Site icon R-bloggers

A table of results for Bayesian mixed-effects models: Grouping variables and specifying random slopes

R on Pablo Bernabeu
[This article was first published on R on Pablo Bernabeu, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

  • Here I share the format applied to tables presenting the results of Bayesian models in Bernabeu (2022; the table for frequentist models is covered in this other post). The sample table presents a Bayesian mixed-effects model that was fitted using the R package brms (Bürkner et al., 2022). The mixed effects were driven by the maximal principle (Brauer & Curtin, 2018). The format of the table resembles one of the examples published by the American Psychological Association. However, there are also deviations from those examples. For instance, in the present table, the effects are grouped under informative labels to facilitate the readers’ comprehension, using the kableExtra package (Zhu, 2022). Furthermore, the random slopes are specified using superscript letters and a footnote. The table can be reproduced using the materials at https://osf.io/gt5uf.

    Loading packages and the results of the models

    library(dplyr)
library(knitr)
library(tibble)
library(stringr)
library(lmerTest)
library(kableExtra)

# Load Bayesian results summary

semanticpriming_summary_weaklyinformativepriors_exgaussian = 
  readRDS(gzcon(url('https://github.com/pablobernabeu/language-sensorimotor-simulation-PhD-thesis/blob/main/semanticpriming/bayesian_analysis/results/semanticpriming_summary_weaklyinformativepriors_exgaussian.rds?raw=true')))

    Adjusting the names of the effects

    First, to facilitate the understanding of the results, the original names of the effects will be adjusted in the brms summary.

    # Rename effects in plain language and specify the random slopes
# (if any) for each effect, in the footnote. For this purpose, 
# superscripts are added to the names of the appropriate effects.
# 
# In the interactions below, word-level variables are presented 
# first for the sake of consistency (the order does not affect 
# the results in any way). Also in the interactions, double 
# colons are used to inform the 'bayesian_model_table' 
# function that the two terms in the interaction must be split 
# into two lines.

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_attentional_control'] = 'Attentional control'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_attentional_control'] = 'Attentional control'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_vocabulary_size'] = 'Vocabulary size <sup>a</sup>'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_recoded_participant_gender'] = 'Gender <sup>a</sup>'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_target_word_frequency'] = 'Word frequency'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_target_number_syllables'] = 'Number of syllables'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_word_concreteness_diff'] = 'Word-concreteness difference'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_cosine_similarity'] = 'Language-based similarity <sup>b</sup>'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_visual_rating_diff'] = 'Visual-strength difference <sup>b</sup>'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_recoded_interstimulus_interval'] = 'Stimulus onset asynchrony (SOA) <sup>b</sup>'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_word_concreteness_diff:z_vocabulary_size'] = 
  'Word-concreteness difference :: Vocabulary size'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_word_concreteness_diff:z_recoded_interstimulus_interval'] = 
  'Word-concreteness difference : SOA'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_word_concreteness_diff:z_recoded_participant_gender'] = 
  'Word-concreteness difference : Gender'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_attentional_control:z_cosine_similarity'] = 
  'Language-based similarity :: Attentional control'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_attentional_control:z_visual_rating_diff'] = 
  'Visual-strength difference :: Attentional control'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_vocabulary_size:z_cosine_similarity'] = 
  'Language-based similarity :: Vocabulary size'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_vocabulary_size:z_visual_rating_diff'] = 
  'Visual-strength difference :: Vocabulary size'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_recoded_participant_gender:z_cosine_similarity'] = 
  'Language-based similarity : Gender'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_recoded_participant_gender:z_visual_rating_diff'] = 
  'Visual-strength difference : Gender'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_cosine_similarity:z_recoded_interstimulus_interval'] = 
  'Language-based similarity : SOA <sup>b</sup>'

rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed)[
  rownames(semanticpriming_summary_weaklyinformativepriors_exgaussian$fixed) == 
    'z_visual_rating_diff:z_recoded_interstimulus_interval'] = 
  'Visual-strength difference : SOA <sup>b</sup>'

    bayesian_model_table()

    This custom function was used in Bernabeu (2022), with a PDF output. In the current scenario, however, we have an HTML output. In the above function, the code used for the \(\widehat{R}\) tailored to the HTML output (&Rcirc;) does not render properly.

    • Session info

    If you encounter any blockers while reproducing the table using the materials at https://osf.io/gt5uf, my current session info may be useful.

    sessionInfo()
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] kableExtra_1.3.4    lmerTest_3.1-3      lme4_1.1-31        
## [4] Matrix_1.5-1        stringr_1.5.0       tibble_3.1.8       
## [7] dplyr_1.0.10        knitr_1.41          xaringanExtra_0.7.0
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.0    xfun_0.36           bslib_0.4.2        
##  [4] splines_4.2.2       lattice_0.20-45     colorspace_2.0-3   
##  [7] vctrs_0.5.1         generics_0.1.3      viridisLite_0.4.1  
## [10] htmltools_0.5.4     yaml_2.3.6          utf8_1.2.2         
## [13] rlang_1.0.6         jquerylib_0.1.4     pillar_1.8.1       
## [16] nloptr_2.0.3        withr_2.5.0         glue_1.6.2         
## [19] DBI_1.1.3           uuid_1.1-0          lifecycle_1.0.3    
## [22] munsell_0.5.0       blogdown_1.16       gtable_0.3.1       
## [25] rvest_1.0.3         evaluate_0.19       fastmap_1.1.0      
## [28] fansi_1.0.3         Rcpp_1.0.9          scales_1.2.1       
## [31] cachem_1.0.6        webshot_0.5.4       jsonlite_1.8.4     
## [34] systems_1.0.4   ggplot2_3.3.5       digest_0.6.31      
## [37] stringi_1.7.8       bookdown_0.31       numDeriv_2016.8-1.1
## [40] grid_4.2.2          cli_3.4.1           tools_4.2.2        
## [43] magrittr_2.0.3      sass_0.4.4          pkgconfig_2.0.3    
## [46] MASS_7.3-58.1       xml2_1.3.3          svglite_2.1.0      
## [49] httr_1.4.4          assertthat_0.2.1    minqa_1.2.5        
## [52] rmarkdown_2.19      rstudioapi_0.14     R6_2.5.1           
## [55] boot_1.3-28         nlme_3.1-160        compiler_4.2.2

    References

    Bernabeu, P. (2022). Language and sensorimotor simulation in conceptual processing: Multilevel analysis and statistical power. Lancaster University. https://doi.org/10.17635/lancaster/thesis/1795

    Brauer, M., & Curtin, J. J. (2018). Linear mixed-effects models and the analysis of nonindependent data: A unified framework to analyze categorical and continuous independent variables that vary within-subjects and/or within-items. Psychological Methods, 23(3), 389–411. https://doi.org/10.1037/met0000159

    Bürkner, P.-C., Gabry, J., Weber, S., Johnson, A., Modrak, M., Badr, H. S., Weber, F., Ben-Shachar, M. S., & Rabel, H. (2022). Package ’brms’. CRAN. https://cran.r-project.org/web/packages/brms/brms.pdf

    Zhu, H. (2022). Package ’kableExtra’. CRAN. https://cran.r-project.org/web/packages/kableExtra/kableExtra.pdf

    To leave a comment for the author, please follow the link and comment on their blog: R on Pablo Bernabeu.
    R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
    Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.