similar to: likelihoods in SAS GENMOD vs R glm

Hello, I have to run a general linear mixed model which looks at 2 dependent variables at the same time (var1 divided by var2). I have tryed to search for such a kind of model structure but since I just started using R my search was not successful. Especielly since I only have an old SAS GENMOD code structure from my project supervisor as an indication. My question is no, does there exist a code

Hello, I have different results from these two softwares for a simple binomial GLM problem. >From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59, coeff(x)=0.95 >From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99, coeff(x)=1.36 Is there anyone tell me what I did wrong? Here are the code and results, 1) SAS Genmod: % r: # of failure % k: size of a risk set data

Der R-Gurus, first apologies if this is a FAQ, but I due to lack of R-knowledge and terminology I wasn't able to find it. I have the following problem in aggregating results of a model calculation: The results are yearly values of several parameters with several hierarchical spatial factors taken from a database as a data frame with the following structure value | year | spatial1 | spatial2

I need capabilities, for my data analysis, like the Pinheiro & Bates S-Plus/R package nlme() but with binomial family and logit link. I need multiple crossed, possibly interacting fixed effects (age cohort of twin when entered study, sex of twin, sampling method used to acquire twin pair, and twin zygosity), a couple of random effects other than the cluster variable, and the ability to

Hi, R-users Could someone help me to understand this following error. I'm using vglm function in VGAM package Best regards and thank you for your ehlp ######## mydata <- read.table("Data2_overruns.csv", sep =";", header = T, row.names=NULL) > attach(mydata) > > y <- mydata$cat.event > phase.vol <-mydata$phase.vol > pilote <- mydata$pilote