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!
At 07:35 AM 2/14/2010, Manuel Jes?s L?pez Rodr?guez 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. ===============================================================Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: ral at lcfltd.com Least Cost Formulations, Ltd. URL: http://lcfltd.com/ 824 Timberlake Drive Tel: 757-467-0954 Virginia Beach, VA 23464-3239 Fax: 757-467-2947 "Vere scire est per causas scire" ================================================================
?p.adjust -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.snow at imail.org 801.408.8111> -----Original Message----- > From: r-help-bounces at r-project.org [mailto:r-help-bounces at r- > project.org] On Behalf Of Manuel Jes?s L?pez Rodr?guez > Sent: Sunday, February 14, 2010 5:35 AM > To: r-help at r-project.org > Subject: [R] multiple-test correlation > > 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! > > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code.