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To cite package 'statsExpressions' in publications use:

  Patil, I., (2021). statsExpressions: R Package for Tidy Dataframes
  and Expressions with Statistical Details. Journal of Open Source
  Software, 6(61), 3236, https://doi.org/10.21105/joss.03236

A BibTeX entry for LaTeX users is

  @Article{,
    doi = {10.21105/joss.03236},
    url = {https://doi.org/10.21105/joss.03236},
    year = {2021},
    publisher = {{The Open Journal}},
    volume = {6},
    number = {61},
    pages = {3236},
    author = {Indrajeet Patil},
    title = {{statsExpressions: {R} Package for Tidy Dataframes and Expressions with Statistical Details}},
    journal = {{Journal of Open Source Software}},
  }

Introduction

Here a go-to 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 one-way (between-subjects) ANOVA, you can run ?stats::oneway.test in your R console.

Abbreviations used: CI = Confidence Interval

Summary of functionality

Summary of available analyses

Test Function Lifecycle
one-sample t-test one_sample_test() lifecycle
two-sample t-test two_sample_test() lifecycle
one-way ANOVA oneway_anova() lifecycle
correlation analysis corr_test() lifecycle
contingency table analysis contingency_table() lifecycle
meta-analysis meta_analysis() lifecycle
pairwise comparisons pairwise_comparisons() lifecycle

Summary of details available for analyses

Analysis Hypothesis testing Effect size estimation
(one/two-sample) t-test
one-way ANOVA
correlation
(one/two-way) contingency table
random-effects meta-analysis

Summary of supported statistical approaches

Description Parametric Non-parametric Robust Bayesian
Between group/condition comparisons
Within group/condition comparisons
Distribution of a numeric variable
Correlation between two variables
Association between categorical variables
Equal proportions for categorical variable levels
Random-effects meta-analysis

Summary of tests and effect sizes

Here a go-to 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 one-way (between-subjects) ANOVA, you can run ?stats::oneway.test in your R console.

centrality_description

Type Measure Function used
Parametric mean parameters::describe_distribution()
Non-parametric median parameters::describe_distribution()
Robust trimmed mean parameters::describe_distribution()
Bayesian MAP (maximum a posteriori probability) estimate parameters::describe_distribution()

oneway_anova

between-subjects

Hypothesis testing

Type No. of groups Test Function used
Parametric > 2 Fisher’s or Welch’s one-way ANOVA stats::oneway.test()
Non-parametric > 2 Kruskal-Wallis one-way ANOVA stats::kruskal.test()
Robust > 2 Heteroscedastic one-way ANOVA for trimmed means WRS2::t1way()
Bayes Factor > 2 Fisher’s ANOVA BayesFactor::anovaBF()

Effect size estimation

Type No. of groups Effect size CI available? Function used
Parametric > 2 partial eta-squared, partial omega-squared Yes effectsize::omega_squared(), effectsize::eta_squared()
Non-parametric > 2 rank epsilon squared Yes effectsize::rank_epsilon_squared()
Robust > 2 Explanatory measure of effect size Yes WRS2::t1way()
Bayes Factor > 2 Bayesian R-squared Yes performance::r2_bayes()

within-subjects

Hypothesis testing

Type No. of groups Test Function used
Parametric > 2 One-way repeated measures ANOVA afex::aov_ez()
Non-parametric > 2 Friedman rank sum test stats::friedman.test()
Robust > 2 Heteroscedastic one-way repeated measures ANOVA for trimmed means WRS2::rmanova()
Bayes Factor > 2 One-way repeated measures ANOVA BayesFactor::anovaBF()

Effect size estimation

Type No. of groups Effect size CI available? Function used
Parametric > 2 partial eta-squared, partial omega-squared Yes effectsize::omega_squared(), effectsize::eta_squared()
Non-parametric > 2 Kendall’s coefficient of concordance Yes effectsize::kendalls_w()
Robust > 2 Algina-Keselman-Penfield robust standardized difference average Yes WRS2::wmcpAKP()
Bayes Factor > 2 Bayesian R-squared Yes performance::r2_bayes()

two_sample_test

between-subjects

Hypothesis testing

Type No. of groups Test Function used
Parametric 2 Student’s or Welch’s t-test stats::t.test()
Non-parametric 2 Mann-Whitney U test stats::wilcox.test()
Robust 2 Yuen’s test for trimmed means WRS2::yuen()
Bayesian 2 Student’s t-test BayesFactor::ttestBF()

Effect size estimation

Type No. of groups Effect size CI available? Function used
Parametric 2 Cohen’s d, Hedge’s g Yes effectsize::cohens_d(), effectsize::hedges_g()
Non-parametric 2 r (rank-biserial correlation) Yes effectsize::rank_biserial()
Robust 2 Algina-Keselman-Penfield robust standardized difference Yes WRS2::akp.effect()
Bayesian 2 difference Yes bayestestR::describe_posterior()

within-subjects

Hypothesis testing

Type No. of groups Test Function used
Parametric 2 Student’s t-test stats::t.test()
Non-parametric 2 Wilcoxon signed-rank test stats::wilcox.test()
Robust 2 Yuen’s test on trimmed means for dependent samples WRS2::yuend()
Bayesian 2 Student’s t-test BayesFactor::ttestBF()

Effect size estimation

Type No. of groups Effect size CI available? Function used
Parametric 2 Cohen’s d, Hedge’s g Yes effectsize::cohens_d(), effectsize::hedges_g()
Non-parametric 2 r (rank-biserial correlation) Yes effectsize::rank_biserial()
Robust 2 Algina-Keselman-Penfield robust standardized difference Yes WRS2::wmcpAKP()
Bayesian 2 difference Yes bayestestR::describe_posterior()

one_sample_test

Hypothesis testing

Type Test Function used
Parametric One-sample Student’s t-test stats::t.test
Non-parametric One-sample Wilcoxon test stats::wilcox.test
Robust Bootstrap-t method for one-sample test WRS2::trimcibt
Bayesian One-sample Student’s t-test BayesFactor::ttestBF

Effect size estimation

Type Effect size CI available? Function used
Parametric Cohen’s d, Hedge’s g Yes effectsize::cohens_d(), effectsize::hedges_g()
Non-parametric r (rank-biserial correlation) Yes effectsize::rank_biserial()
Robust trimmed mean Yes WRS2::trimcibt()
Bayes Factor difference Yes bayestestR::describe_posterior()

corr_test

Hypothesis testing and Effect size estimation

Type Test CI available? Function used
Parametric Pearson’s correlation coefficient Yes correlation::correlation()
Non-parametric Spearman’s rank correlation coefficient Yes correlation::correlation()
Robust Winsorized Pearson correlation coefficient Yes correlation::correlation()
Bayesian Bayesian Pearson’s correlation coefficient Yes correlation::correlation()

contingency_table

two-way table

Hypothesis testing

Type Design Test Function used
Parametric/Non-parametric Unpaired Pearson’s chi-squared test stats::chisq.test()
Bayesian Unpaired Bayesian Pearson’s chi-squared test BayesFactor::contingencyTableBF()
Parametric/Non-parametric Paired McNemar’s chi-squared test stats::mcnemar.test()
Bayesian Paired No No

Effect size estimation

Type Design Effect size CI available? Function used
Parametric/Non-parametric Unpaired Cramer’s V Yes effectsize::cramers_v()
Bayesian Unpaired Cramer’s V Yes effectsize::cramers_v()
Parametric/Non-parametric Paired Cohen’s g Yes effectsize::cohens_g()
Bayesian Paired No No No

one-way table

Hypothesis testing

Type Test Function used
Parametric/Non-parametric Goodness of fit chi-squared test stats::chisq.test()
Bayesian Bayesian Goodness of fit chi-squared test (custom)

Effect size estimation

Type Effect size CI available? Function used
Parametric/Non-parametric Pearson’s C Yes effectsize::pearsons_c()
Bayesian No No No

meta_analysis

Hypothesis testing and Effect size estimation

Type Test Effect size CI available? Function used
Parametric Meta-analysis via random-effects models beta Yes metafor::metafor()
Robust Meta-analysis via robust random-effects models beta Yes metaplus::metaplus()
Bayes Meta-analysis via Bayesian random-effects models beta Yes metaBMA::meta_random()

Effect size interpretation

See effectsize’s interpretation functions to check different rules/conventions to interpret effect sizes:

https://easystats.github.io/effectsize/reference/index.html#section-interpretation

Dataframe as output

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

Suggestions

If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/statsExpressions/issues