Hello,
Try the following.
f <- function(x, y, ...,
alternative = c("two.sided", "less", "greater"), exact = NULL){
#w <- getOption("warn")
#options(warn = -1) # ignore warnings
p <- ks.test(x, y, ..., alternative = alternative, exact =
exact)$p.value
#options(warn = w)
p
}
n <- 1e1
dat <- data.frame(X=rnorm(n), Y=runif(n), Z=rchisq(n, df=3))
apply(dat, 2, function(x) apply(dat, 2, function(y) f(x, y)))
Hope this helps,
Rui Barradas
Em 28-09-2012 11:10, Johannes Radinger escreveu:
Hi,
I have a dataframe with multiple (appr. 20) columns containing
vectors of different values (different distributions).
Now I'd like to create a crosstable
where I compare the distribution of each vector (df-column) with
each other. For the comparison I want to use the ks.test().
The result should contain as row and column names the column names
of the input dataframe and the cells should be populated with
the p-value of the ks.test for each pairwise analysis.
My data.frame looks like:
df <- data.frame(X=rnorm(1000,2),Y=rnorm(1000,1),Z=rnorm(1000,2))
And the test for one single case is:
ks <- ks.test(df$X,df$Z)
where the p value is:
ks[2]
How can I create an automatized way of this pairwise analysis?
Any suggestions? I guess that is a quite common analysis (probably with
other tests).
cheers,
Johannes
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