Try the gls function in the nlme package. It allows you to model the variance as well as the mean.
-----Original Message----- From: "Bunny, lautloscrew.com" <[EMAIL PROTECTED]> To: "r-help@r-project.org" <r-help@r-project.org> Sent: 10/9/08 3:40 AM Subject: [R] adjusted t-test with unequal variance Hi all, right now i am simply comparing means. obviously this can be done by the simple t.test respectively the welch test, if var.equal is set to FALSE. just like this t.test( Y ~ group) t.test( Y ~ group, var.equal = FALSE) now that i need to compare weighted means i am using the lm function as an adjusted t-test: like lmtest <- ( Y ~ group ) anova(lmtest) lmtest$fitted.values[data$group==1] lmtest$fitted.values[data$group==0] basically this delivers just the same means and p.value like the test with equal variance. and here's where my problem is...: checking bartletts test and the var.test i found that the assumption of equal variance might be at least venturesome for some of my variables... Can I replace the lmtest by something else, assuming variances are not equal ? I read about a quasi option of glm on the mailing lists... Thx in advance for any suggestions ______________________________________________ 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. ______________________________________________ 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.
