gustavobrp
Reputation: 53
Problem using ggeffects for mclogit package
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 = 60)
#>
#> 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
#> Iteration 21 - deviance = 121.8318 - criterion = 0.001260772
#> Iteration 22 - deviance = 122.4877 - criterion = 0.001360969
#> Iteration 23 - deviance = 121.8393 - criterion = 0.001090899
#> Iteration 24 - deviance = 122.4094 - criterion = 0.001167889
#> Iteration 25 - deviance = 121.8409 - criterion = 0.0009530937
#> Iteration 26 - deviance = 122.3316 - criterion = 0.001012007
#> Iteration 27 - deviance = 121.8404 - criterion = 0.0008401434
#> Iteration 28 - deviance = 122.2572 - criterion = 0.0008848793
#> Iteration 29 - deviance = 121.8386 - criterion = 0.0007464968
#> Iteration 30 - deviance = 122.187 - criterion = 0.000780115
#> Iteration 31 - deviance = 121.8359 - criterion = 0.0006680006
#> Iteration 32 - deviance = 122.1216 - criterion = 0.0006929306
#> Iteration 33 - deviance = 121.8326 - criterion = 0.0006015421
#> Iteration 34 - deviance = 122.0614 - criterion = 0.0006197156
#> Iteration 35 - deviance = 121.8287 - criterion = 0.0005447618
#> Iteration 36 - deviance = 122.0065 - criterion = 0.0005577124
#> Iteration 37 - deviance = 121.8246 - criterion = 0.0004958485
#> Iteration 38 - deviance = 121.9935 - criterion = 0.0005070107
#> Iteration 39 - deviance = 121.8373 - criterion = 0.0004543268
#> Iteration 40 - deviance = 121.9966 - criterion = 0.0004638443
#> Iteration 41 - deviance = 121.8552 - criterion = 0.0004182164
#> Iteration 42 - deviance = 122.0001 - criterion = 0.0004260434
#> Iteration 43 - deviance = 121.8722 - criterion = 0.0003863024
#> Iteration 44 - deviance = 122.0034 - criterion = 0.0003927238
#> Iteration 45 - deviance = 121.8882 - criterion = 0.0003579502
#> Iteration 46 - deviance = 122.0066 - criterion = 0.0003632011
#> Iteration 47 - deviance = 121.9034 - criterion = 0.0003326424
#> Iteration 48 - deviance = 122.0097 - criterion = 0.0003369169
#> Iteration 49 - deviance = 121.9179 - criterion = 0.0003099521
#> Iteration 50 - deviance = 122.0127 - criterion = 0.0003134115
#> Iteration 51 - deviance = 121.9316 - criterion = 0.000289525
#> Iteration 52 - deviance = 122.0156 - criterion = 0.0002923043
#> Iteration 53 - deviance = 121.9446 - criterion = 0.0002710655
#> Iteration 54 - deviance = 122.0183 - criterion = 0.0002732776
#> Iteration 55 - deviance = 121.9569 - criterion = 0.0002543249
#> Iteration 56 - deviance = 122.0209 - criterion = 0.000256065
#> Iteration 57 - deviance = 121.9685 - criterion = 0.0002390931
#> Iteration 58 - deviance = 122.0234 - criterion = 0.0002404415
#> Iteration 59 - deviance = 121.9794 - criterion = 0.0002251913
#> Iteration 60 - deviance = 122.0257 - criterion = 0.0002262159
#> 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 infosessioninfo::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 -------------------------------------------------------------------
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#>
#> ------------------------------------------------------------------------------
Upvotes: 0
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