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...deviations from the set of 4 females nested within a given male, right? Of course, it is more appropriate for me to treat each of these as random effects. I know Bates has objections to the SAS-style partitioning variances to obtain F statistics and p-values, and I have read relevant parts of Pinhero and Bates, but how can a specify a nested random effects model that yields p-values for both the males (tested against MS for females) and females nested within males? Thanks, Dan Bolnick Section of Integrative Biology University of Texas at Austin

Hi folks Can anyone direct me to a guide to analysing mixed models in R that is understandable by mere mortals (or worse, mere biologists)? I have read MASS and also Mick Crawley's book on the subject, have thrashed my way through the various help files from the NLME package and I'm afraid I remain baffled :-(. Particularly confusing is exactly what groupedData does, how I should use

...p (among Sample nested within group) variance. He/she applied Wald's Z in SPSS, which provides estimates of variance components as well as standard errors of these estimates. Wald's Z is just an F statistic with the variance/se(variance). Here are my two questions: 1) lacking access to Pinhero and Bates at the moment, what is the appropriate model structure in lmer or nlme for this model (year and region fixed, group random nested within fixed region effect, and sample nested within group? 2) How can I obtain s.e(variance) to obtain Wald's Z? Thanks! -Dan Bolnick SAMPLE...

...dummy coding?). My specific questions are: 1. Is my lmer formula correct? If not, how should it be stated? 2. Should I use another formula or command to estimate the interaction component? 3. Do I have to create a fixed factor in order to correctly use lmer()? I know I should have consulted Pinhero?s book, but I?m currently in Costa Rica, and the book is taking a long time to get here. Thanks in advance for all your help. I?m new in the mailing list, and I hope I?ve made myself clear enough. thanks Eric Fuchs Universidad de Costa Rica -- Eric J. Fuchs Biologo http://e.j.fuchs.googlepag...

...) for crosses between males and females from two populations of salamanders. The idea is to fit a fixed effect of Cross, and estimate random effects for females and males. (data available as an *.rda for anyone who wants it...) Following the advice from various postings from R-help and from Pinhero and Bates, I can successfully (I think) code an lme() model, using pdBlocked, pdIdent and a dummy grouping variable in a grouped data object. > load("~/RbinaryData.rda") > dd<-data.frame(RbinaryData,dum=factor(rep(1,120))) > names(dd) [1] "Cross" "Fe...