings3 = list(outcomeOrdered = FNS3,
V = zeta_FNS3*LV,
tau = list(tau_FNS3_1, tau_FNS3_2, tau_FNS3_3, tau_FNS3_4, tau_FNS3_5, tau_FNS3_6),
rows = (task==1|task==2),
componentName = "indic_FNS3")
ol_settings4 = list(outcomeOrdered = FNS4,
V = zeta_FNS4*LV,
tau = list(tau_FNS4_1, tau_FNS4_2, tau_FNS4_3, tau_FNS4_4, tau_FNS4_5, tau_FNS4_6),
rows = (task==1|task==2),
componentName = "indic_FNS4")
ol_settings5 = list(outcomeOrdered = FNS5,
V = zeta_FNS5*LV,
tau = list(tau_FNS5_1, tau_FNS5_2, tau_FNS5_3, tau_FNS5_4, tau_FNS5_5, tau_FNS5_6),
rows = (task==1|task==2),
componentName = "indic_FNS5")
ol_settings6 = list(outcomeOrdered = FNS6,
V = zeta_FNS6*LV,
tau = list(tau_FNS6_1, tau_FNS6_2, tau_FNS6_3, tau_FNS6_4, tau_FNS6_5, tau_FNS6_6),
rows = (task==1|task==2),
componentName = "indic_FNS6")
ol_settings7 = list(outcomeOrdered = FNS7,
V = zeta_FNS7*LV,
tau = list(tau_FNS7_1, tau_FNS7_2, tau_FNS7_3, tau_FNS7_4, tau_FNS7_5, tau_FNS7_6),
rows = (task==1|task==2),
componentName = "indic_FNS7")
ol_settings8 = list(outcomeOrdered = FNS8,
V = zeta_FNS8*LV,
tau = list(tau_FNS8_1, tau_FNS8_2, tau_FNS8_3, tau_FNS8_4, tau_FNS8_5, tau_FNS8_6),
rows = (task==1|task==2),
componentName = "indic_FNS8")
ol_settings9 = list(outcomeOrdered = FNS9,
V = zeta_FNS9*LV,
tau = list(tau_FNS9_1, tau_FNS9_2, tau_FNS9_3, tau_FNS9_4, tau_FNS9_5, tau_FNS9_6),
rows = (task==1|task==2),
componentName = "indic_FNS9")
P[["indic_FNS1"]] = apollo_ol(ol_settings1, functionality)
P[["indic_FNS2"]] = apollo_ol(ol_settings2, functionality)
P[["indic_FNS3"]] = apollo_ol(ol_settings3, functionality)
P[["indic_FNS4"]] = apollo_ol(ol_settings4, functionality)
P[["indic_FNS5"]] = apollo_ol(ol_settings5, functionality)
P[["indic_FNS6"]] = apollo_ol(ol_settings6, functionality)
P[["indic_FNS7"]] = apollo_ol(ol_settings7, functionality)
P[["indic_FNS8"]] = apollo_ol(ol_settings8, functionality)
P[["indic_FNS9"]] = apollo_ol(ol_settings9, functionality)
### Likelihood of choices
### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
V = list()
V[['option_1']] = b_white * (white_1==0) + b_brown * (white_1==2) + b_organic * organic_1 + b_brand * brand_1 + b_lowGI * (health_1==1) + b_lowgluten * (health_1==2) + b_enhanced * (health_1==3) + b_price * price_1 + lambda * LV
V[['option_2']] = b_white * (white_2==0) + b_brown * (white_2==2) + b_organic * organic_2 + b_brand * brand_2 + b_lowGI * (health_2==1) + b_lowgluten * (health_2==2) + b_enhanced * (health_2==3) + b_price * price_2 + lambda * LV
V[['option_3']] = b_white * (white_3==0) + b_brown * (white_3==2) + b_organic * organic_3 + b_brand * brand_3 + b_lowGI * (health_3==1) + b_lowgluten * (health_3==2) + b_enhanced * (health_3==3) + b_price * price_3 + lambda * LV
V[['option_4']] = asc_non_purchase
### Define settings for MNL model component
mnl_settings = list(
alternatives = c('option_1'=1, 'option_2'=2, 'option_3'=3, 'option_4'=4),
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)Model name : rice_Hybrid_with_OL
Model description : Hybrid choice model on rice choice data, using ordered measurement model for indicators
Model run at : 2023-09-12 09:36:03.126313
Estimation method : bgw
Model diagnosis : Function evaluation limit
Number of individuals : 1666
Number of rows in database : 19992
Number of modelled outcomes : 34986
indic_FNS1 : 1666
indic_FNS2 : 1666
indic_FNS3 : 1666
indic_FNS4 : 1666
indic_FNS5 : 1666
indic_FNS6 : 1666
indic_FNS7 : 1666
indic_FNS8 : 1666
indic_FNS9 : 1666
choice : 19992
Number of cores used : 10
Number of inter-individual draws : 100 (halton)
LL(start) : -52758.58
LL (whole model) at equal shares, LL(0) : -56891.77
LL (whole model) at observed shares, LL(C) : -49306.69
LL(final, whole model) : -43988.96
Rho-squared vs equal shares : 0.2268
Adj.Rho-squared vs equal shares : 0.2254
Rho-squared vs observed shares : 0.1078
Adj.Rho-squared vs observed shares : 0.1074
AIC : 88135.93
BIC : 88563.96
LL(0,indic_FNS1) : -3241.89
LL(final,indic_FNS1) : -2539.81
LL(0,indic_FNS2) : -3241.89
LL(final,indic_FNS2) : -2539.78
LL(0,indic_FNS3) : -3241.89
LL(final,indic_FNS3) : -2980.73
LL(0,indic_FNS4) : -3241.89
LL(final,indic_FNS4) : -2695.38
LL(0,indic_FNS5) : -3241.89
LL(final,indic_FNS5) : -2849.14
LL(0,indic_FNS6) : -3241.89
LL(final,indic_FNS6) : -2454.93
LL(0,indic_FNS7) : -3241.89
LL(final,indic_FNS7) : -2867.42
LL(0,indic_FNS8) : -3241.89
LL(final,indic_FNS8) : -2977.32
LL(0,indic_FNS9) : -3241.89
LL(final,indic_FNS9) : -3009.63
LL(0,choice) : -27714.8
LL(final,choice) : -22300.22
Estimated parameters : 79
Time taken (hh:mm:ss) : 05:02:0.79
pre-estimation : 00:03:10.06
estimation : 04:58:48.38
post-estimation : 00:00:2.35
Iterations : 175 (Function evaluation limit)
Unconstrained optimisation.
Estimates:
Estimate s.e. t.rat.(0) Rob.s.e. Rob.t.rat.(0)
asc_non_purchase -1.29513 NA NA NA NA
b_white -0.05163 NA NA NA NA
b_brown -0.16526 NA NA NA NA
b_organic 0.57331 NA NA NA NA
b_brand 0.13023 NA NA NA NA
b_lowGI 0.99513 NA NA NA NA
b_lowgluten 0.98145 NA NA NA NA
b_enhanced 1.38365 NA NA NA NA
b_price -0.06220 NA NA NA NA
lambda 0.28663 NA NA NA NA
gamma_ricehabit 0.06744 NA NA NA NA
gamma_younger 0.40673 NA NA NA NA
gamma_bachelor 0.09274 NA NA NA NA
gamma_kid 0.57793 NA NA NA NA
gamma_highincome 0.30278 NA NA NA NA
gamma_gender -0.28951 NA NA NA NA
zeta_FNS1 -133.70525 NA NA NA NA
zeta_FNS2 -133.12048 NA NA NA NA
zeta_FNS3 -0.94227 NA NA NA NA
zeta_FNS4 -1.17331 NA NA NA NA
zeta_FNS5 -0.90060 NA NA NA NA
zeta_FNS6 -1.00139 NA NA NA NA
zeta_FNS7 -0.92775 NA NA NA NA
zeta_FNS8 -0.40808 NA NA NA NA
zeta_FNS9 -0.67639 NA NA NA NA
tau_FNS1_1 -297.26447 NA NA NA NA
tau_FNS1_2 -163.25145 NA NA NA NA
tau_FNS1_3 -23.15217 NA NA NA NA
tau_FNS1_4 59.93685 NA NA NA NA
tau_FNS1_5 121.48347 NA NA NA NA
tau_FNS1_6 180.66530 NA NA NA NA
tau_FNS2_1 -295.78742 NA NA NA NA
tau_FNS2_2 -162.63290 NA NA NA NA
tau_FNS2_3 -23.11581 NA NA NA NA
tau_FNS2_4 59.62596 NA NA NA NA
tau_FNS2_5 120.91888 NA NA NA NA
tau_FNS2_6 180.16704 NA NA NA NA
tau_FNS3_1 -4.31739 NA NA NA NA
tau_FNS3_2 -2.34942 NA NA NA NA
tau_FNS3_3 -1.21491 NA NA NA NA
tau_FNS3_4 -0.24568 NA NA NA NA
tau_FNS3_5 1.08742 NA NA NA NA
tau_FNS3_6 2.59517 NA NA NA NA
tau_FNS4_1 -3.56004 NA NA NA NA
tau_FNS4_2 -1.41770 NA NA NA NA
tau_FNS4_3 0.07390 NA NA NA NA
tau_FNS4_4 1.38879 NA NA NA NA
tau_FNS4_5 2.49014 NA NA NA NA
tau_FNS4_6 3.95172 NA NA NA NA
tau_FNS5_1 -3.19945 NA NA NA NA
tau_FNS5_2 -1.43405 NA NA NA NA
tau_FNS5_3 -0.11151 NA NA NA NA
tau_FNS5_4 0.97820 NA NA NA NA
tau_FNS5_5 1.99035 NA NA NA NA
tau_FNS5_6 3.52290 NA NA NA NA
tau_FNS6_1 -3.22342 NA NA NA NA
tau_FNS6_2 -1.05937 NA NA NA NA
tau_FNS6_3 0.78942 NA NA NA NA
tau_FNS6_4 2.22204 NA NA NA NA
tau_FNS6_5 3.19731 NA NA NA NA
tau_FNS6_6 4.89953 NA NA NA NA
tau_FNS7_1 -3.01151 NA NA NA NA
tau_FNS7_2 -1.30008 NA NA NA NA
tau_FNS7_3 -0.14376 NA NA NA NA
tau_FNS7_4 0.97139 NA NA NA NA
tau_FNS7_5 1.93643 NA NA NA NA
tau_FNS7_6 3.52921 NA NA NA NA
tau_FNS8_1 -2.73999 NA NA NA NA
tau_FNS8_2 -1.21944 NA NA NA NA
tau_FNS8_3 -0.20553 NA NA NA NA
tau_FNS8_4 0.78810 NA NA NA NA
tau_FNS8_5 1.85675 NA NA NA NA
tau_FNS8_6 3.15427 NA NA NA NA
tau_FNS9_1 -3.27427 NA NA NA NA
tau_FNS9_2 -1.49047 NA NA NA NA
tau_FNS9_3 -0.30296 NA NA NA NA
tau_FNS9_4 0.39214 NA NA NA NA
tau_FNS9_5 1.27725 NA NA NA NA
tau_FNS9_6 2.67444 NA NA NA NA
Could you please suggest me what to do?
Best,
Xian
