similar to: question about glm behavior

I have some data (from Affymetrix experiments) where I fit an aov() model to a large number of outcome variables. A reasonable fraction of the outcome variables have 0 variability because values below a cutoff have been replaced with the cutoff value (often 20) . In this case, the overall p-value from summary(lm(..)) is misleadingly small: For example (equivalent results in R 1.6.1 on both

Dear Sir/Ma, I Adelabu.A.A, one of the R-users from Nigeria. When am running a coxph command the below error was generated, and have try some idea but not going through. kindly please assist: > cox1 <- coxph(Surv(tmonth,status) ~ sex + age + marital + sumassure, X) Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not

This command cdmoutcome<- glm(log(value)~factor(year) > +log(gdppcpppconst)+log(gdppcpppconstAII) > +log(co2eemisspc)+log(co2eemisspcAII) > +log(dist) > +fdiboth > +odapartnertohost > +corrupt > +log(infraindex) > +litrate > +africa >

Hi, Over the past six months, a MemorySSA-backed DSE implementation has been added to LLVM and it now covers almost all cases the existing DSE implementation does, plus adding a major new capability: eliminating stores across basic blocks. Thanks everyone involved with reviews, testing & patches! I think now would be a good time to start working towards switching to use MemorySSA-backed DSE

I've got a list of 5 matrices that are each 5 x 6. I'd like to end up with a 5 x 6 matrix that contains the mean value of the 5 original matrices. I can do this by brute force, but there must be a better way than making each matrix into a vector and then remaking a matrix thanks very much for any help david freedman ll=list(structure(c(9.7, 17.6, 20.8, 24.1, 33.8, 14.5, 25.7, 29.8,

Hi, I have a question about estimating IRT models by using nlme, not just rasch model, but also other models. Behavior Research Methods <http://www.springerlink.com/content/1554-351x/> Volume 37, Number 2 <http://www.springerlink.com/content/1554-351x/37/2/>, 202-218, DOI: 10.3758/BF03192688 Using SAS PROC NLMIXED to fit item response theory models (2005). Ching-Fan