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Hi, All,   My data collection is from 4 regions (a, b, c, d). Within each region, it has 2 or 3 units. Within each unit, it has measurement from about 25 sample site. I was trying to use lme function to discribe relationship between y and a few covariates. Both y and covariates were measured at the sample site level. My question is when I use exactlly the same model but choose different estimation

Dear all, I've got some questions, probably due to misunderstandings on my behalf, related to fitting overdispersed binomial data using glm(). 1. I can't seem to get the correct p-values from anova.glm() for the F-tests when supplying the dispersion argument and having fitted the model using family=quasibinomial. Actually the p-values for the F-tests seems identical to the p-values for

I tried to bootstrap the correlation between two variables x1 and x2. The resulting distribution has two distinct peaks, how should I interprete it? The original code is attached. Y. C. Tao ---------------- library(boot); my.correl<-function(d, i) cor(d[i,1], d[i,2]) x1<-c(-2.612,-0.7859,-0.5229,-1.246,1.647,1.647,0.1811,-0.07097,0.8711,0.4323,0.1721,2.143,

Hi, I have just tagged 3.7.1-rc1, so it is ready for testing. As a reminder, when doing regression testing, use the 3.7.0 release as your baseline. Thanks, Tom

I have found the use of save( ) and attach( ) when supported by a pair of functions written by my colleague John Miyamoto, move( ) and rm.sv( ) quite useful in managing (1) collections of useful homebrew functions, (2) project workspaces, and (3) "packages" under development. An .Rdata file containing these and other handy functions together with a brief supporting document can be