group_test: Performs multiple mean comparisons for a data.frame

In pcutils: Some Useful Functions for Statistics and Visualization

Performs multiple mean comparisons for a data.frame

Description

Performs multiple mean comparisons for a data.frame

Usage

group_test(
  df,
  group,
  metadata = NULL,
  method = "wilcox.test",
  pattern = NULL,
  p.adjust.method = "none",
  threads = 1,
  verbose = TRUE
)

Arguments

df	a data.frame
group	The compare group (categories) in your data, one column name of metadata when metadata exist or a vector whose length equal to columns number of df.
metadata	sample information dataframe contains group
method	the type of test. Default is wilcox.test. Allowed values include: t.test (parametric) and wilcox.test (non-parametric). Perform comparison between two groups of samples. If the grouping variable contains more than two levels, then a pairwise comparison is performed. anova (parametric) and kruskal.test (non-parametric). Perform one-way ANOVA test comparing multiple groups. chisq.test, performs chi-squared contingency table tests and goodness-of-fit tests. 'pearson', 'kendall', or 'spearman' (correlation), see cor.
pattern	a named vector matching the group, e.g. c('G1'=1,'G2'=3,'G3'=2), use the correlation analysis with specific pattern to calculate p-value.
p.adjust.method	p.adjust.method, see p.adjust, default BH.
threads	default 1
verbose	logical

Value

data.frame

Examples

data(otutab)
group_test(otutab, metadata$Group, method = "kruskal.test")
group_test(otutab[, 1:12], metadata$Group[1:12], method = "wilcox.test")

pcutils documentation built on April 4, 2025, 5:14 a.m.