kw_rank_sum: Kruskal-Wallis rank sum test

In structToolbox: Data processing & analysis tools for Metabolomics and other omics

Description

The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n>=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features.

Usage

kw_rank_sum(alpha = 0.05, mtc = "fdr", factor_names, ...)

Arguments

alpha	(numeric) The p-value cutoff for determining significance. The default is 0.05.
mtc	(character) Multiple test correction method. Allowed values are limited to the following: "bonferroni": Bonferroni correction in which the p-values are multiplied by the number of comparisons. "fdr": Benjamini and Hochberg False Discovery Rate correction. "none": No correction. The default is "fdr".
factor_names	(character) The name of sample meta column(s) to use.
...	Additional slots and values passed to struct_class.

Value

A kw_rank_sum object.

Examples

D = iris_DatasetExperiment()
M = kw_rank_sum(factor_names='Species')
M = model_apply(M,D)

structToolbox documentation built on Nov. 8, 2020, 6:54 p.m.