kw_rank_sum {structToolbox} | R 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)

### Examples

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

[Package structToolbox version 1.6.1 Index]