[R] Help formulating gamm with repeated measures and spatial autocorrelation using mgcv package

(Also posted on StackExchange but submitting to R-help to reach more potential experts)I am using the gamm function in the mgcv package in R to specify a model that predicts abundance with respect to elevation and year based on repeated measures from several sites. My overarching question is how

[R] Party package: varimp(..., conditional=TRUE) error: term 1 would require 9e+12 columns (fwd)

Hello, I'm following previous threads on the use of varimp in the R "party" package. I'm running up against similar problems to that described here (and the thread referenced therein): https://stat.ethz.ch/pipermail/r-help/2011-October/292897.html Specifically, I'm running cforest and then varim

[R] evaluating spatial autocorrelation in a raster

Hi I am attempting to explore the scale of spatial autocorrelation in a raster (eventually across a stack of 10 but for now a single layer) and consequently in a potential sample of points across the landscape (ie. if we wanted to know what sampling design in terms of distance would minimize aut

[R] lme help configuring random effects

Hi Everyone,  Sorry to ask what I think is a basic question but I really haven't found my answer yet in the archives.  I am trying to run a mixed effects model in R using the lme package. My experiment is such that I am interested in the effects of Temperature (2 levels) and Species (3 levels)

[R] help with calculation from dataframe with multiple entries per sample

Hi  I have a dataframe similar to: >Sample<-c(1,1,1,2,2,2,3,3,3) >Time<-c(1,2,3,1,2,3,1,2,3) >Mass<-c(3,3.1,3.4,4,4.3,4.4,3,3.2,3.5) >mydata<-as.data.frame(cbind(Sample,Time,Mass))   Sample Time Mass 1      1    1  3.0 2      1    2  3.1 3      1    3  3.4 4      2    1  4.0 5      2    2  4