`tab_model` not working with large `rlmerMod` model with compositional data

[mjakobs]

Hello,

Firstly, thank you for this wonderful package!

I recently ran into an error when trying to use tab_model with an rlmerMod object.

Small reproducible example:

library(compositions)
library(robustlmm)
library(sjPlot)

data(Hydrochem)
cdata <- acomp(Hydrochem[,6:19])

test_model <- rlmer(rnorm(485) ~ ilr(cdata) + (1|Hydrochem$Location))

tab_model(test_model)

Error produced:

Error in `purrr::map2()`:
ℹ In index: 1.
ℹ With name: test_model.
Caused by error in `[.data.frame`:
! undefined columns selected

Session info:

R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/London
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] sjPlot_2.8.14      robustlmm_3.2-0    lme4_1.1-34        Matrix_1.6-0       compositions_2.0-7
[6] ggplot2_3.4.4     

loaded via a namespace (and not attached):
 [1] gtable_0.3.4       tensorA_0.36.2.1   xfun_0.41          bayestestR_0.13.1  htmlwidgets_1.6.2 
 [6] insight_0.19.3     lattice_0.21-8     vctrs_0.6.5        tools_4.3.1        sjstats_0.18.2    
[11] generics_0.1.3     datawizard_0.8.0   rgl_1.2.8          tibble_3.2.1       fansi_1.0.5       
[16] DEoptimR_1.1-1     pkgconfig_2.0.3    ggeffects_1.2.3    lifecycle_1.0.4    compiler_4.3.1    
[21] sjmisc_2.8.9       munsell_0.5.0      fastGHQuad_1.0.1   codetools_0.2-19   carData_3.0-5     
[26] htmltools_0.5.7    yaml_2.3.7         Rttf2pt1_1.3.12    pillar_1.9.0       car_3.1-2         
[31] nloptr_2.0.3       extrafontdb_1.0    tidyr_1.3.0        MASS_7.3-60        boot_1.3-28.1     
[36] abind_1.4-5        nlme_3.1-163       robustbase_0.99-0  tidyselect_1.2.0   sjlabelled_1.2.0  
[41] digest_0.6.33      performance_0.10.4 mvtnorm_1.2-2      dplyr_1.1.2        purrr_1.0.2       
[46] splines_4.3.1      extrafont_0.19     fastmap_1.1.1      grid_4.3.1         colorspace_2.1-0  
[51] cli_3.6.1          magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4         broom_1.0.5       
[56] withr_2.5.2        backports_1.4.1    scales_1.2.1       estimability_1.4.1 modelr_0.1.11     
[61] rmarkdown_2.25     emmeans_1.8.7      evaluate_0.23      knitr_1.45         parameters_0.21.1 
[66] rlang_1.1.2        Rcpp_1.0.11        xtable_1.8-4       glue_1.6.2         bayesm_3.1-6      
[71] effectsize_0.8.5   rstudioapi_0.15.0  minqa_1.2.5        jsonlite_1.8.8     R6_2.5.1

Any help or advice would be much appreciated!