similar to: GEEPACK vs GEE: What are the differences in the estimators calculated by geeglm() (GEEPACK) and gee() (GEE)?

Hi, having used both the gee pacakge and the geepack package, i am unsure of how to interpret the results. Here are the results from the geeglm function from the geepack package > gee2<-geeglm(data$erythema~data$product, data = data, id=subject, > family=binomial, corstr="independence") Warning message: In model.response(mf, "numeric") : using

Hi A quick question. Standard errors reported by gee/yags differs from the ones in geeglm (geepack). require(gee) require(geepack) require(yags) mm <- gee(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable") mm2 <- geeglm(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable", std.err = "san.se") mm3 <- yags(breaks ~

*H*i, I am trying to fit a GEE model with *geeglm* function. The model is the following: Modelo<-geeglm(sqrt ~Tra+ Mes, id=Lugar , data=datos, family=gaussian(identity), corstr="independence") *Tra( is a experimental treatment, 2 levels)*, *Mes* (is the month of take data, 4 levels) and *Lugar* (is the site of study, 3 levels) are categorical variables and *sqrt* (sqrt of Total

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Hello R Forum members. I have installed for my statistician user, apparently without error, both the concord and geepack packages. The target system is R 2.10.1 on a 64-bit RedHat Enterprise Linux platform. However when she attempts to invoke a function in geepack, for example... geeglm((abuse_total ~ case),id=mother, family="poisson") the resultant error is Error: could not find

Dear R-users, I was doing some Poisson regression using the geepack package and I found a "strange" behaviour of the geeglm command when defining an offset. Maybe it's my limited knowledge of R syntax, or maybe it's something else. Let's make an example. After loading the geepack library, you may write

I'm using the package geepack to fit GEE models. Does anyone know of methods for add1 and drop1 for a 'geeglm' model object, or perhaps a method for extractAIC based on the QIC of Pan 2001? I see there has been some mention of this on R-help a few years ago (RSiteSearch("QIC")). The package does provide an anova method for its model objects, and update() seems to work:

Hi, I downloaded both gee and geepack, and I am trying to understand the differences between the two libraries. I used the same data and estimated the same model, with a correlation structure autoregressive of order 1. Surprisingly for me, I found very different results. Coefficients are slightly different in value but sometimes opposite in sign. Moreover, the estimate of rho (correlation

Dear all, I need to fit a gee model with an auto-regressive correlation structure and I faced some problems. I attach a simple example: ####################################################### library(gee) library(geepack) # I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS set.seed(1) y <- rpois(500,50) x <- rnorm(500) id <- rep(1:50,each=10) # EXAMPLES FOR

Hi all, I am trying to do a generalized estimating equation (GEE) with the "geepack" package and I am not 100% sure what exactly the "id" argument means. It seems to be an important argument because results differ considerably defining different clusters. I have a data set of counts (poisson distribution): numbers of butterfly species counted every month during a period of

event.nab.2 is 0/1 and I dichotomized va to get va.2 to see if I could get geeglm to work. glm has no problem with the data but geeglm chokes. Each subject (patient.id) has at most 2 observations and more than 3/4 of the subjects have 2 observations. I have even worse problems trying to use glmmPQL from MASS and worse still trying to use lmer from lme4. But I figured a marginal model would work.

Dear all, We have got data (response and predictor variables) for each country of the world; I started by fitting standard GLM and tested for spatial correlation using variogram models (geoR) fitted to the residuals of the GLM. Spatial autocorrelation is significant. Therefore, I think about using general estimation equations (geeglm or geese in geepack) allowing for residual spatial

Dear all, Is it possible to incorporate a nested design in GEE? I have measurements on trees that where measured in two years. The trees are nested in plots. Each plot contains 24 trees. The number of plots is 72. Hence we would expect 2 * 24 * 72 = 3456 data points. A few are missing, so we end up wih 3431 data points. This is what I have tried until now. #assuming independence between trees

Hi, I'm running GEE using geepack. I set corstr = "ar1" as below: > m.ar <- geeglm(L ~ O + A, + data = firstgrouptxt, id = id, + family = binomial, corstr = "ar1") > summary(m.ar) Call: geeglm(formula = L ~ O + A, family = binomial, data = firstgrouptxt, id = id, corstr = "ar1") Coefficients:

I have included data at the bottom of this email. It can be read in by highlighting the data and then using this command: dat <- read.table("clipboard", header = TRUE,sep="\t") I can obtain solutions with both of these: library(gee) fit.gee<-gee(score ~ chem + time, id=id, family=gaussian,corstr="exchangeable",data=dat) and library(yags) fit.yags <-

Hello Fellow R Users,I have spent the last week trying to find a work around to this problem and I can't seem to solve it. I simply want to plot my GEE model result with 95% confidence bands. I am using the geepack package to run a basic GEE model involving nestling weights, to a Gaussian distribution, with "exchangeable" error structure. I am examining how nestling weight varies

Hi Does anyone know how to install geepack on fedora 16 - 64bit - R google only yields a couple of hits with unanswered Q Installation just throws an error with an undefined symbol. Previous version won't load due to lack of NAMESPACE. Sadly, its a show stopper w.r.t. upgrading my work PCs Any hints/suggestions most welcome Thanks Cheers Peter

Dear R users Recently I received advice from this fine group on gee() and sample weights One suggestion was to use geeglm() I hope someone can help me to solve a problem that arises when converting a code from gee to geeglm. *Here is a code that I wrote with the original data, not weighted: * > m1 <- gee( Bin ~ educ+agemean+ residencysize + yearx , id = rad09 , data = Males,

Hi there, I just installed the package under windows, and it works very well. However, as I tried to install it on a cluster which uses linux OS, "Linux lionxc.aset.psu.edu 2.6.18-194.el5 #1 SMP Tue Mar 16 21:52:39 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux" I always failed. The message during the installation process is as follows: > install.packages("geepack") Warning

Dear R users, I fitted a GEE model using the function 'geese' (or 'geeglm') with user defined correlation matrix. I want to get the var-cov matrix of the regression coefficients. But the output provides only limited information. I would be very much thankful if you could kindly let me know how to get it..since I am struggling lot getting this. Thanks -- View this message in