As you seem to be wandering in the wilderness here, it sounds like you should really be seeking local statistical help that can provide a fuller 1-1 discussion, rather than posting on the internet. Alternatively, although you are working within R, your questions are primarily about statistical matters, for which stats.stackexchange.com is a better fit. We tend to be more focused on R programming and features rather than statistics on this list, although they certainly overlap.
Of course, you may get lucky -- Terry is often generous with his time and advice -- but if you do not... Cheers, Bert On Sat, Dec 21, 2013 at 9:22 AM, David Gibbs <dgib...@gatech.edu> wrote: > Hello all, > > I have some questions about specifying a coxme model and then simplifying > it after reading the coxme documentation and posts here. The situation is > this: > > I glued 4 pieces of small coral fragments onto small ceramic tiles, which I > placed at 4 distances east and west of 10 large coral colonies (i.e. site). > Thus, each tile represents one distance-direction-site combination. I > checked the small coral fragments daily to see which had died overnight and > at the end of the experiment some were still alive (thus, censored). I > therefore had 4 fragments per tile*4 distances*2 directions*10 sites = 320 > small fragments. Distance and direction are fixed effects, while the tile > that each fragment is on and the site are random effects. In addition, each > large colony is a different size, so the size of the large colonies should > be a random effect, too (SiteSize). > > The model I wrote to express this is: > mefull<-coxme(Surv(death, censor) ~ Distance*Direction+(1|Site/Tile) > +(1|SiteSize)) > > First, can anyone tell me if this properly specifies the situation I > described above? > > After running this model, I found that neither fixed effect nor their > interaction was significant. Also, the standard deviation for Site and > SiteSize are identical (~1.12), which seems strange to me. Is there a > reason for that? The fact that they are both greater than 1 indicates to me > that they contribute a lot of variation to survival. Is that correct? > > My next major question is how to simplify this model. My instinct (and > based on reading Terry Therneau's manuals and other posts here) is to > remove each random effect in turn and compare the AICs of the integrated > log-likelihood of the resulting models; the higher AIC is the preferred > model in this formulation. Is that correct? > > However, I'd also like to try to try to simplify the model through removal > of the non-significant fixed effects, starting with their interaction. How > can I do this while also removing random effects? What terms should I start > with removing, or does the order not matter as long as I start with > higher-order terms (i.e. interaction)? Can I try as many combinations as I > like or do issues with multiple tests come into play? > > Some options are removing one of the random effects (me2) or removing the > interaction between the fixed effects but keeping the random effects in > place (me3). > me2<-coxme(surv ~ Distance*Direction+(1|Site/Tile)) > me3<-coxme(surv ~ Distance+Direction+(1|Site/Tile)+(1|SiteSize)) > > When I run these and other combinations of factors, their AIC is always > lower than that of the full model, which suggests to me that the full model > is best. Any guidance on how to simplify this model would be greatly > appreciated. > > Finally, to compare the model with random effects to one without, can I > compare the NULL log-likelihood with the integrated log-likelihood? From my > understanding of the coxme manual, the one closer to 0 is the better model, > so if the integrated one is closer to 0 then the model with random effects > is preferred over the one without random effects. > > Thanks very much for your time and help. > > David Gibbs > Georgia Institute of Technology > > [[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. -- Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 ______________________________________________ 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.
