factorH: intro

What this package does (and why)

factorH provides a simple, single-call workflow for multifactor nonparametric, rank-based ANOVA and publication-ready outputs:

• ANOVA-like table based on ranks (rooted in Kruskal-Wallis H and the 2x2 Scheirer-Ray-Hare extension),

• effect sizes computed directly from H

• Dunn–Bonferroni post hoc comparison matrices

• simple-effects post hocs (pairwise comparisons within levels of conditioning factors),

• compact descriptive tables and a TSV writer for quick formatting in Excel or a manuscript.

Why? Popular GUI stats tools do not offer a ready-made, user-friendly multifactor rank-based pipeline that mirrors standard H / SRH analyses in a way that is easy for beginners. factorH aims to fill that gap with clear, R-like formula syntax and a one-command report function.

The package is intentionally small: most users will only ever need:

• srh.kway.full(…) to compute everything
• write.srh.kway.full.tsv(…) to export the results into a single tab-separated file.

Formula syntax at a glance

All high-level functions use standard R model formulas:

response ~ factorA + factorB + factorC

lists main effects - Interactions are handled internally. You do not need to write A:B or A*B. The response (left of ~) must be numeric (e.g., a Likert score coded as 1..5 stored as numeric).

Examples below use the included dataset mimicry.

library(factorH)
data(mimicry, package = "factorH")
str(mimicry)

Predictors should be factors. If not, functions will coerce them.

What is allowed?

# One factor (KW-style):  
  liking ~ condition

# Two factors (SRH-style):  
  liking ~ gender + condition

# Three or more factors (k-way):  
  liking ~ gender + condition + age_cat

You do not need to write gender:condition or gender*condition. The package will build all needed interactions internally when relevant.

Numeric response (Likert note)

The response must be numeric. For Likert-type items (e.g., 1 = strongly disagree … 5 = strongly agree), keep them numeric; rank-based tests are robust for such ordinal-like data.

If your Likert is accidentally a factor or character, coerce safely:

# if stored as character "1","2",...:
mimicry$liking <- as.numeric(mimicry$liking)
# if stored as factor with labels "1","2",...:
mimicry$liking <- as.numeric(as.character(mimicry$liking))

The one-call pipeline

The main function srh.kway.full() runs:

1. ANOVA-like table on ranks
2. descriptive summary
3. post hoc matrices (Dunn–Bonferroni; P.adj)
4. simple-effects post hocs (within-family Bonferroni).

For 2 factors:

res2 <- srh.kway.full(liking ~ gender + condition, data = mimicry)
names(res2)
res2$anova
head(res2$summary)
names(res2$posthoc_cells)
names(res2$posthoc_simple)[1:4]

For 3 factors:

res3 <- srh.kway.full(liking ~ gender + condition + age_cat, data =
mimicry)
res3$anova

Export to a tab-separated file

f <- tempfile(fileext = ".tsv")
write.srh.kway.full.tsv(res3, file = f, dec = ".") # decimal dot
file.exists(f)

If you need comma as decimal mark:

f2 <- tempfile(fileext = ".tsv")
write.srh.kway.full.tsv(res3, file = f2, dec = ",") # decimal comma
file.exists(f2)

The TSV contains clearly separated sections:
## SRH: EFFECTS TABLE, ## SUMMARY STATS, ## POSTHOC CELLS, ## SIMPLE EFFECTS, ## META.

What is in the example dataset?

mimicry is a real study on the chameleon effect (Trzmielewska, Duras, Juchacz & Rak, 2025): how mimicry vs other movement conditions affect liking of an interlocutor. Potential moderators include gender and age (with dichotomized age_cat, and a 3-level age_cat2). This makes it a natural playground for multifactor rank-based analyses.

table(mimicry\$condition)
table(mimicry\$gender)
table(mimicry\$age_cat)

What the functions compute (high level)

• srh.kway(): rank-based k-way ANOVA table using Type II SS on ranks; p-values are tie-corrected; H is reported with and without the correction factor; effect sizes from unadjusted H.
• srh.effsize(): 2-way SRH table with effect sizes (eta2H, eps2H) computed from H.
• nonpar.datatable(): compact descriptive tables with global ranks (means of ranks per cell), medians, quartiles, IQR, etc., for all main effects and interactions.
• srh.posthocs(): Dunn–Bonferroni pairwise matrices (P.adj) for all effects (main and interactions).
• srh.simple.posthoc() / srh.simple.posthocs(): simple-effects pairwise comparisons within levels of conditioning factors (SPSS-like “within” scope by default).
• srh.kway.full(): orchestrates all of the above.
• write.srh.kway.full.tsv(): exports everything into one TSV (with dot or comma decimal mark).

That is it. For most users, the intro ends here: use srh.kway.full() and export with write.srh.kway.full.tsv().

C:8WGoPl2c1c40c845ef-intro.R