similar to: Create subset using variable

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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:

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

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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

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

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:

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

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 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

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

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

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")

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

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Hi A.K Regarding my question on comparing normal/ obese/overweight with blood pressure change, I did finally as per the first suggestion of stacking the data and creating a normal category . This only gives me a obese not obese 14, but when I did with the wide format hoping to get a obese14,normal14,overweight 14 Vs hibp 21, i could not complete any of the models. This time I classified obese=1

Dear R community I am using the gee package to run logistics regression on paired cases from a panel sample. We are getting request from a reviewer to use sample weights to account for different level of attrition. After searching the documentation I am unable to find a way to incorporate sample weights into the gee formula. I found a way to incorporate precision weight but I understand that I

Hello R-helpers, I would like to calculate the standard error for the predicted value from geeglm. As an example, I would like to calculate the GEE mean of treatments and their standard error. I first specified the model as mod <- geeglm(resp ~ trt, data=dat,id=id,family=Gaussian,corstr="ar1",weights=weight) Then I predicted the GEE mean and se using the following code