kw_rank_sum: Kruskal-Wallis rank sum test

View source: R/kw_rank_sum_class.R

kw_rank_sumR Documentation

Kruskal-Wallis rank sum test

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 with the following output slots:

test_statistic (data.frame) The value of the calculated statistic which is converted to a p-value when compared to a chi2-distribution.
p_value (data.frame) The probability of observing the calculated statistic.
dof (numeric) The number of degrees of freedom used to calculate the test statistic.
significant (data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).
estimates (data.frame)

Inheritance

A kw_rank_sum object inherits the following struct classes:

⁠[kw_rank_sum]⁠ >> ⁠[model]⁠ >> ⁠[struct_class]⁠

Examples

M = kw_rank_sum(
      alpha = 0.05,
      mtc = "fdr",
      factor_names = "V1")

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

computational-metabolomics/structtoolbox documentation built on July 2, 2024, 10:46 p.m.