similar to: Inverse Gaussian dist in a GEE model

Hello, I am interested in running generalized estimating equation models in R. Currently there are two main packages for doing so in R, geepack and gee. I understand that even though one can obtain similar to almost identical results using either of the two, that there are differences between the packages. The paper that introduces the geepack package (

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

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

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 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 ~

Hi, I have a bivariate longitudinal dataset. As an example say, i have the data frame with column names var1 var2 Unit time trt (trt represents the treatment) Now suppose I want to fit a joint model of the form for the *i* th unit var1jk = alpha1 + beta1*timejk + gamma1* trtjk + delta1* timejk:trtjk + error1jk var2 = alpha2 + beta2*timejk + gamma2* trtjk + delta2* timejk:trtjk +

Hi, I am trying to fit a ordinal GEE model using ordgee {geepack}. In order to check the validity of the function, I specified the correlation structure as independence (i.e. constr = "independence") and compared the result with that using polr {MASS}. Because a GEE model with an independent working correlation structure is equivalent to an ordinary GLM model, we would expect the same

Hello, I want to fit a GEE with a user-defined link function. For the user-defined link-function I still read http://finzi.psych.upenn.edu/R/Rhelp01/archive/6555.html and http://finzi.psych.upenn.edu/R/Rhelp02a/archive/25727.html. Only for testing purposes I added a new link function (corlogit) in make.link (as well as in binomial) with exactly the same code as logit before using my code.

Hello, I have the following r-codes for solving a quasilikelihood estimating equation: >library(geepack) >fit<-geese(y~x1+x2+x3,jack=TRUE,id=id,scale.fix=TRUE,data=dat,mean.link = "logit", corstr="independence") Now my question is how can I calculate the confidence interval of the parameters of the above model "fit"? [[alternative HTML version deleted]]

Hello, I would like to try to fit a GEE with user-specified link function. I read through a couple of thread on the list, for example http://tolstoy.newcastle.edu.au/R/help/04/12/9768.html#start and http://tolstoy.newcastle.edu.au/R/help/06/04/25298.html. I noticed that they are all 6 or more years old and the answer is very clear for GLM, however for GEE I am still not sure. There are two

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:

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

Two questions: 1. inverse.gaussian is up there as one of the glm families, but do people ever use it? There is no inverse.gaussian in the R distribution family, and when I checked McCullagh & Nelder, it only appeared twice in the book (according to subject index), once in the table on p. 30 and once on p. 38 in a passing sentence. Is there a good reference on this distribution? 2. When I

Hi, I'm using 'geepack' to run Generalized Estimating Equations. I'm aware that I can use anova to compare two models, but would it be possible to test QIC on R? It seems that there were similar questions a couple of years ago, but the question has not been answered yet. I'd appreciate if someone could show me the code! Thank you, Sachi [[alternative HTML version

Hi, I want to fit a GEE with a negative binomial distribution. I have uesd already a poisson glm and then neg binommial to deal with alot of dispersion. In my neg binomial residuals i have some patterns so i have implemented a GEE, but only with a poisson family as i couldnt with neg binomial. However the residual patterns in fact look worse here. When i try and put neg binomial family it

Hello, Is it possible to derive Pan's QIC (2001 Biometrics 57:120) from either a fitted gee() object in the gee package or from a geese() fit in the geepack package? If so, would anyone be kind enough to provide me with code to do so? I realize that QIC is part of the output from yags() but I would like to use one of the other functions. Thanks. Richard

Hi , I use library(gee),library(geepack),library(yags) perform GEE data analysis , but all of them cannot do variable selection! Both step and stepAIC can do variable selection based on AIC criterion under linear regression and glm, but they cannot work when model is based on GEE. I want to ask whether any variable selection function or package under GEE model avaliable now? Thanks! Best,

Hi, I have a variable (current speed direction) which is circular (0=360 degrees), and I'd like my GLM to include the variable as a circular variable. Can I do this? And what is the code? I'm actually doing a GLM-GEE using the 'geepack' package, so want to use it in that, but also interested in whether it can also be used in GLMs and GAMs (I use the 'mgcv' package for

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 <-

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