kw_rank_sum {structToolbox} | R Documentation |
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.
kw_rank_sum(alpha = 0.05, mtc = "fdr", factor_names, ...)
alpha |
(numeric) The p-value cutoff for determining significance. The default is |
mtc |
(character) Multiple test correction method. Allowed values are limited to the following:
The default is |
factor_names |
(character) The name of sample meta column(s) to use. |
... |
Additional slots and values passed to |
A kw_rank_sum
object.
D = iris_DatasetExperiment() M = kw_rank_sum(factor_names='Species') M = model_apply(M,D)