Problem using ggeffects for mclogit package

[gustavobrp]

Currently trying to use ggeffects for a mmblogit object from the mclogit package, but the following messages are being shown:

> ggeffects::ggeffect(model.example)
Can't compute marginal effects, 'effects::Effect()' returned an error.

Reason: 'arg' should be one of “PQL”, “MQL”
You may try 'ggpredict()' or 'ggemmeans()'.

Can't compute marginal effects, 'effects::Effect()' returned an error.

Reason: 'arg' should be one of “PQL”, “MQL”
You may try 'ggpredict()' or 'ggemmeans()'.

NULL
> ggeffects::ggpredict(model.example)
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : 
  contrasts can be applied only to factors with 2 or more levels
> ggeffects::ggemmeans(model.example)
Error: `terms` needs to be a character vector with at least one predictor name: one term used for the
  x-axis, more optional terms as grouping factors.

Here is a reprex from the database and the model. I'm using the uptaded versions of both packages. Note: the model converge in the complete dataset and model, and this happens using PQL or MQL method.

library(tidyverse)
#> Warning: package 'tidyverse' was built under R version 4.1.3
#> Warning: package 'ggplot2' was built under R version 4.1.3
#> Warning: package 'tibble' was built under R version 4.1.3
#> Warning: package 'tidyr' was built under R version 4.1.3
#> Warning: package 'readr' was built under R version 4.1.3
#> Warning: package 'purrr' was built under R version 4.1.3
#> Warning: package 'dplyr' was built under R version 4.1.3
#> Warning: package 'stringr' was built under R version 4.1.3
#> Warning: package 'forcats' was built under R version 4.1.3
#> Warning: package 'lubridate' was built under R version 4.1.3
library(mclogit)
#> Loading required package: Matrix
#> Warning: package 'Matrix' was built under R version 4.1.3
#> 
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#> 
#>     expand, pack, unpack
library(ggeffects)

db.example <- structure(list(dep_resultado_academico = structure(c(3L, 2L, 
                                                                   1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 3L, 1L, 1L, 
                                                                   3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 2L, 
                                                                   3L, 2L, 2L, 2L, 3L, 1L), .Label = c("Cursando", "Graduado", "Evasão"
                                                                   ), class = "factor"), faixa_idade = structure(c(3L, 2L, 2L, 3L, 
                                                                                                                   1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                                                                                   2L, 1L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 
                                                                                                                   2L, 2L, 1L, 1L), .Label = c("Até 18 anos", "Entre 19 e 24 anos", 
                                                                                                                                               "Entre 25 e 29 anos", "30 anos ou mais"), class = "factor"), 
                             SEXO = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 
                                                1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L
                             ), .Label = c("Masculino", "Feminino"), class = "factor"), 
                             CURSO_ATUAL = structure(c(1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 4L, 5L, 5L, 5L, 5L, 6L, 
                                                       6L, 6L, 7L, 8L, 4L, 9L, 5L, 5L, 5L, 1L, 1L, 1L, 10L, 10L, 
                                                       9L, 9L, 9L), .Label = c("Letras", "Medicina", "Engenharia Química", 
                                                                               "Pedagogia", "Direito", "Enfermagem", "Engenharia Civil", 
                                                                               "Engenharia Mecânica", "Psicologia", "Geografia", "Odontologia", 
                                                                               "Educação Física", "Administração", "Engenharia Elétrica", 
                                                                               "Geologia", "Ciências Biológicas", "Comunicação Social", 
                                                                               "Arquitetura e Urbanismo", "Engenharia de Produção", "Artes Visuais", 
                                                                               "Biblioteconomia", "História", "Farmácia", "Filosofia", "Medicina Veterinária", 
                                                                               "Matemática", "Ciências Contábeis", "Engenharia de Minas", 
                                                                               "Química", "Física", "Ciências Sociais", "Engenharia de Controle e Automação", 
                                                                               "Ciências Econômicas", "Engenharia Metalúrgica", "Fisioterapia", 
                                                                               "Terapia Ocupacional", "Fonoaudiologia", "Turismo", "Nutrição", 
                                                                               "Ciência da Computação", "Ciências Atuariais", "Estatística", 
                                                                               "Sistemas de Informação"), class = "factor")), row.names = c(NA, 
                                                                                                                                            -40L), class = c("tbl_df", "tbl", "data.frame"))

model.example <- mblogit(dep_resultado_academico ~ faixa_idade + SEXO, 
                 data = db.example,
                 random = c(~1|CURSO_ATUAL),
                 method = "MQL",
                 estimator = "REML",
                 maxit = 20)
#> 
#> Iteration 1 - deviance = 106.9198 - criterion = 1.029838
#> Iteration 2 - deviance = 117.4668 - criterion = 0.1434256
#> Iteration 3 - deviance = 120.2036 - criterion = 0.08122211
#> Iteration 4 - deviance = 119.9487 - criterion = 0.05115243
#> Iteration 5 - deviance = 122.9978 - criterion = 0.04380277
#> Iteration 6 - deviance = 120.1087 - criterion = 0.02562604
#> Iteration 7 - deviance = 122.7511 - criterion = 0.02329051
#> Iteration 8 - deviance = 120.0579 - criterion = 0.01496665
#> Iteration 9 - deviance = 122.5763 - criterion = 0.01425761
#> Iteration 10 - deviance = 120.0299 - criterion = 0.009791495
#> Iteration 11 - deviance = 122.4501 - criterion = 0.009594708
#> Iteration 12 - deviance = 120.0111 - criterion = 0.006903091
#> Iteration 13 - deviance = 122.4977 - criterion = 0.007190019
#> Iteration 14 - deviance = 120.0471 - criterion = 0.005561767
#> Iteration 15 - deviance = 120.4887 - criterion = 0.001793266
#> Iteration 16 - deviance = 122.1066 - criterion = 0.002193508
#> Iteration 17 - deviance = 121.6214 - criterion = 0.001708503
#> Iteration 18 - deviance = 122.5464 - criterion = 0.001892841
#> Iteration 19 - deviance = 121.7983 - criterion = 0.001469536
#> Iteration 20 - deviance = 122.5543 - criterion = 0.001601244
#> Warning: Algorithm did not converge
#> Warning: Fitted probabilities numerically 0 occurred

ggeffects::ggeffect(model.example)
#> Can't compute marginal effects, 'effects::Effect()' returned an error.
#> 
#> Reason: 'arg' should be one of "PQL", "MQL"
#> You may try 'ggpredict()' or 'ggemmeans()'.
#> 
#> Can't compute marginal effects, 'effects::Effect()' returned an error.
#> 
#> Reason: 'arg' should be one of "PQL", "MQL"
#> You may try 'ggpredict()' or 'ggemmeans()'.
#> NULL

ggeffects::ggpredict(model.example)
#> Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels

ggeffects::ggemmeans(model.example)
#> Error: `terms` needs to be a character vector with at least one predictor name:
#>   one term used for the x-axis, more optional terms as grouping factors.

^{Created on 2023-07-27 with reprex v2.0.2}

Session info

``` r sessioninfo::session_info() #> - Session info --------------------------------------------------------------- #> setting value #> version R version 4.1.1 (2021-08-10) #> os Windows 10 x64 (build 22621) #> system x86_64, mingw32 #> ui RTerm #> language (EN) #> collate Portuguese_Brazil.1252 #> ctype Portuguese_Brazil.1252 #> tz America/Sao_Paulo #> date 2023-07-27 #> pandoc 2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown) #> #> - Packages ------------------------------------------------------------------- #> package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.1.1) #> boot 1.3-28 2021-05-03 [2] CRAN (R 4.1.1) #> car 3.1-1 2022-10-19 [1] CRAN (R 4.1.1) #> carData 3.0-5 2022-01-06 [1] CRAN (R 4.1.3) #> cli 3.6.1 2023-03-23 [1] CRAN (R 4.1.3) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.1.1) #> codetools 0.2-18 2020-11-04 [2] CRAN (R 4.1.1) #> colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.1.3) #> data.table 1.14.8 2023-02-17 [1] CRAN (R 4.1.3) #> DBI 1.1.3 2022-06-18 [1] CRAN (R 4.1.3) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.1.3) #> dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.1.3) #> effects 4.2-2 2022-07-13 [1] CRAN (R 4.1.3) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.1.0) #> emmeans 1.8.1-1 2022-09-08 [1] CRAN (R 4.1.3) #> estimability 1.4.1 2022-08-05 [1] CRAN (R 4.1.3) #> evaluate 0.17 2022-10-07 [1] CRAN (R 4.1.3) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.1.3) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.1.0) #> forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.1.3) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.1.3) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.1.3) #> ggeffects * 1.2.3 2023-06-11 [1] CRAN (R 4.1.1) #> ggplot2 * 3.4.2 2023-04-03 [1] CRAN (R 4.1.3) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.1.3) #> gtable 0.3.1 2022-09-01 [1] CRAN (R 4.1.3) #> highr 0.9 2021-04-16 [1] CRAN (R 4.1.0) #> hms 1.1.2 2022-08-19 [1] CRAN (R 4.1.3) #> htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.1.3) #> insight 0.19.3 2023-06-29 [1] CRAN (R 4.1.1) #> jsonlite 1.8.4 2022-12-06 [1] CRAN (R 4.1.3) #> knitr 1.40 2022-08-24 [1] CRAN (R 4.1.3) #> lattice 0.20-44 2021-05-02 [2] CRAN (R 4.1.1) #> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.1.3) #> lme4 1.1-30 2022-07-08 [1] CRAN (R 4.1.3) #> lubridate * 1.9.2 2023-02-10 [1] CRAN (R 4.1.3) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.1.3) #> MASS 7.3-54 2021-05-03 [2] CRAN (R 4.1.1) #> Matrix * 1.5-1 2022-09-13 [1] CRAN (R 4.1.3) #> mclogit * 0.9.4.2 2023-07-26 [1] local #> memisc 0.99.31.6 2023-03-10 [1] CRAN (R 4.1.3) #> minqa 1.2.4 2014-10-09 [1] CRAN (R 4.1.3) #> mitools 2.4 2019-04-26 [1] CRAN (R 4.1.2) #> multcomp 1.4-22 2023-02-10 [1] CRAN (R 4.1.3) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0) #> mvtnorm 1.1-3 2021-10-08 [1] CRAN (R 4.1.1) #> nlme 3.1-152 2021-02-04 [2] CRAN (R 4.1.1) #> nloptr 2.0.3 2022-05-26 [1] CRAN (R 4.1.3) #> nnet 7.3-18 2022-09-28 [1] CRAN (R 4.1.3) #> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.1.3) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0) #> purrr * 1.0.1 2023-01-10 [1] CRAN (R 4.1.3) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.1.3) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.1.3) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.1.3) #> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.1.3) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.1) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.1.3) #> readr * 2.1.4 2023-02-10 [1] CRAN (R 4.1.3) #> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.1.3) #> rlang 1.1.0 2023-03-14 [1] CRAN (R 4.1.3) #> rmarkdown 2.17 2022-10-07 [1] CRAN (R 4.1.3) #> rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.1.3) #> sandwich 3.0-2 2022-06-15 [1] CRAN (R 4.1.3) #> scales 1.2.1 2022-08-20 [1] CRAN (R 4.1.3) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.3) #> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.2) #> stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.1.3) #> styler 1.10.1 2023-06-05 [1] CRAN (R 4.1.1) #> survey 4.1-1 2021-07-19 [1] CRAN (R 4.1.3) #> survival 3.4-0 2022-08-09 [1] CRAN (R 4.1.3) #> TH.data 1.1-1 2022-04-26 [1] CRAN (R 4.1.3) #> tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.1.3) #> tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.1.3) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.1.1) #> tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.1.3) #> timechange 0.2.0 2023-01-11 [1] CRAN (R 4.1.3) #> tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.1.3) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0) #> vctrs 0.6.1 2023-03-22 [1] CRAN (R 4.1.3) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.3) #> xfun 0.33 2022-09-12 [1] CRAN (R 4.1.3) #> xtable 1.8-4 2019-04-21 [1] CRAN (R 4.1.0) #> yaml 2.3.7 2023-01-23 [1] CRAN (R 4.1.3) #> zoo 1.8-11 2022-09-17 [1] CRAN (R 4.1.3) #> #> #> ------------------------------------------------------------------------------ ```

[strengejacke]

I cannot reproduce your example, the model gives an error:

library(mclogit)
#> Loading required package: Matrix
library(ggeffects)

db.example <- structure(list(dep_resultado_academico = structure(c(3L, 2L, 
                                                                   1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 3L, 1L, 1L, 
                                                                   3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 2L, 
                                                                   3L, 2L, 2L, 2L, 3L, 1L), .Label = c("Cursando", "Graduado", "Evasão"
                                                                   ), class = "factor"), faixa_idade = structure(c(3L, 2L, 2L, 3L, 
                                                                                                                   1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                                                                                   2L, 1L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 
                                                                                                                   2L, 2L, 1L, 1L), .Label = c("Até 18 anos", "Entre 19 e 24 anos", 
                                                                                                                                               "Entre 25 e 29 anos", "30 anos ou mais"), class = "factor"), 
                             SEXO = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 
                                                1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L
                             ), .Label = c("Masculino", "Feminino"), class = "factor"), 
                             CURSO_ATUAL = structure(c(1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 4L, 5L, 5L, 5L, 5L, 6L, 
                                                       6L, 6L, 7L, 8L, 4L, 9L, 5L, 5L, 5L, 1L, 1L, 1L, 10L, 10L, 
                                                       9L, 9L, 9L), .Label = c("Letras", "Medicina", "Engenharia Química", 
                                                                               "Pedagogia", "Direito", "Enfermagem", "Engenharia Civil", 
                                                                               "Engenharia Mecânica", "Psicologia", "Geografia", "Odontologia", 
                                                                               "Educação Física", "Administração", "Engenharia Elétrica", 
                                                                               "Geologia", "Ciências Biológicas", "Comunicação Social", 
                                                                               "Arquitetura e Urbanismo", "Engenharia de Produção", "Artes Visuais", 
                                                                               "Biblioteconomia", "História", "Farmácia", "Filosofia", "Medicina Veterinária", 
                                                                               "Matemática", "Ciências Contábeis", "Engenharia de Minas", 
                                                                               "Química", "Física", "Ciências Sociais", "Engenharia de Controle e Automação", 
                                                                               "Ciências Econômicas", "Engenharia Metalúrgica", "Fisioterapia", 
                                                                               "Terapia Ocupacional", "Fonoaudiologia", "Turismo", "Nutrição", 
                                                                               "Ciência da Computação", "Ciências Atuariais", "Estatística", 
                                                                               "Sistemas de Informação"), class = "factor")), row.names = c(NA, 
                                                                                                                                            -40L), class = c("tbl_df", "tbl", "data.frame"))

model.example <- mblogit(dep_resultado_academico ~ faixa_idade + SEXO, 
                 data = db.example,
                 random = c(~1|CURSO_ATUAL),
                 method = "MQL",
                 estimator = "REML",
                 maxit = 20)
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 1 - deviance = 109.3839 - criterion = 1.033022
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 2 - deviance = 120.908 - criterion = 0.1304513
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 3 - deviance = 119.6012 - criterion = 0.04783325
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 4 - deviance = 119.6994 - criterion = 0.03275368
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 5 - deviance = 119.7083 - criterion = 0.02436854
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 6 - deviance = 119.6681 - criterion = 0.01883837
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 7 - deviance = 119.684 - criterion = 0.01487719
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 8 - deviance = 119.6689 - criterion = 0.0120209
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 9 - deviance = 119.6765 - criterion = 0.009879401
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 10 - deviance = 119.6706 - criterion = 0.008252256
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 11 - deviance = 119.674 - criterion = 0.006984561
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 12 - deviance = 119.6716 - criterion = 0.005983284
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 13 - deviance = 119.6731 - criterion = 0.005178449
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 14 - deviance = 119.6721 - criterion = 0.004523611
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 15 - deviance = 119.6728 - criterion = 0.003981716
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 16 - deviance = 119.6723 - criterion = 0.003526526
#> Error in .solve.checkCond(a, tol): 'a' is computationally singular, rcond(a)=1.0131e-16

summary(model.example)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'model.example' not found

^{Created on 2023-08-30 with reprex v2.0.2}

[gustavobrp]

Hi Daniel.

I tested again here and the model worked, strange. What can I do to help?

I'm using the lastest version of mclogit() and ggeffects().

mclogit() gives the warning that the algorithm did not converge here, but it works, and when I try to use ggeffects() the problem persists.

library(mclogit)
#> Loading required package: Matrix
library(ggeffects)

db.example <- structure(list(dep_resultado_academico = structure(c(3L, 2L, 
                                                                   1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 3L, 1L, 1L, 
                                                                   3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 2L, 
                                                                   3L, 2L, 2L, 2L, 3L, 1L), .Label = c("Cursando", "Graduado", "Evasão"
                                                                   ), class = "factor"), faixa_idade = structure(c(3L, 2L, 2L, 3L, 
                                                                                                                   1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                                                                                   2L, 1L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 
                                                                                                                   2L, 2L, 1L, 1L), .Label = c("Até 18 anos", "Entre 19 e 24 anos", 
                                                                                                                                               "Entre 25 e 29 anos", "30 anos ou mais"), class = "factor"), 
                             SEXO = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 
                                                1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L
                             ), .Label = c("Masculino", "Feminino"), class = "factor"), 
                             CURSO_ATUAL = structure(c(1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                                       2L, 1L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 4L, 5L, 5L, 5L, 5L, 6L, 
                                                       6L, 6L, 7L, 8L, 4L, 9L, 5L, 5L, 5L, 1L, 1L, 1L, 10L, 10L, 
                                                       9L, 9L, 9L), .Label = c("Letras", "Medicina", "Engenharia Química", 
                                                                               "Pedagogia", "Direito", "Enfermagem", "Engenharia Civil", 
                                                                               "Engenharia Mecânica", "Psicologia", "Geografia", "Odontologia", 
                                                                               "Educação Física", "Administração", "Engenharia Elétrica", 
                                                                               "Geologia", "Ciências Biológicas", "Comunicação Social", 
                                                                               "Arquitetura e Urbanismo", "Engenharia de Produção", "Artes Visuais", 
                                                                               "Biblioteconomia", "História", "Farmácia", "Filosofia", "Medicina Veterinária", 
                                                                               "Matemática", "Ciências Contábeis", "Engenharia de Minas", 
                                                                               "Química", "Física", "Ciências Sociais", "Engenharia de Controle e Automação", 
                                                                               "Ciências Econômicas", "Engenharia Metalúrgica", "Fisioterapia", 
                                                                               "Terapia Ocupacional", "Fonoaudiologia", "Turismo", "Nutrição", 
                                                                               "Ciência da Computação", "Ciências Atuariais", "Estatística", 
                                                                               "Sistemas de Informação"), class = "factor")), row.names = c(NA, 
                                                                                                                                            -40L), class = c("tbl_df", "tbl", "data.frame"))

model.example <- mblogit(dep_resultado_academico ~ faixa_idade + SEXO, 
                         data = db.example,
                         random = c(~1|CURSO_ATUAL),
                         method = "MQL",
                         estimator = "REML",
                         maxit = 20)
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 1 - deviance = 109.3839 - criterion = 1.033022
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 2 - deviance = 120.908 - criterion = 0.1304513
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 3 - deviance = 119.6012 - criterion = 0.04783325
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 4 - deviance = 119.6994 - criterion = 0.03275368
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 5 - deviance = 119.7083 - criterion = 0.02436854
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 6 - deviance = 119.6681 - criterion = 0.01883837
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 7 - deviance = 119.684 - criterion = 0.01487719
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 8 - deviance = 119.6689 - criterion = 0.0120209
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 9 - deviance = 119.6765 - criterion = 0.009879401
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 10 - deviance = 119.6706 - criterion = 0.008252256
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 11 - deviance = 119.674 - criterion = 0.006984559
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 12 - deviance = 119.6716 - criterion = 0.005983286
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 13 - deviance = 119.6731 - criterion = 0.005178275
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 14 - deviance = 119.6721 - criterion = 0.004523205
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 15 - deviance = 119.6728 - criterion = 0.003982371
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 16 - deviance = 119.6723 - criterion = 0.003511752
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 17 - deviance = 119.6726 - criterion = 0.003164549
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 18 - deviance = 119.6724 - criterion = 0.002484265
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 19 - deviance = 119.6725 - criterion = 0.001902648
#> Warning: Inner iterations did not coverge - nlminb message: false convergence
#> (8)
#> 
#> Iteration 20 - deviance = 119.6725 - criterion = 0.0006252818
#> Warning: Algorithm did not converge
#> Warning: Fitted probabilities numerically 0 or 1 occurred

ggeffect(model.example)
#> Can't compute marginal effects, `effects::Effect()` returned an error.
#> 
#> Reason: unused argument (qr = TRUE)
#> You may try `ggpredict()` or `ggemmeans()`.
#> 
#> Can't compute marginal effects, `effects::Effect()` returned an error.
#> 
#> Reason: unused argument (qr = TRUE)
#> You may try `ggpredict()` or `ggemmeans()`.
#> NULL

Created on 2023-08-30 with [reprex v2.0.2](https://reprex.tidyverse.org/)

[strengejacke]

Can you save the model object (in RData or RDS format), zip it and attach it to this issue?