View source: R/kw_rank_sum_class.R
kw_rank_sum | 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 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) |
A kw_rank_sum
object inherits the following struct
classes:
[kw_rank_sum]
>> [model]
>> [struct_class]
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)
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