Hello,
You can compute the p-value from the test statistic if you know the
samples' sizes. R calls functions written in C for the several cases,
for the two samples case, this is the code (edited)
n.x <- 100 # length of 1st sample
n.y <- 100 # length of 2nd sample
STATISTIC <- 1.23
PVAL <- 1 - .C("psmirnov2x",
p = as.double(STATISTIC),
as.integer(n.x),
as.integer(n.y))$p
PVAL <- min(1.0, max(0.0, PVAL))
For the other cases check the source, file stats/ks.test.R.
As for the second question, I believe the answer is no, you must provide
at least on sample and a CDF. Something like
x <- rnorm(100)
f <- ecdf(rnorm(100))
ks.test(x, f)
Hope this helps,
Rui Barradas
Em 03-03-2013 09:58, Rani Elkon escreveu:
Dear all,
I calculate the test statistic for the KS test outside R, and wish to use R
only to calculate the corresponding p-value.
Is there a way for doing this? (as far as I see, ks.test() requires raw
data as input). Alternatively, is there a way to provide the ks.test() the
two CDFs (two samples test) rather than the (x, y) data vectors?
Thanks in advance,
Rani
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