At 07:35 AM 2/14/2010, Manuel Jesús López Rodríguez wrote:
Dear all,
I am trying to study the correlation between one
"independent" variable ("V1") and several others
dependent among them ("V2","V3","V4" and "V5").
For doing so, I would like to analyze my data by
multiple-test (applying the Bonferroni´s
correction or other similar), but I do not find
the proper command in R. What I want to do is to
calculate Kendall´s correlation between "V1" and
the others variables (i.e. "V1" vs "V2", "V1" vs
"V3", etc.) and to correct the p values by
Bonferroni or other. I have found
"outlier.test", but I do not know if this is
what I need (also, I would prefer to use a less
conservative method than Bonferroni´s, if possible).
Thank you very much in advance!
One approach might be to first test for any
correlations via a likelihood ratio test:
Ho: P = I (no correlations) or covariances are diagonal
T = -a ln V ~ chi-square [p(p-1)/2]
where
V = det(R)
a = N -1 - (2 p +5)/6 N = # data
p = # variables
Reject Ho if T > X^2 (alpha, p(p-1)/2)
Then do the pairwise tests without familywise
error control. I.e., this is similar to doing the
F test in ANOVA before doing LSD testing.
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Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: r...@lcfltd.com
Least Cost Formulations, Ltd. URL: http://lcfltd.com/
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