I am currently using the relaimpo package to estimate the relative importance of regressors (N= 4000):
> m1 <- lm(y ~ x1+x2+x3+x4+x5+, data=data) > calc.relimp(m1, rela=TRUE) > m2=boot.relimp(m1, boot = 500, rela=TRUE, type="lmg") > booteval.relimp(m2) > plot(booteval.relimp(m2)) In a new dataset with 3 measurement points (0,6,12 weeks), I want to perform a similar analysis, and want overall relative importance estimates over all 3 time points. A standard mixed effects model would be adding time as fixed and subject as random effect: > m1 <- lmer(y ~ x1+x2+x3+x4+x5+time+(1| subject), data=data) > m1.p = pvals.fnc(m1) Unfortunately, that does not allow me to estimate relative importance because relaimpo cannot handle LME4 output. > Error in calc.relimp.default.intern(object = <S4 object of class > "mer">, : If x is NULL, then object must be a data frame or a > matrix. Would it be possible to transform the LME4 output in a way that relaimpo could read it? Do you have other ideas as to how to tackle this, e.g. other ways of tackling the concept of relative importance / strength of the regressors? Thank you EF [[alternative HTML version deleted]] ______________________________________________ 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.