I don't think that the loop-part puts a great penalty in this case;
however, it'd be better to convert the data.frames to matrices, since
matrix indexing is faster, e.g.,
# I presume that you only want to keep the p-values
mat.A <- data.matrix(df.A)
mat.B <- data.matrix(df.B)
pvalues <- numeric(200)
for (i in 1:200) {
pvalues[i] <- wilcox.test(x = mat.A[, i], y = mat.B[, i])$p.value
}
I hope it helps.
Best,
Dimitris
----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/(0)16/336899
Fax: +32/(0)16/337015
Web: http://med.kuleuven.be/biostat/
http://www.student.kuleuven.be/~m0390867/dimitris.htm
----- Original Message -----
From: "Diogo Alagador" <[EMAIL PROTECTED]>
To: <r-help@r-project.org>
Sent: Tuesday, November 06, 2007 12:30 PM
Subject: [R] wilcox test on two data frames
Hi all,
Basically I have 2 data frames with equal dimension and I want to
apply the wilcox.test to compare columns in the same position (i.e.
1st of df.A with 1st of df.B, 2nd of df.A with 2nd of df.B,...).
Anyone give me an hint on this, as I think it is nicer to avoid loops,
specially for huge dataframes (30000 x 200)
Thanks in advance,
Diogo André Alagador
Portugal
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