similar to: Genmod in SAS vs. glm in R

List: I'm helping a colleague with some Poisson regression modeling. He uses SAS proc GENMOD and I'm using glm() in R. Note on the SAS and R output below that our estimates, standard errors, and deviances are identical but what we get for likelihoods differs considerably. I'm assuming that these must differ just by some constant but it would be nice to have some confirmation

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

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

Hey folks, Some good news. I''ve finished cFerret and it''s ruby bindings to the point where I can run all of the unit tests. I still have to work out how I''m going to package and release it but it shouldn''t be long now. If you can''t wait you might like to try it from the subversion repository. It''ll probably only work on linux at the moment and

I use "while" loop but it produces an errro. I have no idea about this. Error in "[<-"(`*tmp*`, i, value = numeric(0)) : nothing to replace with The problem description is The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0

Hello Everybody: I am searching for an R package for fitting Generalized Estimating Equations (GEE) with weights (i.e. Weighted Estimating Equations). From the R documentation I found "geese(geepack)" for fitting Generalized Estimating Equations. In this documentation, under the paragraph “weights” it has been written, “an optional vector of weights to be used in the fitting process.

Hello. I am attempting to duplicate a negative binomial regression in R. SAS uses generalized estimating equations for model fitting in the GENMOD procedure. proc genmod data=mydata (where=(gender='F')); by agegroup; class id gender type; model count = var1 var2 var3 /dist=NB link=log offset=lregtm; repeated subject=id /type=exch; run; Since my dataset has several observations for

I am trying to figure out how to apply a loglog link to a binomial model (dichotomous response variable with far more zeros than ones). I am aware that there are several relevant posts on this list, but I am afraid I need a little more help. The two suggested approaches seem to be: 1) modify the make.link function in GLM, or 2) use the loglog or cloglog functions in the VGAM package.

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and this generates an error for me: >

I would like to do an R glm() with family = binomial(link="loglog") Right now, the cloglog link exists, which is nice when the data have a heavy tail to the left. I have the opposite case and the loglog link is what I need. Can someone suggest how to add the loglog link onto glm()? It would be lovely to have it there by default, and it certainly makes sense to have the two opposite

Dear all, I'm working with count data following over-dispersed poisson distribution and have to work with mixed-models on them (like proc GENMOD on SAS sys.). I'm still not to sure about what function to use. It seems to me that a glmmPQL will do the job I want, but I'll be glad if people who worked on this type of data can share what they learned. Thanks for your time. simon

Hi All, I'm using glm() in R to perform Poisson regression, I'm wondering if its possible to get equivalent Type 1 / Type 3 Analysis (similar to one in PROC GENMOD). Thanks, Kim [[alternative HTML version deleted]]

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n <- 10 N <- 1000 DF <- data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit <- GLMM(yield~1, random=~1|nest, family=binomial, data=DF, weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect

We are using glm() to models to counts of deaths due to rare causes using a log link and Poisson error distribution, with population as the offset. Approximate confidence intervals for the parameter estimates are easy to calculate using a standard normal deviate, but obviously when the counts of deaths are small (which is why we are using Poisson regression), these intervals are very approximate

Hello, I'm not a Stata user so I'm trying to reproduce Stata results that are given to me in R. I would like to use a GLM with a complementary log-log function. The stata code I have is: glm c IndA fia, family(binomial s) link(cloglog) offset(offset) The R code is: glmt <- glm(data=dataset, c ~ IndA + fia, offset = offset, family = binomial(link = cloglog)) Which yields

I would like to get horizontal numbers on the both axes: X and Y. I got horizontal numbers only on the Y axis when adding las=2, How to obtain a horizontal orientation for number on scale also for the X axis (now they are vertical)? Here is my code: plot(survfit(Y~addicts$clinic), fun="cloglog", las=2) [[alternative HTML version deleted]]

Hello everybody, does anybody know how the function plot.survfit exactly works? I'd like to plot the log of the cummulative hazard against the log time by using plot.survfit(...fun="cloglog") which does not work correctly. The scales are wrong and there is an error message about infinit numbers. It must have something to do with the censored data, doesn't it? #Example:

Hello, The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updataed alpha, replicate 1) and 2) until alpha is converged to a value I coded 1) and 2) (it works), but faced some

Hi, I would like apply different link functions from Aranda-Ordaz (1981) family to large binary dataset (n = 2000). The existing links in glm for binomial data (logit, probit, cloglog) are not adequate for my data, and I need to test some other transformations. Is it possible to do this in R? And how? Thank you for your help, /Sharon

Just a quick point which may be easy to correct. Whilst typing the wrong thing into R 2.2.1, I noticed the following error messages, which seem to have some stray quotation marks and commas in the list of available families. Perhaps they have been corrected in the latest version (sorry, I don't want to upgrade yet, but it should be easy to check)? > glm(1 ~ 2,