On Friday 23 January 2009, David Freedman wrote: > library(MASS); library(boot) > #create intercorrelated data > Sigma <- matrix(c(1,.5,.4, .5,1,.8, .4,.8,1),3,3) > Sigma > dframe<-as.data.frame(mvrnorm(n<-200, rep(0, 3), Sigma)) > names(dframe)<-c('disease','age','ht') #age and ht are predictors of > 'disease' > head(dframe); cor(dframe) > > #bootstrap the difference between models containing the 2 predictors > model.fun <- function(data, indices) { > dsub<-dframe[indices,] > m1se<-summary(lm(disease~age,data=dsub))$sigma; > m2se<-summary(lm(disease~ht,da=dsub))$sigma; > diff<-m1se-m2se; #diff is the difference in the SEs of the 2 models > } > eye <- boot(dframe,model.fun, R=200); class(eye); names(eye); > des(an(eye$t)) > boot.ci(eye,conf=c(.95,.99),type=c('norm')) >
This may be a naive question, but could this be used to test two models based on difference transformations of the dependent variable? [...] m1se<-summary(lm(disease ~ age, data=dsub))$sigma m2se<-summary(lm(log(disease) ~ age, da=dsub))$sigma [...] or would the differences in scales render meaningless results? Cheers, Dylan -- Dylan Beaudette Soil Resource Laboratory http://casoilresource.lawr.ucdavis.edu/ University of California at Davis 530.754.7341 ______________________________________________ R-help@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.