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Error in Latent Class Model with LV

Ask questions about the results reported after estimation. If the output includes errors, please include your model code if possible.
Post Reply
mdl
Posts: 2
Joined: 13 Aug 2021, 12:00

Error in Latent Class Model with LV

Post by mdl » 13 Aug 2021, 13:50

Hi,
I am trying to estimate a latent class model where a latent variable explains the class allocation. I don't know if there are any errors in my code, but after the estimation result a lot of Nas appear. It that, in the output file NAs appear between "Estimates" and "Summary of class allocation for LC model component choice". And them between "Summary of class allocation for LC model component choice" and "Classical covariance matrix".

I attach my code and the result of the estimation. I really appreciate the help.

Code: Select all

 ### Clear memory
rm(list = ls())
##-------------------------------------------------------------------------------------##
## LOADING PACKAGES ##

require(apollo)
library(apollo) 
##-------------------------------------------------------------------------------------##
### INITIALICE CODE 

apollo_initialise()

### Set core controls
apollo_control = list(
  modelName  ="model",
  modelDescr ="latent variable in latent class",
  indivID    ="ID_Global",
  mixing     = TRUE,
  analyticGrad = FALSE,
  nCores     = 4  
)
#### DEFINE MODEL PARAMETERS  

apollo_beta=c( 
  # Choice class params
  c1_asc_Alt1        = 3,
  c1_asc_Alt2        = 2,
  c1_asc_Alt4      = 0,
  c1_asc_Alt3      = 0,
  
  c2_asc_Alt3      = 3,
  c2_asc_Alt1        = 2,
  c2_asc_Alt4      = 1,
  c2_asc_Alt2        = 0,
  
  c1_b_distancia_B   = -0.3,
  c2_b_distancia_B   = -0.2,
  c1_b_costo_B       = -0.3,
  c2_b_costo_B       = -0.2,
  c1_b_busqueda_B    = 0,
  c2_b_busqueda_B    = 0,
  c1_b_descarga_B1   = 0.26,
  c2_b_descarga_B1   = 0,
  c1_b_costo_S       = 0,
  c2_b_costo_S       = 0,
  c1_b_realizadas_NP = 0,
  c2_b_realizadas_NP = 0,
  c1_b_ventana_S    = 0,
  c2_b_ventana_S    = 0,
  
  # Class allocation model params
  c1_delta=0,
  c2_delta=0.5,
  delta_COND = 0,
  
  # LV Structural equation params
  
  # LV measurement model params  
  zeta_EstProh_COND     = 0.106,
  tau_EstProh_1         = -3,
  tau_EstProh_2         = -2,
  tau_EstProh_3         = -1,
  tau_EstProh_4         =  0,
  zeta_EstSeg_COND      = 0.166,
  tau_EstSeg_1          = -3,
  tau_EstSeg_2          = -2,
  tau_EstSeg_3          = -1,
  tau_EstSeg_4          = 0,
  zeta_CauCong_COND     = 0.748,
  tau_CauCong_1         = -5,
  tau_CauCong_2         = 0.05,
  tau_CauCong_3         = 1,
  tau_CauCong_4         = 7,
  zeta_DescVia_COND     = 0.462,
  tau_DescVia_1         = -4,
  tau_DescVia_2         = 0,
  tau_DescVia_3         = 0.35,
  tau_DescVia_4         = 4,
  zeta_EstAnd_COND      = 0.368,
  tau_EstAnd_1          = -3,
  tau_EstAnd_2          = 0.08,
  tau_EstAnd_3          = 0.15,
  tau_EstAnd_4          = 3 
)

apollo_fixed = c("c1_asc_Alt3", "c1_delta", "c2_asc_Alt2", "c2_b_descarga_B1", "c1_b_costo_S", "c1_b_ventana_S")

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

apollo_draws = list(
  interDrawsType="mlhs", 
  interNDraws=1000,   
  interUnifDraws=c(),      
  interNormDraws=c("eta1"), 
  
  intraDrawsType="mlhs",
  intraNDraws=0,          
  intraUnifDraws=c(),     
  intraNormDraws=c()      
)

apollo_randCoeff=function(apollo_beta, apollo_inputs){
  randcoeff = list()
  randcoeff[["COND"]] =  eta1 
  return(randcoeff)
}

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

apollo_lcPars=function(apollo_beta, apollo_inputs){
  
  lcpars = list()
  
  lcpars[["b_asc_Alt4"]]=list(c1_asc_Alt4,c2_asc_Alt4)
  lcpars[["b_asc_Alt1"]]=list(c1_asc_Alt1,c2_asc_Alt1)
  lcpars[["b_asc_Alt2"]]=list(c1_asc_Alt2,c2_asc_Alt2)
  lcpars[["b_asc_Alt3"]]=list(c1_asc_Alt3,c2_asc_Alt3)
  lcpars[["b_distancia_B"]]=list(c1_b_distancia_B,c2_b_distancia_B)
  lcpars[["b_costo_B"]]=list(c1_b_costo_B,c2_b_costo_B)
  lcpars[["b_busqueda_B"]]=list(c1_b_busqueda_B,c2_b_busqueda_B)
  lcpars[["b_descarga_B1"]]=list(c1_b_descarga_B1,c2_b_descarga_B1)
  lcpars[["b_costo_S"]]=list(c1_b_costo_S,c2_b_costo_S)
  lcpars[["b_realizadas_NP"]]=list(c1_b_realizadas_NP,c2_b_realizadas_NP)
  lcpars[["b_ventana_S"]]=list(c1_b_ventana_S,c2_b_ventana_S)
  
  V=list()
  V[["class_1"]] = c1_delta
  V[["class_2"]] = c2_delta + delta_COND * COND 
  
  Cla = list(
    alternatives = c(class_1=1, class_2=2), 
    avail         = 1,
    choiceVar    = NA, 
    V            = V
  )
  
  lcpars[["pi_values"]] = apollo_mnl(Cla, functionality = "raw")
  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))
  
  P = list()
  PClass = list()
  
  ### Likelihood of indicators 
  { ol_settings1 = list(outcomeOrdered=Estac_Prohibido, 
                        V=zeta_EstProh_COND*COND, 
                        tau=list(tau_EstProh_1, tau_EstProh_2, tau_EstProh_3, tau_EstProh_4),
                        coding = list(1,2,3,4,5),
                        rows=(Escenario==1),
                        componentName  = "indic_Estac_Prohibido")}		
  
  { ol_settings2 = list(outcomeOrdered=Estac_Seguro, 
                        V=zeta_EstSeg_COND*COND, 
                        tau=list(tau_EstSeg_1, tau_EstSeg_2, tau_EstSeg_3, tau_EstSeg_4),
                        coding = list(1,2,3,4,5), 
                        rows=(Escenario==1),
                        componentName  = "indic_Estac_Seguro")}					  
  
  { ol_settings3 = list(outcomeOrdered=Causa_Congestion, 
                        V=zeta_CauCong_COND*COND, 
                        tau=list(tau_CauCong_1, tau_CauCong_2, tau_CauCong_3, tau_CauCong_4),
                        coding = list(1,2,3,4,5), 
                        rows=(Escenario==1),
                        componentName  = "indic_Causa_Congestion")}
  
  { ol_settings4 = list(outcomeOrdered=Descarga_EnVia, 
                        V=zeta_DescVia_COND*COND, 
                        tau=list(tau_DescVia_1, tau_DescVia_2, tau_DescVia_3, tau_DescVia_4),
                        coding = list(1,2,3,4,5), 
                        rows=(Escenario==1),
                        componentName  = "indic_Descarga_EnVia")}					  
  
  { ol_settings5 = list(outcomeOrdered=Estac_Anden, 
                        V=zeta_EstAnd_COND*COND, 
                        tau=list(tau_EstAnd_1, tau_EstAnd_2, tau_EstAnd_3, tau_EstAnd_4),
                        coding = list(1,2,3,4,5), 
                        rows=(Escenario==1),
                        componentName  = "indic_Estac_Anden")}
  
  ### Keep only the first row, as it is the only one that matters
  {  
    P[["indic_Estac_Prohibido"]]  = apollo_firstRow(apollo_ol(ol_settings1, functionality), apollo_inputs )
    P[["indic_Estac_Seguro"]]     = apollo_firstRow(apollo_ol(ol_settings2, functionality), apollo_inputs )
    P[["indic_Causa_Congestion"]] = apollo_firstRow(apollo_ol(ol_settings3, functionality), apollo_inputs )
    P[["indic_Descarga_EnVia"]]   = apollo_firstRow(apollo_ol(ol_settings4, functionality), apollo_inputs )
    P[["indic_Estac_Anden"]]      = apollo_firstRow(apollo_ol(ol_settings5, functionality), apollo_inputs )
  } 
  
  for(s in 1:2){
    
    Transf_Tdescarga = log(T_Descarga)
    asc_Alt4      = b_asc_Alt4[[s]]
    asc_Alt1        = b_asc_Alt1[[s]]
    asc_Alt2        = b_asc_Alt2[[s]]
    asc_Alt3      = b_asc_Alt3[[s]]
    Beta_Distancia  = b_distancia_B[[s]] 
    Beta_Costo      = b_costo_B[[s]]   
    Beta_Busqueda   = b_busqueda_B[[s]] 
    Beta_Descarga   = b_descarga_B1[[s]] 
    Beta_CostoMulta = b_costo_S[[s]]   
    Beta_Ventana    = b_ventana_S[[s]] 
    Beta_Realizadas = b_realizadas_NP[[s]] 
    
    V = list()
    V[['Alt1']]   = asc_Alt1 +  Beta_Distancia * Distancia1 + Beta_Costo * Costo1  + Beta_Busqueda * T_busquedaSP + Beta_Descarga * Transf_Tdescarga
    V[['Alt2']]   = asc_Alt2 +  Beta_Distancia * Distancia2 + Beta_Costo * Costo2  + Beta_Busqueda * T_busquedaSP 
    V[['Alt3']] = asc_Alt3                              + Beta_CostoMulta * Costo_multa  + Beta_Ventana * T_ventana 
    V[['Alt4']] = asc_Alt4 + Beta_Realizadas * N_Realizadas 
    
    Cho = list(
      alternatives  = c(Alt1=1, Alt2=2, Alt3=3, Alt4=4),
      avail         = 1, 
      choiceVar     = Choice,
      V             = V
    )
    
    PClass[[paste0("Class_",s)]] = apollo_mnl(Cho, functionality)
    PClass[[paste0("Class_",s)]] = apollo_panelProd(PClass[[paste0("Class_",s)]], apollo_inputs ,functionality)
  }
  
  lc_settings   = list(inClassProb=PClass, classProb=pi_values)
  P[["choice"]] = apollo_lc(lc_settings, apollo_inputs, functionality)
  
  
  P = apollo_combineModels(P, apollo_inputs, functionality)
  P = apollo_avgInterDraws(P, apollo_inputs, functionality)
  P = apollo_prepareProb(P, apollo_inputs, functionality)
  return(P)
}

# ################################################################# #
#### MODEL ESTIMATION AND OUTPUT                                 ####
# ################################################################# #
s = list(writeIter=FALSE, hessianRoutine="maxLik")
model = apollo_estimate(apollo_beta, apollo_fixed, 
                        apollo_probabilities, apollo_inputs,
                        estimate_settings=s)

apollo_modelOutput(model)

Code: Select all


Estimation method                : bfgs
Model diagnosis                  : successful convergence 
Number of individuals            : 232
Number of rows in database       : 1392
Number of modelled outcomes      : 0

Number of cores used             :  4 
Number of inter-individual draws : 1000 (mlhs)

LL(start)                        : -3000.255
LL(0, whole model)               : Not applicable
LL(final, whole model)           : -2213.946
Rho-square (0)                   : Not applicable
Adj.Rho-square (0)               : Not applicable
AIC                              :  4515.89 
BIC                              :  -Inf 

LL(0,indic_Estac_Prohibido)      : Not applicable
LL(final,indic_Estac_Prohibido)  : -187.3616
LL(0,indic_Estac_Seguro)         : Not applicable
LL(final,indic_Estac_Seguro)     : -148.5431
LL(0,indic_Causa_Congestion)     : Not applicable
LL(final,indic_Causa_Congestion) : -241.5213
LL(0,indic_Descarga_EnVia)       : Not applicable
LL(final,indic_Descarga_EnVia)   : -247.3904
LL(0,indic_Estac_Anden)          : Not applicable
LL(final,indic_Estac_Anden)      : -258.7416
LL(0,choice)                     : -1929.722
LL(final,choice)                 : -1197.325

Estimated parameters             :  44
Time taken (hh:mm:ss)            :  00:36:19.61 
     pre-estimation              :  00:00:15.86 
     estimation                  :  00:17:54.9 
     post-estimation             :  00:18:8.85 
Iterations                       :  67  
Min abs eigenvalue of Hessian    :  0.158839 

These outputs have had the scaling used in estimation applied to them.
Estimates:
                      Estimate        s.e.   t.rat.(0)    Rob.s.e. Rob.t.rat.(0)
c1_asc_Alt1            3.89456    0.761095       5.117    0.782929         4.974
c1_asc_Alt2            3.09162    0.718145       4.305    0.648786         4.765
c1_asc_Alt4            0.78612    0.497974       1.579    0.606661         1.296
c1_asc_Alt3            0.00000          NA          NA          NA            NA
c2_asc_Alt3            3.09179    0.582839       5.305    0.468083         6.605
c2_asc_Alt1            1.15339    0.429636       2.685    0.493792         2.336
c2_asc_Alt4            1.17678    0.561634       2.095    0.461613         2.549
c2_asc_Alt2            0.00000          NA          NA          NA            NA
c1_b_distancia_B      -0.32401    0.186774      -1.735    0.154648        -2.095
c2_b_distancia_B      -0.33330    0.184432      -1.807    0.138277        -2.410
c1_b_costo_B          -2.91558    1.323555      -2.203    1.274323        -2.288
c2_b_costo_B          -3.74226    1.805568      -2.073    1.763677        -2.122
c1_b_busqueda_B        0.03850    0.016340       2.356    0.015673         2.456
c2_b_busqueda_B        0.02492    0.008834       2.821    0.008386         2.972
c1_b_descarga_B1       0.27283    0.106815       2.554    0.207931         1.312
c2_b_descarga_B1       0.00000          NA          NA          NA            NA
c1_b_costo_S           0.00000          NA          NA          NA            NA
c2_b_costo_S          -0.02487    0.011074      -2.245    0.007063        -3.521
c1_b_realizadas_NP     0.03792    0.011426       3.318    0.010901         3.478
c2_b_realizadas_NP     0.02670    0.008881       3.007    0.014045         1.901
c1_b_ventana_S         0.00000          NA          NA          NA            NA
c2_b_ventana_S        -0.01660    0.005187      -3.200    0.003112        -5.334
c1_delta               0.00000          NA          NA          NA            NA
c2_delta              -0.58745    0.162773      -3.609    0.172236        -3.411
delta_COND            -0.89267    0.231729      -3.852    0.307975        -2.899
zeta_EstProh_COND      0.56500    0.202217       2.794    0.262163         2.155
tau_EstProh_1         -4.90195    0.721978      -6.790    0.771544        -6.353
tau_EstProh_2         -2.20425    0.230855      -9.548    0.260521        -8.461
tau_EstProh_3         -2.07603    0.221241      -9.384    0.240758        -8.623
tau_EstProh_4          2.05501    0.217787       9.436    0.224467         9.155
zeta_EstSeg_COND       1.01989    0.243435       4.190    0.333000         3.063
tau_EstSeg_1          -5.27864    0.755264      -6.989    0.886779        -5.953
tau_EstSeg_2          -3.70392    0.414851      -8.928    0.452692        -8.182
tau_EstSeg_3          -3.27384    0.361028      -9.068    0.361102        -9.066
tau_EstSeg_4           2.30246    0.260666       8.833    0.310463         7.416
zeta_CauCong_COND      3.18670    0.968029       3.292    1.131795         2.816
tau_CauCong_1         -7.79369    1.866200      -4.176    2.133603        -3.653
tau_CauCong_2         -2.85794    0.724525      -3.945    0.839872        -3.403
tau_CauCong_3         -2.27036    0.609080      -3.728    0.692008        -3.281
tau_CauCong_4          4.86430    1.193871       4.074    1.298885         3.745
zeta_DescVia_COND      1.48486    0.263147       5.643    0.337978         4.393
tau_DescVia_1         -5.74120    0.805374      -7.129    0.702537        -8.172
tau_DescVia_2         -1.29085    0.214847      -6.008    0.228039        -5.661
tau_DescVia_3         -0.96402    0.202328      -4.765    0.207673        -4.642
tau_DescVia_4          3.19690    0.356553       8.966    0.420823         7.597
zeta_EstAnd_COND       1.02294    0.205432       4.979    0.247644         4.131
tau_EstAnd_1          -3.75682    0.402556      -9.332    0.374720       -10.026
tau_EstAnd_2          -0.87257    0.167421      -5.212    0.167208        -5.218
tau_EstAnd_3          -0.80342    0.165497      -4.855    0.163333        -4.919
tau_EstAnd_4           2.35513    0.250420       9.405    0.275018         8.564

NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
...

Summary of class allocation for LC model component choice:
         Mean prob.
Class_1      0.6230
Class_2      0.3770

NA
NA
NA
NA
NA
NA
NA
NA
...

Classical covariance matrix:


dpalma
Posts: 117
Joined: 24 Apr 2020, 17:54

Re: Error in Latent Class Model with LV

Post by dpalma » 14 Aug 2021, 13:45

Hi,

Thank you for bringing this to our attention. There is indeed a bug in Apollo v0.2.5 when estimating models with latent variables inside the class allocation. I have produced a new version without this problem that you can download from one of the links below, depending on your operation system. If you are using macOS, let us know and we will generate a version for you, but it will take a bit longer.

For windows: https://www.dropbox.com/s/sgykepcs31ptq ... 6.zip?dl=1
For Linux: https://www.dropbox.com/s/qlgar6735zmq6 ... ar.gz?dl=1

Once you have downloaded the file, you can install it using the following command.

Code: Select all

install.packages("C:/pathToFile/apollo_0.2.6.zip", repos=NULL)
Where you need to replace C:/pathToFile for the corresponding path to the file in your computer.

Also, please be aware that the syntax for latent class models was slightly modified since v0.2.5. The only change is that inside apollo_lcPars, instead of calling apollo_mnl(mnl_setting, functionality='raw'), you should call apollo_classAlloc(classAlloc_settings). You can download a example from here.

In your case, I believe you would only need to change the definition of apollo_lcPars to the following (I don't have the data, so I couldn't test it).

Code: Select all

apollo_lcPars=function(apollo_beta, apollo_inputs){
  
  lcpars = list()
  
  lcpars[["b_asc_Alt4"]]=list(c1_asc_Alt4,c2_asc_Alt4)
  lcpars[["b_asc_Alt1"]]=list(c1_asc_Alt1,c2_asc_Alt1)
  lcpars[["b_asc_Alt2"]]=list(c1_asc_Alt2,c2_asc_Alt2)
  lcpars[["b_asc_Alt3"]]=list(c1_asc_Alt3,c2_asc_Alt3)
  lcpars[["b_distancia_B"]]=list(c1_b_distancia_B,c2_b_distancia_B)
  lcpars[["b_costo_B"]]=list(c1_b_costo_B,c2_b_costo_B)
  lcpars[["b_busqueda_B"]]=list(c1_b_busqueda_B,c2_b_busqueda_B)
  lcpars[["b_descarga_B1"]]=list(c1_b_descarga_B1,c2_b_descarga_B1)
  lcpars[["b_costo_S"]]=list(c1_b_costo_S,c2_b_costo_S)
  lcpars[["b_realizadas_NP"]]=list(c1_b_realizadas_NP,c2_b_realizadas_NP)
  lcpars[["b_ventana_S"]]=list(c1_b_ventana_S,c2_b_ventana_S)
  
  classAlloc_settings = list(
    V = list(class_1 = c1_delta,
             class_2 = c2_delta + delta_COND * COND)
  )
  lcpars[["pi_values"]] = apollo_classAlloc(classAlloc_settings)
  
  return(lcpars)
}
Please try installing the new version, making the changes to apollo_lcPars described above, and let us know how it goes.

Best
David

mdl
Posts: 2
Joined: 13 Aug 2021, 12:00

Re: Error in Latent Class Model with LV

Post by mdl » 18 Aug 2021, 09:49

Thanks for the reply David. Now everything works very well! :)

Maira

bokapatsila
Posts: 19
Joined: 28 Jul 2021, 02:41

Re: Error in Latent Class Model with LV

Post by bokapatsila » 10 Nov 2021, 17:00

dpalma wrote:
14 Aug 2021, 13:45
Hi,

Thank you for bringing this to our attention. There is indeed a bug in Apollo v0.2.5 when estimating models with latent variables inside the class allocation. I have produced a new version without this problem that you can download from one of the links below, depending on your operation system. If you are using macOS, let us know and we will generate a version for you, but it will take a bit longer.

For windows: https://www.dropbox.com/s/sgykepcs31ptq ... 6.zip?dl=1
For Linux: https://www.dropbox.com/s/qlgar6735zmq6 ... ar.gz?dl=1

Once you have downloaded the file, you can install it using the following command.

Code: Select all

install.packages("C:/pathToFile/apollo_0.2.6.zip", repos=NULL)
Where you need to replace C:/pathToFile for the corresponding path to the file in your computer.

Also, please be aware that the syntax for latent class models was slightly modified since v0.2.5. The only change is that inside apollo_lcPars, instead of calling apollo_mnl(mnl_setting, functionality='raw'), you should call apollo_classAlloc(classAlloc_settings). You can download a example from here.

In your case, I believe you would only need to change the definition of apollo_lcPars to the following (I don't have the data, so I couldn't test it).

Code: Select all

apollo_lcPars=function(apollo_beta, apollo_inputs){
  
  lcpars = list()
  
  lcpars[["b_asc_Alt4"]]=list(c1_asc_Alt4,c2_asc_Alt4)
  lcpars[["b_asc_Alt1"]]=list(c1_asc_Alt1,c2_asc_Alt1)
  lcpars[["b_asc_Alt2"]]=list(c1_asc_Alt2,c2_asc_Alt2)
  lcpars[["b_asc_Alt3"]]=list(c1_asc_Alt3,c2_asc_Alt3)
  lcpars[["b_distancia_B"]]=list(c1_b_distancia_B,c2_b_distancia_B)
  lcpars[["b_costo_B"]]=list(c1_b_costo_B,c2_b_costo_B)
  lcpars[["b_busqueda_B"]]=list(c1_b_busqueda_B,c2_b_busqueda_B)
  lcpars[["b_descarga_B1"]]=list(c1_b_descarga_B1,c2_b_descarga_B1)
  lcpars[["b_costo_S"]]=list(c1_b_costo_S,c2_b_costo_S)
  lcpars[["b_realizadas_NP"]]=list(c1_b_realizadas_NP,c2_b_realizadas_NP)
  lcpars[["b_ventana_S"]]=list(c1_b_ventana_S,c2_b_ventana_S)
  
  classAlloc_settings = list(
    V = list(class_1 = c1_delta,
             class_2 = c2_delta + delta_COND * COND)
  )
  lcpars[["pi_values"]] = apollo_classAlloc(classAlloc_settings)
  
  return(lcpars)
}
Please try installing the new version, making the changes to apollo_lcPars described above, and let us know how it goes.

Best
David
Dear David,

When using v 0.2.6 I get the message "Variable(s) {calculateLLC} were not recognised in apollo_control and will be ignored. Check
?apollo_control for a list of valid control variables." at the validate inputs stage even for the ILVLC example you shared. What should be done to fix it?

stephanehess
Site Admin
Posts: 440
Joined: 24 Apr 2020, 16:29

Re: Error in Latent Class Model with LV

Post by stephanehess » 29 Nov 2021, 13:37

Hi

we ran the file on our machines and cannot replicate the error you receive. Is it still happening?

Can you share the full on screen output, please?

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

bokapatsila
Posts: 19
Joined: 28 Jul 2021, 02:41

Re: Error in Latent Class Model with LV

Post by bokapatsila » 29 Nov 2021, 15:25

Thank you for the reply. I reinstalled Apollo and stopped receiving the warning.

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