similar to: equivalent to SAS genmod code in R?

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

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

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

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've been trying to get comfy with arima() and associated functions in the ts() package. I'm thinking seriously about using this package, and R generally, in a 4th year intro time series course that I'm teaching this autumn. I have a couple of questions about arima: (1) The help file says that residuals component of the value returned by arima() consists of the

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]]

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

Greetings. I'm trying to recreate in R some regression models I've done in SAS, but I'm not getting the same results. My advisor suspects this may be due to differences in precision between R and SAS. Does anyone know where I can find specifications for R's type double? (It doesn't seem to be in the R Language Definition.) Thanks in advance for any help anyone can

I have a dataset at a hospital level (as opposed to the patient level) that contains number of patients experiencing events (call this number y), and the number of patients eligible for such events (call this number n). I am trying to model logit(y/n) = XBeta. In SAS this can be done in PROC LOGISTIC or GENMOD with a model statement such as: model y/n = <predictors>;. Can this be done

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

    Hello Everyone,   I'm trying to use SAS to replicate some results obtained in R. I was wondering if anyone call tell me the SAS equivalent of the code that appears below.   fm.glm.x <- glm(resp ~ . - 1, data = as.data.frame(mm.x),  na.action = na.exclude, family = binomial(link = "probit")) summary(fm.glm.x) Thanks,   Paul [[alternative HTML version deleted]]

Hi, we try to do a logistic regression with the function glm. But we notice that this function don't give the same results as the SAS proc catmod (differents estimate given). We try to change the contrast on R system with: > options(contrasts=c(unordered="contr.SAS",ordered="contr.poly")) We also try with brlr and logistf functions. Unfortunately, the estimate

Dear friends, I used R and SAS to analyze my data through generalized linear model, and there is some difference between them. Results from R: glm(formula = snail ~ grass + gheight + humidity + altitude + soiltemr + airtemr, family = Gamma) Deviance Residuals: Min 1Q Median 3Q Max -1.23873 -0.41123 -0.08703 0.24339 1.21435 Coefficients:

bonjour, Je suis actuellement en derniere annee d'ecole d'ingenieur en informatique et statistiques et je dois réaliser mon projet de fin d'études sur le logiciel R. En fait, je dois réaliser un scoring sous R puis le meme sous SAS et comparer les resultats. Mon fichier se prete à une regression logistique. J'ai donc utilisé la fonction glm sous R et Catmod sous SAS seumement, je

Hello R-users: I am using "yags" for fitting GEE which is giving me the same result as "Proc GENMOD". Now I have couple of questions related to yags output. (By the way, someone told me to run the geeglm for the same analysis and I did run but did not get the same result as of genmod and don't know how to correct the geeglm codes so that all three will be same!)

Hi everyone, newbee query! I've installed R 2.8.0 and tried to run this simple glm - x is no of cars in a given year, y is the number voted in an election that year while n is the population 18+: votes <- data.frame(x = c(0.62,0.77,0.71,0.74,0.77,0.86,1.13,1.44), + y=c(502,542,711,653,771,806,934,1123), n=

Hi to all, So I'm also a new user. I downloaded the program last week and I think it's great. Thanks to those who have developed R. I have a special interest in GLM as a tool to describe fisheries and its variables and I'm just begging to study it. As I could understand there's two types of GLM sun of squares: in "type I" the factors are added in sequence and

Hi to All, I am building a package in R and whenever I run command "R CMD build OAR" in the terminal, I get the following error: * checking for file ?OAR/DESCRIPTION? ... OK * preparing ?OAR?: * checking DESCRIPTION meta-information ... ERROR Malformed Depends or Suggests or Imports or Enhances field. Offending entries: R (>=3.0.2) Entries must be names of packages optionally