aldex.kw: Calculate the Kruskal-Wallis test and glm statistics

In ggloor/ALDEx_bioc: Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account

Calculate the Kruskal-Wallis test and glm statistics

Description

aldex.kw calculates the expected values of the Kruskal-Wallis test and a glm on the data returned by aldex.clr.

Usage

aldex.kw(clr, useMC = FALSE, verbose = FALSE)

Arguments

clr	An ALDEx2 object. The output of aldex.clr.
useMC	Toggles whether to use multi-core.
verbose	A boolean. Toggles whether to print diagnostic information while running. Useful for debugging errors on large datasets. Applies to effect = TRUE.

Details

use the aldex.glm function unless you really need the nonparametric KW test

Value

Returns a data.frame with the following information:

kw.ep	a vector containing the expected p-value of the Kruskal-Wallis test for each feature
kw.eBH	a vector containing the corresponding expected value of the Benjamini-Hochberg corrected p-value for each feature
glm.ep	a vector containing the expected p-value of the glm ANOVA for each feature
glm.eBH	a vector containing the corresponding expected value of the Benjamini-Hochberg corrected p-value for each feature. Note, you should use the aldex.glm function for better post-hoc test statistics.

Author(s)

Arianne Albert

References

Please use the citation given by citation(package="ALDEx2").

See Also

aldex, aldex.clr, aldex.ttest, aldex.kw, aldex.glm, aldex.effect, aldex.corr, selex

Examples

data(selex)
#subset for efficiency
selex <- selex[1201:1600,]
conds <- c(rep("A", 4), rep("B", 3), rep("C", 7))
x <- aldex.clr(selex, conds, mc.samples=1, denom="all")
kw.test <- aldex.kw(x)

ggloor/ALDEx_bioc documentation built on Oct. 31, 2023, 1:13 a.m.