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ICLV model questions

https://www.apollochoicemodelling.com/forum/viewtopic.php?t=23

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Re: ICLV model questions

Posted: 15 Mar 2021, 15:27
by cybey
I would like to ask another question about ICLV model specifications: Is it appropriate to use the same socio-demographic characteristics as explanatory variables for both the latent variable and as covariates for the utility coefficients?

For example, let us assume I would like to test whether gender has an effect on the LV "environmental awareness", which in turn has an effect on the travel mode "bus" (beta coefficient "b_bus"). On the other hand, gender might have an effect on the utility of "bus", which cannot be explained by the indirect effect of the latent variable. In this case, both coefficients could be significant, i. e. the effect of gender on the LV as well as the effect of gender on the beta coefficient "b_bus". But what happens if the effect of gender is (almost) perfectly moderated by the latent variable? Can this cause identification problems? A few papers I have looked into use different socio-demographic characteristics as covariates for the utility parameters and as explanatory variables for the LV.

Re: ICLV model questions

Posted: 16 Mar 2021, 10:13
by stephanehess
Hi Nico

yes, and in fact, this is in my view the correct approach as it allows you to disentangle the effects. See e.g. http://www.stephanehess.me.uk/papers/jo ... A_2018.pdf

If the LV is used in both the choice part and the measurement model for indicators, but the additional parameters for the same socios are used only in the choice part, then theoretical identification is fine, but of course empirical identification might be dataset specific

Stephane

Re: ICLV model questions

Posted: 16 Mar 2021, 10:38
by cybey
Hi Stephane,

thanks for the fast reply! I saw this approach for the first time in your paper, but nowhere else. In other papers, different socio-demographic characteristics are usually used as explanatory variables for the latent variables and as covariates.

But just to get this straight: Something like this is okay and does not lead to identification problems?

Image
  • b_Income_Price: direct influence of income on the price coefficient
  • gamma_Income_PriceSensitivity: explanatory variable income of the latent variable "PriceSensitivity"
  • lamba_PriceSensitivity_Price: influence of the LV on the price coefficient
  • Indicators of the LV
Nico

Re: ICLV model questions

Posted: 17 Mar 2021, 15:08
by stephanehess
Hi Nico

yes, this is fine. In fact, any papers that do not attempt to put the same covariates into the utility directly and into the LV are potentially misattributing the source of heterogeneity. This is the same reason why any hybrid choice model should always also incorporate random heterogeneity directly in the utility, not just through the LV

Stephane

Re: ICLV model questions

Posted: 06 Apr 2021, 06:24
by cybey
Perfect, thanks a lot!

Re: ICLV model questions

Posted: 16 Jul 2025, 04:13
by gabystephanie
Dear Stephane,
I have a question about my model estimation. I have already estimated the MNL and MXL models, and now I’m trying to estimate the HCM. Specifically, I would like to estimate:
  • the indirect effect of BP on utility,
  • the influence of an attribute on BP, and
  • how SC moderates the indirect effect of BP on utility.
Do you think this specification makes sense and is feasible to estimate?
I’ve attempted to estimate the model, but I keep running into errors. Would you be able to advise me on how to approach this, or point me towards what might be going wrong?

Thank you very much in advance for your help.

Code: Select all

apollo_control = list(
  modelName       = "mmnl241a",  # or any name you like
  modelDescr      = "mmnl for trust in green hotels",
  indivID         = "ID",                 # replace with your actual respondent ID column
  nCores          = 7,
  outputDirectory = "output")                    # or more, depending on your machine


database$BP_1=database$BP_1-mean(database$BP_1)
database$BP_2=database$BP_2-mean(database$BP_2)
database$BP_3=database$BP_3-mean(database$BP_3)
database$BP_4=database$BP_4-mean(database$BP_4)
database$BP_5=database$BP_5-mean(database$BP_5)
database$BP_6=database$BP_6-mean(database$BP_6)
database$BP_7=database$BP_7-mean(database$BP_7)
database$SC_1=database$SC_1-mean(database$SC_1)
database$SC_2=database$SC_2-mean(database$SC_2)
database$SC_3=database$SC_3-mean(database$SC_3)
database$SC_4=database$SC_4-mean(database$SC_4)
### Vector of parameters, including any that are kept fixed in estimation
apollo_beta = c(mu_log_b_HT2    = 0.1526, mu_log_b_HT3    = 0.1085,
                sigma_log_b_HT2 =  0.08571, sigma_log_b_HT3 =  0.08259, # Use abs(3.1) if you had a valid positive estimate
                mu_log_b_GM2    = 0.5232, mu_log_b_GM3 =  0.4581,
                sigma_log_b_GM2 =  0.08760, sigma_log_b_GM3 =  0.08471,
                mu_log_b_TC2    =  0.8261, mu_log_b_TC3    =  1.0635,
                sigma_log_b_TC2 =  0.07089, sigma_log_b_TC3 =  0.09575, 
                lambda_BP = 0, lambda_BP_SC = 0, 
                zeta_BP_1 = 0.54592, zeta_BP_2 = 0.46008, zeta_BP_3 = 0.31626, zeta_BP_4 = 0.51507,
                zeta_BP_5 = 0.54111, zeta_BP_6 = 0.38038, zeta_BP_7 = 0.47925,
                zeta_SC_1 = 0.35201, zeta_SC_2 = 0.31958, zeta_SC_3 = 0.83219, zeta_SC_4 = 0.74095,
                sigma_BP1 = 1, sigma_BP2 = 1, sigma_BP3 = 1, sigma_BP4 = 1,sigma_BP5 = 1, sigma_BP6 = 1,sigma_BP7 = 1,
                sigma_SC1 = 1, sigma_SC2 = 1, sigma_SC3 = 1, sigma_SC4 = 1, 
                gamma_SC_univ = 0, gamma_SC_reason = 0, gamma_SC_age = 0,  gamma_HT = 0, gamma_TC = 0, gamma_GM = 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()

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

### Set parameters for generating draws
apollo_draws = list(
  interDrawsType = "sobol", 
  interNDraws    = 2000,  
  interUnifDraws = c(),        
  interNormDraws = c("eta_BP", "eta_SC","draws_HT2", "draws_GM2", "draws_TC2","draws_HT3", "draws_GM3", "draws_TC3"),
  intraDrawsType = "sobol",
  intraNDraws    = 0,
  intraUnifDraws = c(),
  intraNormDraws = c()
)

### Create random parameters
apollo_randCoeff = function(apollo_beta, apollo_inputs){
  randcoeff = list()
  randcoeff[["b_HT2"]] = mu_log_b_HT2 + sigma_log_b_HT2 * draws_HT2 
  randcoeff[["b_GM2"]] = mu_log_b_GM2 + sigma_log_b_GM2 * draws_GM2 
  randcoeff[["b_TC2"]] = mu_log_b_TC2 + sigma_log_b_TC2 * draws_TC2 
  randcoeff[["b_HT3"]] = mu_log_b_HT3 + sigma_log_b_HT3 * draws_HT3 
  randcoeff[["b_GM3"]] = mu_log_b_GM3 + sigma_log_b_GM3 * draws_GM3 
  randcoeff[["b_TC3"]] = mu_log_b_TC3 + sigma_log_b_TC3 * draws_TC3 
  randcoeff[["BP"]] = gamma_HT*HT + gamma_TC*TC + gamma_GM*GM + eta_BP  
  randcoeff[["SC"]] = gamma_SC_univ*Education + gamma_SC_reason*Reason + gamma_SC_age*Age + eta_SC 
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()

  normalDensity_settings1 = list(outcomeNormal = BP_1, 
                      xNormal       = zeta_BP_1*BP, 
                      mu            = 0,
                      sigma		= sigma_BP1,
			    rows           = (task==1),
			    componentName = "indic_BP_1")
			    
			  
  normalDensity_settings2 = list(outcomeNormal = BP_2, 
                      xNormal        = zeta_BP_2*BP, 
                     mu            = 0,
                      sigma		= sigma_BP2,
			    rows           = (task==1),
			    componentName = "indic_BP_2")
  normalDensity_settings3 = list(outcomeNormal = BP_3, 
                      xNormal        = zeta_BP_3*BP, 
                      mu            = 0,
                      sigma		= sigma_BP3,
			    rows           = (task==1),
			    componentName = "indic_BP_3")
  normalDensity_settings4 = list(outcomeNormal = BP_4, 
                      xNormal        = zeta_BP_4*BP, 
                      mu            = 0,
                      sigma		= sigma_BP4,
			    rows           = (task==1),
			    componentName = "indic_BP_4")

  normalDensity_settings5 = list(outcomeNormal = BP_5, 
                      xNormal        = zeta_BP_5*BP, 
                      mu            = 0,
                      sigma		= sigma_BP5,
			    rows           = (task==1),
			    componentName = "indic_BP_5")

  normalDensity_settings6 = list(outcomeNormal = BP_6, 
                      xNormal        = zeta_BP_6*BP, 
                      mu            = 0,
                      sigma		= sigma_BP6,
			    rows           = (task==1),
			    componentName = "indic_BP_6")

  normalDensity_settings7 = list(outcomeNormal = BP_7, 
                      xNormal        = zeta_BP_7*BP, 
                      mu            = 0,
                      sigma		= sigma_BP7,
			    rows           = (task==1),
			    componentName = "indic_BP_7")
  normalDensity_settings8 = list(outcomeNormal = SC_1, 
                      xNormal        = zeta_SC_1*SC, 
                      mu            = 0,
                      sigma		= sigma_SC1,
			    rows           = (task==1),
			    componentName = "indic_SC_1")

  normalDensity_settings9 = list(outcomeNormal = SC_2, 
                      xNormal        = zeta_SC_2*SC, 
                      mu            = 0,
                      sigma		= sigma_SC2,
			    rows           = (task==1),
			    componentName = "indic_SC_2")
  normalDensity_settings10 = list(outcomeNormal = SC_3, 
                      xNormal        = zeta_SC_3*SC, 
                      mu            = 0,
                      sigma		= sigma_SC3,
			    rows           = (task==1),
			    componentName = "indic_SC_3")
  normalDensity_settings11 = list(outcomeNormal = SC_4, 
                      xNormal        = zeta_SC_4*SC, 
                      mu            = 0,
                      sigma		= sigma_SC4,
			    rows           = (task==1),
			    componentName = "indic_SC_4")

 P[["indic_BP_1"]] = apollo_normalDensity(normalDensity_settings1, functionality)
 P[["indic_BP_1"]] = apollo_panelProd(P[["indic_BP_1"]]    , apollo_inputs, functionality)
 P[["indic_BP_2"]] = apollo_normalDensity(normalDensity_settings2, functionality)
 P[["indic_BP_2"]] = apollo_panelProd(P[["indic_BP_2"]], apollo_inputs, functionality)
 P[["indic_BP_3"]] = apollo_normalDensity(normalDensity_settings3, functionality)
 P[["indic_BP_3"]] = apollo_panelProd(P[["indic_BP_3"]], apollo_inputs, functionality)
 P[["indic_BP_4"]] = apollo_normalDensity(normalDensity_settings4, functionality)
 P[["indic_BP_4"]] = apollo_panelProd(P[["indic_BP_4"]]  , apollo_inputs, functionality)
 P[["indic_BP_5"]] = apollo_normalDensity(normalDensity_settings5, functionality)
 P[["indic_BP_5"]] = apollo_panelProd(P[["indic_BP_5"]]    , apollo_inputs, functionality)
 P[["indic_BP_6"]] = apollo_normalDensity(normalDensity_settings6, functionality)
 P[["indic_BP_6"]] = apollo_panelProd(P[["indic_BP_6"]], apollo_inputs, functionality)
 P[["indic_BP_7"]] = apollo_normalDensity(normalDensity_settings7, functionality)
 P[["indic_BP_7"]] = apollo_panelProd(P[["indic_BP_7"]], apollo_inputs, functionality)
 P[["indic_SC_1"]] = apollo_normalDensity(normalDensity_settings8, functionality)
 P[["indic_SC_1"]] = apollo_panelProd(P[["indic_SC_1"]]    , apollo_inputs, functionality)
 P[["indic_SC_2"]] = apollo_normalDensity(normalDensity_settings9, functionality)
 P[["indic_SC_2"]] = apollo_panelProd(P[["indic_SC_2"]], apollo_inputs, functionality)
 P[["indic_SC_3"]] = apollo_normalDensity(normalDensity_settings10, functionality)
 P[["indic_SC_3"]] = apollo_panelProd(P[["indic_SC_3"]], apollo_inputs, functionality)
 P[["indic_SC_4"]] = apollo_normalDensity(normalDensity_settings11, functionality)
 P[["indic_SC_4"]] = apollo_panelProd(P[["indic_SC_4"]]  , apollo_inputs, functionality)
 
  ### Likelihood of choices
  ### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
  V = list()
 V[['alt1']] = (b_HT2*(HT_1=="2") + b_HT3*(HT_1=="3") + b_GM2*(GM_1=="2") + b_GM3*(GM_1=="3")+ b_TC2*(TC_1=="2") + b_TC3*(TC_1=="3")+lambda_BP*BP+lambda_BP_SC*BP*SC)     
 V[['alt2']] =(b_HT2*(HT_2=="2") + b_HT3*(HT_2=="3") + b_GM2*(GM_2=="2") + b_GM3*(GM_2=="3")+ b_TC2*(TC_2=="2") + b_TC3*(TC_2=="3")+lambda_BP*BP+lambda_BP_SC*BP*SC) 
      
   
  ### Define settings for MNL model component
  mnl_settings = list(
    alternatives = c(alt1=1, alt2=2),
    avail        = list(alt1=1, alt2=1),
    choiceVar    = choice,
    V    = V
  )
  
  ### Compute probabilities for MNL model component
  P[["model"]] = apollo_mnl(mnl_settings, 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                                            ####
# ################################################################# #
  
model = apollo_estimate(apollo_beta, apollo_fixed,apollo_probabilities, apollo_inputs,
estimate_settings=list(estimationRoutine="bfgs",maxIterations=100000))

Code: Select all

Testing influence of parametersError in apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities,  : 
  SPECIFICATION ISSUE - Parameters zeta_BP_1, zeta_BP_2, zeta_BP_3, zeta_BP_4, zeta_BP_5, zeta_BP_6, zeta_BP_7, zeta_SC_1, zeta_SC_2, zeta_SC_3, zeta_SC_4, sigma_BP1, sigma_BP2, sigma_BP3, sigma_BP4, sigma_BP5, sigma_BP6, sigma_BP7, sigma_SC1, sigma_SC2, sigma_SC3, sigma_SC4 do not influence the log-likelihood of your model!

Re: ICLV model questions

Posted: 21 Jul 2025, 17:03
by stephanehess
Hi

you are missing one crucial step in your specification, which is to combine the model components. At the moment, you are only using the MNL part.

What you need to do is to replace the final part of your code by:

Code: Select all

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

  ### Average across inter-individual draws
  P = apollo_avgInterDraws(P, apollo_inputs, functionality)

  ### Likelihood of the whole model
  P = apollo_combineModels(P, apollo_inputs, functionality)
  
  ### Prepare and return outputs of function
  P = apollo_prepareProb(P, apollo_inputs, functionality)
  return(P)
}
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