Another possibility is to do a regression to predict variance for species with a single observation. Or even do a phylogenetic regression so species nearer the ones with missing data matter more.
But all this stuff is minor tweaks: it's great you're incorporating measurement error at all, and I hope your results are robust to any of these suggestions for tweaks. Best, Brian _______________________________________________________________________ Brian O'Meara, http://www.brianomeara.info, especially Calendar <http://brianomeara.info/calendars/omeara/>, CV <http://brianomeara.info/cv/>, and Feedback <http://brianomeara.info/teaching/feedback/> Associate Professor, Dept. of Ecology & Evolutionary Biology, UT Knoxville Associate Head, Dept. of Ecology & Evolutionary Biology, UT Knoxville Associate Director for Postdoctoral Activities, National Institute for Mathematical & Biological Synthesis <http://www.nimbios.org> (NIMBioS) Communication Director, Society of Systematic Biologists On Tue, Aug 16, 2016 at 11:53 PM, Liam J. Revell <liam.rev...@umb.edu> wrote: > Hi Santiago. > > This is identical to my suggestion, except that the pooled variance is a > weighted mean in which weights (for better or worse) are proportional to > the sample size of each species. If the variances are indeed homogeneous, > this should be preferred because it gives greater weight to species whose > variance we should know well. If not, then it risks giving high weight to a > species with a well-estimated, but peculiarly high or low variance. > Computing the straight mean, as you suggest, comes with exactly the > opposite set of shortcomings since species with relatively small sample > sizes may have very poorly estimated variances. > > All the best, Liam > > Liam J. Revell, Associate Professor of Biology > University of Massachusetts Boston > web: http://faculty.umb.edu/liam.revell/ > email: liam.rev...@umb.edu > blog: http://blog.phytools.org > > On 8/16/2016 10:46 PM, Santiago Claramunt wrote: > >> Hi Rafael, >> >> Your method would underestimate the error associated with values derived >> from single specimens because those values would have the highest errors, >> not average errors. >> What I have done in such cases is to estimate an average standard >> deviation across species and use that average standard deviation as the >> standard error of the species with single specimens. >> I don't know of a formal description of this solution but it is mentioned >> in one of my papers: http://rspb.royalsocietypublis >> hing.org/content/279/1733/1567 >> >> Best, >> >> Santiago >> >> Research Associate >> Department of Ornithology >> American Museum of Natural History >> https://sites.google.com/site/sclaramuntuy/ >> >> >> On Aug 16, 2016, at 4:34 PM, Rafael S. Marcondes <raf.marcon...@gmail.com> >>> wrote: >>> >>> Hi all, >>> >>> I’m using OUwie to fit multi-optima OU models and I have a question about >>> incorporating measurement error into my analyses. >>> >>> I’m running my models with known measurement error (mserr=‘known’) and >>> using the standard error (std.error()) as an estimate of it, as >>> recommended >>> by Ives et al (2007). However, for some (a minority) of my tips, I was >>> only >>> able to measure 1 specimen, so I have no standard error for them. So I’m >>> not sure about how to deal with those. At first I thought about just >>> setting their measurement error as 0, but then I figured that would >>> introduce false confidence. So what I’m doing now is I’m setting >>> measurement error for those tips as the mean of the errors of all the >>> tips >>> for which I did measure more than one specimen. I got that idea also from >>> Ives et al when they mention averaging the error across species (jn the >>> third-to-last paragraph), but that was in a different context. I can’t >>> find >>> any references that report dealing with the same problem, even though I >>> assume it must not be an uncommon one. So I’m wondering if mine is really >>> the best way to do it and, or if anyone has alternative suggestions? >>> >>> i hope I’ve made my problem clear, and thanks in advance for any >>> suggestions. >>> >>> >>> *--* >>> *Rafael Sobral Marcondes* >>> >>> PhD Candidate (Systematics, Ecology and Evolution/Ornithology) >>> Museum of Natural Science <http://sites01.lsu.edu/wp/mns/> >>> Louisiana State University >>> 119 Foster Hall >>> Baton Rouge, LA 70803, USA >>> >>> Twitter: @rafmarcondes <https://twitter.com/rafmarcondes> >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> R-sig-phylo mailing list - R-sig-phylo@r-project.org >>> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo >>> Searchable archive at http://www.mail-archive.com/r- >>> sig-ph...@r-project.org/ >>> >> >> _______________________________________________ >> R-sig-phylo mailing list - R-sig-phylo@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo >> Searchable archive at http://www.mail-archive.com/r- >> sig-ph...@r-project.org/ >> >> > _______________________________________________ > R-sig-phylo mailing list - R-sig-phylo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-phylo > Searchable archive at http://www.mail-archive.com/r- > sig-ph...@r-project.org/ > [[alternative HTML version deleted]] _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/
