Package index • colleyRstats

Session setup

Configure a session once, at the top of a script. See vignette("getting-started") for where the call belongs.

colleyRstats_setup(): Configure Global R Environment for colleyRstats
colley_theme(): The colleyRstats ggplot2 theme

One-call pipelines

Go from a data frame to a figure and manuscript-ready sentences in a single call, for one dependent variable or many.

analyze_and_report(): Analyze one dependent variable and produce everything a paper needs
report_all(): Analyze and report several dependent variables at once
emit_overleaf(): Bundle an analysis into an Overleaf-ready folder

Choosing a test

Inspect the data and get the matching model, with a fit call and a methods sentence you can edit.

recommend_test() recommend_analysis(): Recommend a principled analysis for one outcome
classify_outcome(): Classify the measurement scale of an outcome variable
assumption_methods_text(): Methods-section sentence justifying the test selection
cite_methods(): Citations and methods boilerplate for the analyses used

Checking assumptions

checkAssumptionsForAnova() check_assumptions_anova(): Check the assumptions for an ANOVA with a variable number of factors: Normality and Homogeneity of variance assumption.
check_normality_by_group(): Check normality for groups
check_homogeneity_by_group(): Check homogeneity of variances across groups
debug_contr_error(): Debug contrast errors in ANOVA-like models

Plots

ggstatsplot wrappers that pick the parametric or non-parametric test for you, effect plots, and multi-objective optimisation plots.

ggbetweenstatsWithPriorNormalityCheck() plot_between_stats(): Check the data's distribution. If non-normal, take the non-parametric variant of ggbetweenstats. x and y have to be in parentheses, e.g., "ConditionID".
ggbetweenstatsWithPriorNormalityCheckAsterisk() plot_between_stats_asterisk(): Check the data's distribution. If non-normal, take the non-parametric variant of ggbetweenstats. x and y have to be in parentheses, e.g., "ConditionID".
ggwithinstatsWithPriorNormalityCheck() plot_within_stats(): Check the data's distribution. If non-normal, take the non-parametric variant of ggwithinstats. x and y have to be in parentheses, e.g., "ConditionID".
ggwithinstatsWithPriorNormalityCheckAsterisk() plot_within_stats_asterisk(): Check the data's distribution. If non-normal, take the non-parametric variant of ggwithinstats. x and y have to be in parentheses, e.g., "ConditionID". Add Asterisks instead of p-values.
generateEffectPlot() plot_effect(): Function to define a plot, either showing the main or interaction effect in bold.
generateMoboPlot() plot_mobo(): Generate a Multi-objective Optimization Plot
generateMoboPlot2() plot_mobo2(): Generate a Multi-objective Optimization Plot
stat_sum_df(): Generating the sum and adding a crossbar.
n_fun(): Build a median/size label for plot annotations

Saving figures

Publication presets that size a figure’s type to the width it is written at.

save_paper_figure(): Save a plot with publication-ready defaults
figure_base_size(): Base font size for a figure of a given width

Reporting results

APA-compliant, LaTeX-ready sentences and tables for each model family.

reportART() report_art(): Generate the Latex-text based on the ARTool (see https://github.com/mjskay/ARTool). The ART result must be piped into an anova(). Only significant main and interaction effects are reported. P-values are rounded for the third digit. Attention: Effect sizes are not calculated! Attention: the independent variables of the formula and the term specifying the participant must be factors (i.e., use as.factor()).
reportArtCon() report_art_con(): Report significant ART contrasts (art.con) as LaTeX text
reportArtConTable() report_art_con_table(): Report ART contrasts (art.con) as a LaTeX table. Customizable with sensible defaults. Companion to reportDunnTestTable().
reportCLMM() report_clmm(): Report a cumulative link (mixed) model in LaTeX/APA style
reportGLMM() report_glmm(): Report a (generalized) linear mixed model in LaTeX/APA style
reportDunnTest() report_dunn_test(): Report dunnTest as text. Required commands in LaTeX: \newcommand{\padjminor}{\textit{p$_{adj}<$}} \newcommand{\padj}{\textit{p$_{adj}$=}} \newcommand{\rankbiserial}[1]{$r_{rb} = #1$}
reportDunnTestTable() report_dunn_test_table(): report Dunn test as a table. Customizable with sensible defaults. Required commands in LaTeX: \newcommand{\padjminor}{\textit{p$_{adj}<$}} \newcommand{\padj}{\textit{p$_{adj}$=}} \newcommand{\rankbiserial}[1]{$r_{rb} = #1$}
reportggstatsplot() report_ggstatsplot(): Report statistical details for ggstatsplot.
reportggstatsplotPostHoc() report_ggstatsplot_posthoc(): Report significant post-hoc pairwise comparisons
reportMeanAndSD() report_mean_sd(): Report the mean and standard deviation of a dependent variable for all levels of an independent variable rounded to the 2nd digit.
reportNparLD() report_nparld(): Report the model produced by nparLD. The model provided must be the model generated by the command 'nparLD' nparLD (see https://CRAN.R-project.org/package=nparLD).
reportNPAV(): Generate the Latex-text based on the NPAV by Lüpsen (see https://www.uni-koeln.de/~luepsen/R/). Only significant main and interaction effects are reported. P-values are rounded for the third digit and partial eta squared values are provided when possible. Attention: the independent variables of the formula and the term specifying the participant must be factors (i.e., use as.factor()).

Effect sizes

rFromWilcox(): Calculation based on Rosenthal's formula (1994). N stands for the number of measurements.
rFromWilcoxAdjusted(): rFromWilcoxAdjusted
rFromNPAV(): Calculation based on Rosenthal's formula (1994). N stands for the number of measurements.

LaTeX and Overleaf output

Getting the generated text into a document that compiles immediately.

latex_preamble(): LaTeX preamble required by the report functions
latex_escape(): Escape LaTeX special characters in plain text
latexify_report(): Transform text from report::report() into LaTeX-friendly output.
expand_latex_macros(): Expand the colleyRstats LaTeX macros to plain standard LaTeX
define_result_macro(): Define a named LaTeX macro for a single result (single source of truth)
emit_name_macros(): Generate \newcommand stubs for variable/factor names
use_colleyrstats_sty(): Write colleyRstats.sty into a project (for Overleaf)

Data preparation

reshape_data(): Reshape Excel Data Based on Custom Markers and Include Custom ID Column
replace_values(): Replace values across a data frame
remove_outliers_REI(): Flag suspicious survey responses via the Response Entropy Index (REI)
normalize(): This function normalizes the values in a vector to the range [new_min, new_max] based on their original range [old_min, old_max].
na.zero(): Replace NA values with zero
add_pareto_emoa_column(): Add PARETO_EMOA Column to a Data Frame
add_pareto_moocore_column(): Add PARETO_MOOCORE Column to a Data Frame

Small utilities

not_in() `%!in%`: Negate %in% membership
not_empty(): Ensure input is not empty
pathPrep(): Convert Windows paths to R-friendly format