John Fox wrote: > Dear Horace, > > The Bonferonni p-value is obtained from the "unadjusted" p-value by > multiplying the latter by the number of observations, and provides a > conservative (although usually quite accurate) outlier test. When the > adjusted p-value exceeds 1 you can take that as an indication that there are > no unusually large studentized residuals (and indeed that the largest > studentized residual is smaller than one would expect under the standard > linear-model assumptions). > > Yes, or put differently, the Bonferroni p is an upper bound of the true p. It is only accurate at the low end of the p scale. (It works by approximating P(A or B) by P(A)+P(B); since P(A and B) gets counted twice (make a diagram) that term needs to be small.)
BTW. This was yet another case of someone tacking their mail onto a completely different thread (replying to a random mail from r-help). Please avoid, since it confuses threading mail programs and the archiving system). I was looking for responses, so shifted to threaded mode and the post all but disappeared because it got tucked in under "multi-level modeling & R?" > I hope this helps, > John > > -------------------------------- > John Fox > Department of Sociology > McMaster University > Hamilton, Ontario > Canada L8S 4M4 > 905-525-9140x23604 > http://socserv.mcmaster.ca/jfox > -------------------------------- > > >> -----Original Message----- >> From: [EMAIL PROTECTED] >> [mailto:[EMAIL PROTECTED] On Behalf Of Horace Tso >> Sent: Wednesday, March 28, 2007 6:36 PM >> To: 'R R-help' >> Subject: [R] Bonferroni p-value greater than 1 >> >> Hi folks, >> >> I use the outlier.test in package car to test a lm model and >> the bonferroni p value returned is shown as NA. When the >> object is typed it indicates the p value is greater than 1. >> I'm not sure how to interpret it. >> >> Thanks in advance. >> >> Horace W. Tso >> >> >> >>> outlier.test(mod)$test >>> >> max|rstudent| df unadjusted p Bonferroni p >> 2.04106376 18.00000000 0.05618628 NA >> >> >>> outlier.test(mod) >>> >> max|rstudent| = 2.041064, degrees of freedom = 18, >> unadjusted p = 0.05618628, Bonferroni p > 1 >> >> Observation: 1 >> >> The lm model looks fine to me, >> >> >>> summary(mod) >>> >> Call: >> lm(formula = x ~ ind, na.action = na.fail) >> >> Residuals: >> Min 1Q Median 3Q Max >> -1.2082 -0.5200 0.1309 0.5725 0.9593 >> >> Coefficients: >> Estimate Std. Error t value Pr(>|t|) >> (Intercept) 59.84586 0.31900 187.6 < 2e-16 *** >> ind -0.16768 0.02541 -6.6 2.57e-06 *** >> --- >> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 >> >> Residual standard error: 0.705 on 19 degrees of freedom >> Multiple R-Squared: 0.6963, Adjusted R-squared: 0.6803 >> F-statistic: 43.56 on 1 and 19 DF, p-value: 2.57 >> >> ______________________________________________ >> R-help@stat.math.ethz.ch 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. >> >> > > ______________________________________________ > R-help@stat.math.ethz.ch 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. > ______________________________________________ R-help@stat.math.ethz.ch 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.
