aldex.kw | R Documentation |
aldex.kw
calculates the expected values of the Kruskal-Wallis
test and a glm on the data returned by aldex.clr
.
aldex.kw(clr, useMC = FALSE, verbose = FALSE)
clr |
An |
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
|
use the aldex.glm function unless you really need the nonparametric KW test
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. |
Arianne Albert
Please use the citation given by
citation(package="ALDEx2")
.
aldex
,
aldex.clr
,
aldex.ttest
,
aldex.kw
,
aldex.glm
,
aldex.effect
,
aldex.corr
,
selex
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)
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