On Thu, 10 Jul 2008, Georg Ehret wrote:
Dear R community,
I am using 6 variables to test for an effect (by linear regression).
These 6 variables are strongly correlated among each other and I would like
to find out the number of independent test that I perform in this
calcuation.
For what purpose?
If you are trying to perform a multiple comparisons adjustment, you might
do better to skip this bit and go on to a resampling or permutational
procedure. There is an enormous literature on this subject. One example:
@book{West:Youn:1993,
author = {Westfall, Peter H. and Young, S. Stanley},
title = {Resampling-based multiple testing: {E}xamples and
methods for $p$-value adjustment},
year = {1993},
pages = {340},
ISBN = {0471557617},
publisher = {John Wiley \& Sons},
keywords = {Simultaneous inference; Bootstrap}
}
HTH,
Chuck
For this I calculated a matrix of correlation coefficients
between the variables (see below). But to find the rank of the table in R is
not the right approach... What else could I do to find the effective number
of independent tests?
Any suggestion would be very welcome!
Thanking you and with my best regards, Georg.
for (a in 1:6){
+ for (b in 1:6){
+
r[a,b]<-summary(lm(unlist(d[a])~unlist(d[b])),na.action="na.exclude")$adj.r.squared
+ }
+ }
r
SR SU ST DR DU DT
SR 1.0000000 0.9636642 0.9554952 0.2975892 0.3211303 0.3314694
SU 0.9636642 1.0000000 0.9101678 0.3324979 0.3331389 0.3323826
ST 0.9554952 0.9101678 1.0000000 0.2756876 0.3031676 0.3501157
DR 0.2975892 0.3324979 0.2756876 1.0000000 0.9981733 0.9674843
DU 0.3211303 0.3331389 0.3031676 0.9981733 1.0000000 0.9977780
DT 0.3314694 0.3323826 0.3501157 0.9674843 0.9977780 1.0000000
*************************
Georg Ehret
Johns Hopkins University
Baltimore, US
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Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:[EMAIL PROTECTED] UC San Diego
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