On Sat, May 3, 2008 at 9:00 PM, Tobias Erik Reiners <[EMAIL PROTECTED]> wrote: > Dear Helpers, > > I just started working with R and I'm a bit overloaded with information. > > My data is from marsupials reindroduced in a area. I have weight(wt), hind > foot > lenghts(pes) as continues variables and origin and gender as categorial. > condition is just the residuals i took from the model. > > > > names(dat1) > > > [1] "wt" "pes" "origin" "gender" "condition" > > my model after model simplification so far: > model1<-lm(log(wt)~log(pes)+origin+gender+gender:log(pes)) > -->six intercepts and two slopes > > the problem is i have some things I can't include in my analysis: > 1.Very different sample sizes for each of the treatments > > > tapply(log(wt),origin,length) > > > captive site wild > 119 149 19 > 2.Substantial differences in the range of values taken by the covariate > (leg length) between treatments > > > tapply(pes,origin,var) > > > captive site wild > 82.43601 71.44442 60.42544 > > > tapply(pes,origin,mean) > > > captive site wild > 147.3261 144.8698 148.2895 > > 4.Outliers > 5.Poorly behaved residuals > > thanks for the answer I am open minded to any different kind of analysis.
How about starting with some graphics? e.g. with ggplot2 the following would give you some clues as to whether your models are appropriate or not: qplot(pes, wt, data=dat1, colour=gender, facets = . ~ origin, log="xy") + geom_smooth(method=lm) qplot(pes, wt, data=dat1, facets = gender ~ origin, log="xy") + geom_smooth(method=lm) If you wanted to the see the effect of a robust fit, as suggested by Brian Ripley, replace lm with rlm. Hadley -- http://had.co.nz/ ______________________________________________ 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.
