"anders superanders" <[EMAIL PROTECTED]> writes: > Hi I was wondering if there is a permutation test available in R for linear > models with continuous dependent covariates. I want to do a test like the > one shown here. > > bmi<-rnorm(100,25) > x<-c(rep(0,75),rep(1,25)) > y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x > > H0<-lm(y~1+x+bmi) > H1<-lm(y~1+x+bmi+x*bmi) > anova(H0,H1) > summary(lm(y~1+x+bmi)) > > > But I want to use permutation testing to avoid an inflated p-value due to a > y that is not totally normal distributed and I do not want to log transform > y.
Er, what would you permute? For an interaction test like this (notice by the way that "*" in your model formula does not mean what you think it does) I do not think a permutation test exists. You could try bootstrapping to get an improved approximation the distribution of the interaction term. -- O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
