I am using "apply" to run exact permutation t-tests by columns
using the coin package. For example:
library(coin)
dat <- matrix(rnorm(7*35),7,35)
fun <- function(x) {
pvalue( independence_test(x~f,
data=data.frame(x, f=factor(c(rep("a",4),rep("b",3)))),
distribution = "exact"))
}
p.vals <- apply(dat, 2, fun)
Some small-scale comparisons with the perm package show
that coin does the actual permutation tests much faster
than perm but when I apply them both over the same matrix
coin ends up taking longer. My guess is that turning
each column into a data frame is what slows coin down.
Any ideas on how to tune this up would be most appreciated.
Bryan
---------------
Bryan Keller, Doctoral Student/Project Assistant
Educational Psychology - Quantitative Methods
The University of Wisconsin - Madison
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