similar to: geepack installation problem?

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:

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

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, Let's say one has an object with multiple classes, and a generic function to apply to it has associated S3 methods for more than one of those classes. Further, the method it chooses (I do not know how; some order in the class vector?) is not the suitable one and it produces an error. Would there be some way to make the generic function to choice the correct method, or in case that for

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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

Dear R users I am analysing data from a group of twins and their siblings. The measures that we are interested in are all correlated within families, with the correlations being stronger between twins than between non-twin siblings. The measures are all calculated from survey answers and by definition have limited ranges (e.g. -5 to +5), though within the range they are approximately normally

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:

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

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 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

I am trying to create a loop that will perform a series of analyses. I am using geeglm from geepack, which fails if there are any null values. Creating a subset solves this, but do not seem to be able to set the subset dynamically based on a changing variable. while (j <= y.num) { strSubset = as.character(df.IV$IV[j]) #Gives column name in quotes df.data.sub = subset(df.data,

I'm trying to fit a generalized mixed effects model to a data set where each subject has paired categorical responses y (so I'm trying to use a binomial logit link). There are about 183 observations and one explanatory factor x. I'm trying to fit something like: (lmer(y~x+(1|subject))) I also tried fitting the same type of model using glmmPQL from MASS. In both cases, I get a

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

Dear R users, I included 2 internal functions in the package 'dlnm', called 'mkbasis' and 'mklagbasis'. Despite they are not meant to be called by the users, I included them in the namespace in order to make them available, keeping the process more transparent and giving the opportunity to change or improve them. I included an help page 'dlnm-internal.Rd' to

Dear R users, I included 2 internal functions in the package 'dlnm', called 'mkbasis' and 'mklagbasis'. Despite they are not meant to be called by the users, I included them in the namespace in order to make them available, keeping the process more transparent and giving the opportunity to change or improve them. I included an help page 'dlnm-internal.Rd' to

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

*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

I have a generalized linear model to solve. I used package "geepack". When I use the correlation structure "unstructured", I get a messeage that- R GUI front-end has stopped working. Why this happens? What is the solution? The r codes are as follows: a<-read.table("d:/bmt.txt",header=T")

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