At the moment I have a data frame of p values.
l<- data.frame(p1 = c(0.01,0.5,0.6), p2= c(0.04,0.9,0.02))
and I would like to apply p.adjust using all methods on each column independently of the others.
Below is the only way that seems to work... but only for one column at a time.
library(multcomp)
set.seed(2020)
l<- data.frame(p1 = c(0.01,0.5,0.6), p2= c(0.04,0.9,0.02))
p.adjust.M<- p.adjust.methods
sapply(p.adjust.M, function(meth) p.adjust( l[,1], meth))
`p.adjust.M<- p.adjust.methods`
The output below is for the first column. I would ideally like to apply all the methods below to all columns in one go and have n dataframes corresponding to n columns of adjustments.
holm hochberg hommel bonferroni BH BY fdr none
[1,] 0.03 0.03 0.03 0.03 0.03 0.055 0.03 0.01
[2,] 1.00 0.60 0.60 1.00 0.60 1.000 0.60 0.50
[3,] 1.00 0.60 0.60 1.00 0.60 1.000 0.60 0.60
Unfortunately I am going to have a lot of columns so this isn't a feasible approach.
Thanks in advance!
You could wrap the whole sapply() function inside an lapply() function where the data (first argument) is your dataset, like below:
lapply(l, function(L)sapply(p.adjust.M, function(meth) p.adjust( L, meth)))
# $p1
# holm hochberg hommel bonferroni BH BY fdr none
# [1,] 0.03 0.03 0.03 0.03 0.03 0.055 0.03 0.01
# [2,] 1.00 0.60 0.60 1.00 0.60 1.000 0.60 0.50
# [3,] 1.00 0.60 0.60 1.00 0.60 1.000 0.60 0.60
#
# $p2
# holm hochberg hommel bonferroni BH BY fdr none
# [1,] 0.08 0.08 0.08 0.12 0.06 0.11 0.06 0.04
# [2,] 0.90 0.90 0.90 1.00 0.90 1.00 0.90 0.90
# [3,] 0.06 0.06 0.06 0.06 0.06 0.11 0.06 0.02