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MDCEV model prediction error

Ask questions about post-estimation functions (e.g. prediction, conditionals, etc) or other processing of results.
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yaoyao7031
Posts: 3
Joined: 28 Aug 2021, 01:54

MDCEV model prediction error

Post by yaoyao7031 » 26 Oct 2021, 19:37

Hi all,

I tried to do a model prediction using

Code: Select all

predictions_base = apollo_prediction(model, apollo_probabilities, apollo_inputs )
right after I finish estimating the model.

It works for the model I estimated without a latent class. However, after I added the latent class to the MDCEV model and run the same predition code, it shows the following error:

Error in apollo_lc(lc_settings, apollo_inputs, functionality) :
Class-probability variable for model component "LC" has more elements than in-class-probability.

Since the model estimation works well, my code should be fine. Anyone knows why the model prediction does not work here?

Many thanks!

stephanehess
Site Admin
Posts: 558
Joined: 24 Apr 2020, 16:29

Re: MDCEV model prediction error

Post by stephanehess » 27 Oct 2021, 10:26

Hi

could you please show us the code so we can help understand what is happening

Thanks

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk

yaoyao7031
Posts: 3
Joined: 28 Aug 2021, 01:54

Re: MDCEV model prediction error

Post by yaoyao7031 » 28 Oct 2021, 06:40

Hi Stephane,

Thank you for your quick response. I was trying to do some debug myself. I think the problem occurs when a latent class is added to the MDCEV model. Within the prediction function, apollo_probabilities uses the function "prediction" rather than "estimate". But P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality = "prediction") caused the bug. Because when functionality is "estimate", apollo_panelProd calculates the probability for each INDIVIDUAL instead of each OBSERVATION for the panel data. But when it is "prediction", somehow it still gives the probability at the observation level instead of the individual level. My guess is MDCEV takes a lot of draws and somehow messed it up when latent class is added? I tried the prediction without latent class and no problem at all.

Here is my code which is a bit long. I can provide the saved R file in private if it helps.

Thank you.

Code: Select all

apollo_beta = c(
  gamma_v1_a      = 1.073, 
  gamma_v2_a         = 1.192,
  gamma_v3_a       = 1.369,
  gamma_v4_a     = 1.120,
  gamma_v5_a = 1.185,
  gamma_outside_a      = 0,
  delta_v1_a      = -6.252,
  delta_v2_a         = -6.242,
  delta_v3_a       = -6.264,
  delta_v4_a     = -6.151,
  delta_v5_a = -6.237,
  delta_outside_a      = 0,
  
  gamma_v1_b      = 1.152, 
  gamma_v2_b         = 1.194,
  gamma_v3_b       = 1.282,
  gamma_v4_b     = 1.272,
  gamma_v5_b = 1.155,
  gamma_outside_b      = 0,
  delta_v1_b      = -3.759,
  delta_v2_b         = -3.873,
  delta_v3_b       = -3.705,
  delta_v4_b     = -3.812,
  delta_v5_b = -3.484,
  delta_outside_b      = 0,
  
  sigma              = 0.99,
  bweekend_a = 0.059, bnpv_a = -0.005,  bnclick_a = 0.077,  bnbuy_a = -0.540  , nrccpv_a=-0.011, nrccclick_a=0.267, nrccbuy_a=0.112, 
  badstockpsai_a = 1.082, badstockgamma1_a = 0.203, bresidual_a = 0,
  bweekend_b = -0.013, bnpv_b = -0.011,  bnclick_b = 0.080,  bnbuy_b = -1.005  , nrccpv_b=-0.055, nrccclick_b=0.194, nrccbuy_b=-0.806, 
  badstockpsai_b = 1.087, badstockgamma1_b = 0.098, bresidual_b = 0,
  
  delta_a         = 8.197,
  gamma_N_pot_subcat_full_a = -3.338,
  gamma_Z_pvperday_a  = -5.954
  
)

### 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( "delta_outside_a", "delta_outside_b", "gamma_outside_a", "gamma_outside_b" , "sigma"    ) #, "delta_v5_b")

# ################################################################# #
#### DEFINE LATENT CLASS COMPONENTS                              ####
# ################################################################# #

apollo_lcPars=function(apollo_beta, apollo_inputs){
  lcpars = list()
  lcpars[["gamma_v1"]] = list(gamma_v1_a, gamma_v1_b)
  lcpars[["gamma_v2"]] = list(gamma_v2_a, gamma_v2_b)
  lcpars[["gamma_v3"]] = list(gamma_v3_a, gamma_v3_b)
  lcpars[["gamma_v4"]] = list(gamma_v4_a, gamma_v4_b)
  lcpars[["gamma_v5"]] = list(gamma_v5_a, gamma_v5_b)
  lcpars[["gamma_outside"]] = list(gamma_outside_a, gamma_outside_b)
  
  
  lcpars[["delta_v1"]] = list(delta_v1_a, delta_v1_b)
  lcpars[["delta_v2"]] = list(delta_v2_a, delta_v2_b)
  lcpars[["delta_v3"]] = list(delta_v3_a, delta_v3_b)
  lcpars[["delta_v4"]] = list(delta_v4_a, delta_v4_b)
  lcpars[["delta_v5"]] = list(delta_v5_a, delta_v5_b)
  lcpars[["delta_outside"]] = list(delta_outside_a, delta_outside_b)
  
  lcpars[["bweekend"]] = list(bweekend_a ,  bweekend_b )
  lcpars[["bnpv"]] = list( bnpv_a ,   bnpv_b )
  lcpars[["bnclick"]] = list( bnclick_a ,   bnclick_b )
  lcpars[["bnbuy"]] = list(bnbuy_a ,  bnbuy_b )
  lcpars[["nrccpv"]] = list(nrccpv_a ,  nrccpv_b )
  lcpars[["nrccclick"]] = list(nrccclick_a ,  nrccclick_b )
  lcpars[["nrccbuy"]] = list(nrccbuy_a ,  nrccbuy_b )
  lcpars[["badstockpsai"]] = list(badstockpsai_a ,  badstockpsai_b )
  lcpars[["badstockgamma1"]] = list(badstockgamma1_a ,  badstockgamma1_b )
  lcpars[["bresidual"]] = list(bresidual_a, bresidual_b)
  
  
  
  #This part is the probability of each class. 
  V=list()
  V[["class_a"]] = delta_a + gamma_N_pot_subcat_full_a*N_pot_subcat_full + gamma_Z_pvperday_a*Z_pvperday
  V[["class_b"]] = 0
  
  #This part is the probability of each class.   
  mnl_settings = list(
    alternatives = c(class_a=1, class_b=2), 
    avail        = 1, 
    choiceVar    = NA, 
    V            = V
  )
  
  lcpars[["pi_values"]] = apollo_mnl(mnl_settings, functionality="raw") #this part returns the probability. It use "raw" to ensure that the probabilities are returned for all alternatives.
  #This is also why avail choicevar is NA
  ##This code below makes sure that the probability is assigned to each individual, not each observation in panel data
  lcpars[["pi_values"]] = apollo_firstRow(lcpars[["pi_values"]], apollo_inputs)
  
  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()
  
  ### Define individual alternatives
  alternatives = c("v1", 
                   "v2", 
                   "v3", 
                   "v4", 
                   "v5", 
                   "outside")
  
  ### Define availabilities
  avail = list(v1  = availnew1,    ######Need to check whether it works here
               v2     = availnew2,
               v3   = availnew3,
               v4 = availnew4,
               v5 = availnew5,
               outside  = avail15)
  
  ### Define continuous consumption for individual alternatives
  continuousChoice = list(v1  =clicknew1,
                          v2     =clicknew2,
                          v3   =clicknew3,
                          v4 =clicknew4,
                          v5 =clicknew5,
                          outside  =outsideclick)
  
  
  
  ### Define alpha parameters
  alpha = list(v1  = 1e-3 , 
               v2     = 1e-3 , 
               v3   = 1e-3 , 
               v4 = 1e-3 , 
               v5 = 1e-3 ,
               # v6   = 1e-3 ,
               # v7  = 1e-3 , 
               # v8 = 1e-3 , 
               # v9  = 1e-3 , 
               # v10 = 1e-3 , 
               # v11  = 1e-3 , 
               # v12     = 1e-3 , 
               # v13   = 1e-3 , 
               # v14 = 1e-3 , 
               outside  = 1e-3 )
  
  
  ### Define costs for individual alternatives
  cost = list(v1      = 1, 
              v2         = 1,
              v3       = 1,
              v4     = 1,
              v5 = 1,
              # v6       = 1, 
              # v7      = 1,
              # v8     = 1,
              # v9      = 1,
              # v10     = 1,
              # v11      = 1, 
              # v12         = 1,
              # v13       = 1,
              # v14     = 1,
              outside       = 1)
  
  ### Define budget
  budget = sum_click_updated
  
  ### Define settings for MDCEV model
  mdcev_settings <- list(alternatives      = alternatives,
                         avail             = avail,
                         continuousChoice  = continuousChoice,
                         #V                 = V,
                         alpha             = alpha,
                         #gamma             = gamma, 
                         sigma             = sigma, 
                         cost              = cost,
                         budget            = budget)
  
  
  
  ### Loop over classes
  s=1
  while(s<=2){
    
    ### ### Compute class-specific utilities
    V = list()
    
    V[["v1"    ]] = delta_v1[[s]] +  bweekend[[s]]*weekend + bnpv[[s]]*npvnew1 + bnclick[[s]]*nclicknew1 + bnbuy[[s]]*nbuynew1 + 
      nrccpv[[s]]*rccpvnew1 + nrccclick[[s]]*rccclicknew1 + nrccbuy[[s]]*rccbuynew1 + badstockpsai[[s]]*ad1new + bresidual[[s]]*res1
    V[["v2"    ]] = delta_v2[[s]]  + bweekend[[s]]*weekend + bnpv[[s]]*npvnew2 + bnclick[[s]]*nclicknew2 + bnbuy[[s]]*nbuynew2 + 
      nrccpv[[s]]*rccpvnew2 + nrccclick[[s]]*rccclicknew2 + nrccbuy[[s]]*rccbuynew2 + badstockpsai[[s]]*ad2new + bresidual[[s]]*res2
    V[["v3"  ]] = delta_v3[[s]]    + bweekend[[s]]*weekend + bnpv[[s]]*npvnew3 + bnclick[[s]]*nclicknew3 + bnbuy[[s]]*nbuynew3 + 
      nrccpv[[s]]*rccpvnew3 + nrccclick[[s]]*rccclicknew3 + nrccbuy[[s]]*rccbuynew3 + badstockpsai[[s]]*ad3new + bresidual[[s]]*res3
    V[["v4"]] = delta_v4[[s]]  + bweekend[[s]]*weekend + bnpv[[s]]*npvnew4 + bnclick[[s]]*nclicknew4 + bnbuy[[s]]*nbuynew4 + 
      nrccpv[[s]]*rccpvnew4 + nrccclick[[s]]*rccclicknew4 + nrccbuy[[s]]*rccbuynew4 + badstockpsai[[s]]*ad4new + bresidual[[s]]*res4
    V[["v5"]] = delta_v5[[s]]  + bweekend[[s]]*weekend + bnpv[[s]]*npvnew5 + bnclick[[s]]*nclicknew5 + bnbuy[[s]]*nbuynew5 + 
      nrccpv[[s]]*rccpvnew5 + nrccclick[[s]]*rccclicknew5 + nrccbuy[[s]]*rccbuynew5 + badstockpsai[[s]]*ad5new + bresidual[[s]]*res5
    V[["outside"]] = delta_outside[[s]]
    
    
    ### Define gamma parameters
    gamma = list(v1      = gamma_v1[[s]]+ badstockgamma1[[s]]*ad1new, 
                 v2         = gamma_v2[[s]]+ badstockgamma1[[s]]*ad2new, 
                 v3       = gamma_v3[[s]]+ badstockgamma1[[s]]*ad3new, 
                 v4     = gamma_v4[[s]]+ badstockgamma1[[s]]*ad4new, 
                 v5 = gamma_v5[[s]]+ badstockgamma1[[s]]*ad5new, 
                 outside      = gamma_outside[[s]])
    
    mdcev_settings$V = V
    mdcev_settings$gamma = gamma
    mdcev_settings$componentName = paste0("Class_",s)
    
    ### Compute within-class choice probabilities using MNL model
    P[[paste0("Class_",s)]] = apollo_mdcev(mdcev_settings, functionality)
    
    ### Take product across observation for same individual
    P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality)
    
    s=s+1}
  
  ### Compute latent class model probabilities
  lc_settings   = list(inClassProb = P, classProb=pi_values)
  P[["model"]] = apollo_lc(lc_settings, 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 = "bhhh", maxIterations = 20000))


# ----------------------------------------------------------------- #
#---- MODEL PREDICTIONS AND ELASTICITY CALCULATIONS              ----
# ----------------------------------------------------------------- #

### Use the estimated model to make predictions not working here.
predictions_base = apollo_prediction(model, apollo_probabilities, apollo_inputs, prediction_settings=list(runs=30, modelComponent = "LC"))

stephanehess
Site Admin
Posts: 558
Joined: 24 Apr 2020, 16:29

Re: MDCEV model prediction error

Post by stephanehess » 05 Nov 2021, 15:20

Hi

apologies for the slow reply. If you could share the data with us by e-mail, then we can look into this issue for you

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk

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