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Error in estimating a mixed logit model

Ask questions about errors you encouunter. Please make sure to include full details about your model specifications, and ideally your model file.
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yukiyama
Posts: 2
Joined: 08 Nov 2021, 02:41

Error in estimating a mixed logit model

Post by yukiyama » 08 Nov 2021, 05:29

Hi,

I am estimating a mixed logit model with the latest version of Apollo (0.2.5) and encounter the following error:

==========
Computing covariance matrix using numerical methods (maxLik). This may take a
while, no progress bar displayed.
WARNING: Some eigenvalues of the Hessian are positive, indicating convergence to
a saddle point!
Computing score matrix...
Error in apollo_inputs$apollo_control$mixing && is.function(apollo_inputs$apollo_randCoeff) :
invalid 'x' type in 'x && y'

==========

Because of this, I cannot obtain the output files.

What is hard to understand is that this error occurs only when I set "interNDraws" to 1000 and estimate the model with the full dataset (19860 observations, 4700 individuals).
I didn't encounter the error when I reduced the number of draws (like 10, 100, 250) or the sample size.

What do you think possible causes?

Kind regards,

---
The code is as follows:

Code: Select all

# ################################################################# #
#### LOAD LIBRARY AND DEFINE CORE SETTINGS                       ####
# ################################################################# #

### Clear memory
rm(list = ls())

### Load Apollo library
library(apollo)
library(tidyverse)
library(magrittr)

### Initialise code
apollo_initialise()
modelname  = "test"

### Set core controls
apollo_control = list(
  modelName  = modelname,
  modelDescr ="MXL model",
  indivID    ="no",
  seed = 123,
  mixing = TRUE,
  nCores = parallel::detectCores() - 1
)

# ################################################################# #
#### LOAD DATA AND APPLY ANY TRANSFORMATIONS                     ####
# ################################################################# #

database = read.csv("dataset.csv", header=TRUE)


# ################################################################# #
#### DEFINE MODEL PARAMETERS                                     ####
# ################################################################# #

### Vector of parameters, including any that are kept fixed in estimation
apollo_beta=c(ASC_1	=0,
              ASC_2	=0,
              ASC_3_1	=0,
              ASC_3_2	=0,
              b_1	=0,
              b_2	=0,
              b_3	=0,
              b_4	=0,
              b_5	=0,
              b_6	=0,
              b_7	=0,
              b_8	=0,
              b_9	=-10,
              b_10	=-10,
              b_11	=0,
              b_12	=0,
              b_13	=0,
              b_14	=0,
              b_15	=0,
              b_16	=0,
              b_17	=0,
              b_18	=0,
              b_19	=0,
              b_20	=0,
              b_21	=0,
              b_22	=0,
              b_23	=0,
              b_24	=0,
              b_25	=0,
              b_26	=0,
              b_27	=0,
              b_28	=0,
              b_29	=0,
              b_30	=0,
              b_31	=0,
              b_32	=0,
              b_33	=0,
              b_34	=0,
              b_35	=0,
              b_36	=0,
              b_37	=0,
              b_38	=0,
              b_39	=0,
              b_40	=0,
              b_41	=0,
              v_time_mean	=0,
              v_time_sd	=0,
              v_time_sd2	=0,
              v_time_sd3	=0,
              v_time2_mean	=0,
              v_time2_sd	=0,
              v_time2_sd2	=0,
              v_time2_sd3	=0,
              xi_1_sd = 0.1,
              xi_2_sd = 0.1,
              xi_3_sd = 0.1,
              xi_3_1_sd = 0.1,
              xi_3_2_sd = 0.1,
              xi_3_3_sd = 0.1,
              xi_2_1_sd = 0.1,
              gamma_1 = 0,
              gamma_2 = 0
              )

apollo_draws = list(
  ## inter-personal heterogeneity
  interDrawsType = "sobolOwenFaureTezuka",
  interNDraws = 1000,
  interUnifDraws = c("draws_b_cost"),
  interNormDraws = c("draws_v_time", "draws_v_time2",
                     "draws_xi_1", "draws_xi_2", "draws_xi_3",
                     "draws_xi_3_1", "draws_xi_3_2", "draws_xi_3_3", "draws_xi_2_1"),
  ## intra-personal heterogeneity
  intraDrawsType = "mlhs",
  intraNDraws = 100,
  intraUnifDraws =c(),
  intraNormDraws = c()
  )

### Defining a function that returns random parameters
apollo_randCoeff = function(apollo_beta , apollo_inputs){
  randcoeff = list ()
  randcoeff[["v_time"]] = (v_time_mean + v_time_sd * draws_v_time +
    v_time_sd2 * draws_v_time^2 + v_time_sd3 * draws_v_time^3) * (ref_frequency)^gamma_1
  randcoeff[["v_time2"]] = v_time2_mean +
    v_time2_sd * draws_v_time2 +
    v_time2_sd2 * draws_v_time2^2 +
    v_time2_sd3 * draws_v_time2^3
  randcoeff[["b_cost"]] = -exp(b_9 + (b_10-b_9) * draws_b_cost) * (ref_income)^gamma_2
  randcoeff[["xi_1"]] = xi_1_sd * draws_xi_1
  randcoeff[["xi_2"]] = xi_2_sd * draws_xi_2
  randcoeff[["xi_3"]] = xi_3_sd * draws_xi_3
  randcoeff[["xi_3_1"]] = xi_3_1_sd * draws_xi_3_1
  randcoeff[["xi_3_2"]] = xi_3_2_sd * draws_xi_3_2
  randcoeff[["xi_3_3"]] = xi_3_3_sd * draws_xi_3_3
  randcoeff[["xi_2_1"]] = xi_2_1_sd * draws_xi_2_1
  return(randcoeff)
  }

### 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("b_17", "b_18")

### Read in starting values for at least some parameters from existing model output file
apollo_beta=apollo_readBeta(apollo_beta,
                            apollo_fixed,
                            modelname,
                            overwriteFixed=FALSE)



# ################################################################# #
#### 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()

  # ### Create alternative specific constants and coefficients using interactions with socio-demographics
  V_3 = ASC_3_2 + xi_3 +
    b_cost * v_time2 * time2 +
    b_2 * youth +
    b_4 * elderly +
    b_25 * male +
    b_41 * capital +
    b_8 * s1 +
    b_18 * freq +
    b_27 * free +
    b_39 * teleworker

  V_3_2 = V_3 + b_cost * v_time * time2 + b_cost * (cost_3 + add_cost * holiday2) + (b_19 + xi_2_1) * holiday2
  V_3_3 = V_3 + b_cost * v_time * time3 + b_cost * (cost_3 + add_cost * holiday3) + (b_19 + xi_2_1) * holiday3
  V_3_4 = V_3 + b_cost * v_time * time4 + b_cost * (cost_3 + add_cost * holiday4) + (b_19 + xi_2_1) * holiday4
  V_3_5 = V_3 + b_cost * v_time * time5 + b_cost * (cost_3 + add_cost * holiday5) + (b_19 + xi_2_1) * holiday5
  V_3_6 = V_3 + b_cost * v_time * time6 + b_cost * (cost_3 + add_cost * holiday6) + (b_19 + xi_2_1) * holiday6
  V_3_7 = V_3 + b_cost * v_time * time7 + b_cost * (cost_3 + add_cost * holiday7) + (b_19 + xi_2_1) * holiday7
  V_3_8 = V_3 + b_cost * v_time * time8 + b_cost * (cost_3 + add_cost * holiday8) + (b_19 + xi_2_1) * holiday8

  V_m = ASC_3_1 + xi_3_1 +
    b_5 * alone +
    b_11 * live_child +
    b_13 * couple_double +
    b_15 * live_family +
    b_22 * house +
    b_32 * prof +
    b_34 * public +
    b_36 * self +
    b_30 * parttime +
    b_28 * nojob

  V_n = ASC_3_2 + xi_3_2 +
    b_cost * add_cost_n +
    b_6 * alone +
    b_12 * live_child +
    b_14 * couple_double +
    b_16 * live_family +
    b_23 * house +
    b_33 * prof +
    b_35 * public +
    b_37 * self +
    b_31 * parttime +
    b_29 * nojob

  ### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
  V = list()
  V[['V_1']]	= ASC_1 + xi_1 +
    b_cost * v_time * time1 +
    b_cost * cost1 +
    b_1 * youth +
    b_3 * elderly +
    b_24 * male +
    b_40 * capital +
    b_7 * s1 +
    b_17 * freq +
    b_26 * free +
    b_38 * teleworker

  V[['V_2']]	= b_cost * v_time * time2 + b_cost * cost2 + xi_2

  V[['V_3_2_1']]=V_3_2 + V_m + b_20 * holiday2
  V[['V_3_2_2']]=V_3_2 + xi_3_3
  V[['V_3_2_3']]=V_3_2 + V_n + b_21 * holiday2
  V[['V_3_3_1']]=V_3_3 + V_m + b_20 * holiday3
  V[['V_3_3_2']]=V_3_3 + xi_3_3
  V[['V_3_3_3']]=V_3_3 + V_n + b_21 * holiday3
  V[['V_3_4_1']]=V_3_4 + V_m + b_20 * holiday4
  V[['V_3_4_2']]=V_3_4 + xi_3_3
  V[['V_3_4_3']]=V_3_4 + V_n + b_21 * holiday4
  V[['V_3_5_1']]=V_3_5 + V_m + b_20 * holiday5
  V[['V_3_5_2']]=V_3_5 + xi_3_3
  V[['V_3_5_3']]=V_3_5 + V_n + b_21 * holiday5
  V[['V_3_6_1']]=V_3_6 + V_m + b_20 * holiday6
  V[['V_3_6_2']]=V_3_6 + xi_3_3
  V[['V_3_6_3']]=V_3_6 + V_n + b_21 * holiday6
  V[['V_3_7_1']]=V_3_7 + V_m + b_20 * holiday7
  V[['V_3_7_2']]=V_3_7 + xi_3_3
  V[['V_3_7_3']]=V_3_7 + V_n + b_21 * holiday7
  V[['V_3_8_1']]=V_3_8 + V_m + b_20 * holiday8
  V[['V_3_8_2']]=V_3_8 + xi_3_3
  V[['V_3_8_3']]=V_3_8 + V_n + b_21 * holiday8

  ### Define settings for MNL model component
  mnl_settings = list(
    alternatives = c(V_1	=	1000,
                     V_2	=	2000,
                     V_3_2_1	=	3021,
                     V_3_2_2	=	3022,
                     V_3_2_3	=	3023,
                     V_3_3_1	=	3031,
                     V_3_3_2	=	3032,
                     V_3_3_3	=	3033,
                     V_3_4_1	=	3041,
                     V_3_4_2	=	3042,
                     V_3_4_3	=	3043,
                     V_3_5_1	=	3051,
                     V_3_5_2	=	3052,
                     V_3_5_3	=	3053,
                     V_3_6_1	=	3061,
                     V_3_6_2	=	3062,
                     V_3_6_3	=	3063,
                     V_3_7_1	=	3071,
                     V_3_7_2	=	3072,
                     V_3_7_3	=	3073,
                     V_3_8_1	=	3081,
                     V_3_8_2	=	3082,
                     V_3_8_3	=	3083),
    avail        = list(V_1	=	1,
                        V_2	=	1,
                        V_3_2_1	=	1,
                        V_3_2_2	=	1,
                        V_3_2_3	=	1,
                        V_3_3_1	=	1,
                        V_3_3_2	=	1,
                        V_3_3_3	=	1,
                        V_3_4_1	=	1,
                        V_3_4_2	=	1,
                        V_3_4_3	=	1,
                        V_3_5_1	=	1,
                        V_3_5_2	=	1,
                        V_3_5_3	=	1,
                        V_3_6_1	=	1,
                        V_3_6_2	=	1,
                        V_3_6_3	=	1,
                        V_3_7_1	=	1,
                        V_3_7_2	=	1,
                        V_3_7_3	=	1,
                        V_3_8_1	=	1,
                        V_3_8_2	=	1,
                        V_3_8_3	=	1),
    choiceVar    = Choice,
    V            = V
    )

  ### Compute probabilities using MNL model
  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                                            ####
# ################################################################# #


# apollo_beta = apollo_searchStart(apollo_beta,
#                                  apollo_fixed,
#                                  apollo_probabilities,
#                                  apollo_inputs)#,
#                                  #searchStart_settings)

model = apollo_estimate(apollo_beta,
                        apollo_fixed,
                        apollo_probabilities,
                        apollo_inputs,
                        estimate_settings=list(hessianRoutine="maxLik",maxIterations=300)
                        )

# ################################################################# #
#### MODEL OUTPUTS                                               ####
# ################################################################# #

# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO SCREEN)                               ----
# ----------------------------------------------------------------- #

apollo_modelOutput(model)

# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO FILE, using model name)               ----
# ----------------------------------------------------------------- #

apollo_saveOutput(model)


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

Re: Error in estimating a mixed logit model

Post by dpalma » 12 Nov 2021, 09:57

Hi,

We can't see anything wrong with your code just by looking at it. I would recommend:
  1. First, please install Apollo v0.2.6 following the instructions below depending on you operation system, and try running your model again.
    1. MacOS or Linux: Simply type install.packages("apollo")
    2. Windows: Download the package from the website. Then you can install it using the command install.packages("C:/pathToFile/apollo_0.2.6.zip", repos=NULL), where you need to replace pathToFile by the appropriate path to the file.
  2. If you model still doesn't work with v0.2.6, please share at least part of your data with us, so we can test your model. We'll treat the data confidentially and delete it after we solve the problem. You can email the data to D.Palma [at] leeds.ac.uk
Cheers
David

yukiyama
Posts: 2
Joined: 08 Nov 2021, 02:41

Re: Error in estimating a mixed logit model

Post by yukiyama » 19 Nov 2021, 02:38

Hi David,

Thank you very much for your reply (and sorry for the late response).
We will first try to re-estimate the model with Apollo v0.2.6.
If we continue to have trouble, then we will consider sharing our data.

Thank you and kind regards,

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