vignettes/stats_details.Rmd
stats_details.Rmd
Here a goto summary about statistical test carried out and the returned effect size for each function is provided. This should be useful if one needs to find out more information about how an argument is resolved in the underlying package or if one wishes to browse the source code. So, for example, if you want to know more about how oneway (betweensubjects) ANOVA, you can run ?stats::oneway.test
in your R console.
Abbreviations used: CI = Confidence Interval
two_sample_test
+ oneway_anova
No. of groups: 2
=> two_sample_test
No. of groups: > 2
=> oneway_anova
Following (betweensubjects) tests are carried out for each type of analyses
Type  No. of groups  Test  Function used 

Parametric  > 2  Fisher’s or Welch’s oneway ANOVA  stats::oneway.test 
Nonparametric  > 2  Kruskal–Wallis oneway ANOVA  stats::kruskal.test 
Robust  > 2  Heteroscedastic oneway ANOVA for trimmed means  WRS2::t1way 
Bayes Factor  > 2  Fisher’s ANOVA  BayesFactor::anovaBF 
Parametric  2  Student’s or Welch’s ttest  stats::t.test 
Nonparametric  2  Mann–Whitney U test  stats::wilcox.test 
Robust  2  Yuen’s test for trimmed means  WRS2::yuen 
Bayesian  2  Student’s ttest  BayesFactor::ttestBF 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Type  No. of groups  Effect size  CI?  Function used 

Parametric  > 2  \(\eta_{p}^2\), \(\omega_{p}^2\)  ✅ 
effectsize::omega_squared , effectsize::eta_squared

Nonparametric  > 2  \(\epsilon_{ordinal}^2\)  ✅  effectsize::rank_epsilon_squared 
Robust  > 2  \(\xi\) (Explanatory measure of effect size)  ✅  WRS2::t1way 
Bayes Factor  > 2  \(R_{posterior}^2\)  ✅  performance::r2_bayes 
Parametric  2  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  2  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  2  \(\xi\) (Explanatory measure of effect size)  ✅  WRS2::yuen.effect.ci 
Bayesian  2  \(\delta_{posterior}\)  ✅  bayestestR::describe_posterior 
Following (withinsubjects) tests are carried out for each type of analyses
Type  No. of groups  Test  Function used 

Parametric  > 2  Oneway repeated measures ANOVA  afex::aov_ez 
Nonparametric  > 2  Friedman rank sum test  stats::friedman.test 
Robust  > 2  Heteroscedastic oneway repeated measures ANOVA for trimmed means  WRS2::rmanova 
Bayes Factor  > 2  Oneway repeated measures ANOVA  BayesFactor::anovaBF 
Parametric  2  Student’s ttest  stats::t.test 
Nonparametric  2  Wilcoxon signedrank test  stats::wilcox.test 
Robust  2  Yuen’s test on trimmed means for dependent samples  WRS2::yuend 
Bayesian  2  Student’s ttest  BayesFactor::ttestBF 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Type  No. of groups  Effect size  CI?  Function used 

Parametric  > 2  \(\eta_{p}^2\), \(\omega_{p}^2\)  ✅ 
effectsize::omega_squared , effectsize::eta_squared

Nonparametric  > 2  \(W_{Kendall}\) (Kendall’s coefficient of concordance)  ✅  effectsize::kendalls_w 
Robust  > 2  \(\delta_{Ravg}^{AKP}\)  ✅  AlginaKeselmanPenfield robust standardized difference average 
Bayes Factor  > 2  \(R_{posterior}^2\)  ✅  performance::r2_bayes 
Parametric  2  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  2  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  2  \(\delta_{R}^{AKP}\) (AlginaKeselmanPenfield robust standardized difference)  ✅  WRS2::dep.effect 
Bayesian  2  \(\delta_{posterior}\)  ✅  bayestestR::describe_posterior 
one_sample_test
Following tests are carried out for each type of analyses
Type  Test  Function used 

Parametric  Onesample Student’s ttest  stats::t.test 
Nonparametric  Onesample Wilcoxon test  stats::wilcox.test 
Robust  Bootstrapt method for onesample test 
trimcibt (custom) 
Bayesian  Onesample Student’s ttest  BayesFactor::ttestBF 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Type  Effect size  CI?  Function used 

Parametric  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  trimmed mean  ✅ 
trimcibt (custom) 
Bayes Factor  \(\delta_{posterior}\)  ✅  bayestestR::describe_posterior 
corr_test
Following tests are carried out for each type of analyses. Additionally, the correlation coefficients (and their confidence intervals) are used as effect sizes
Type  Test  CI?  Function used 

Parametric  Pearson’s correlation coefficient  ✅  correlation::correlation 
Nonparametric  Spearman’s rank correlation coefficient  ✅  correlation::correlation 
Robust  Winsorized Pearson correlation coefficient  ✅  correlation::correlation 
Bayesian  Pearson’s correlation coefficient  ✅  correlation::correlation 
contingency_table
Following tests are carried out for each type of analyses
Type of data  Design  Test  Function used 

Unpaired  \(n \times p\) contingency table  Pearson’s \(\chi^2\) test  stats::chisq.test 
Paired  \(n \times p\) contingency table  McNemar’s \(\chi^2\) test  stats::mcnemar.test 
Frequency  \(n \times 1\) contingency table  Goodness of fit (\(\chi^2\) test)  stats::chisq.test 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Test  Effect size  CI?  Function used 

Pearson’s \(\chi^2\) test  Cramer’s \(V\)  ✅  effectsize::cramers_v 
McNemar’s test  Cohen’s \(g\)  ✅  effectsize::cohens_g 
Goodness of fit  Cramer’s \(V\)  ✅  effectsize::cramers_v 
meta_analysis
Type  Test  Effect size  CI?  Function used 

Parametric  Metaanalysis via randomeffects models  \(\beta\)  ✅  metafor::metafor 
Robust  Metaanalysis via robust randomeffects models  \(\beta\)  ✅  metaplus::metaplus 
Bayes  Metaanalysis via Bayesian randomeffects models  \(\beta\)  ✅  metaBMA::meta_random 
See effectsize
’s interpretation functions to check different rules/conventions to interpret effect sizes:
https://easystats.github.io/effectsize/reference/index.html#sectioninterpretation
Although the primary focus of this package is to get expressions containing statistical results, one can also use it to extract dataframes containing these details.
For a more detailed summary of these dataframe: https://indrajeetpatil.github.io/statsExpressions//articles/web_only/dataframe_outputs.html
For parametric and nonparametric effect sizes: https://easystats.github.io/effectsize/articles/simple_htests.html
For robust effect sizes: https://CRAN.Rproject.org/package=WRS2/vignettes/WRS2.pdf
For Bayesian posterior estimates: https://easystats.github.io/bayestestR/articles/bayes_factors.html
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues