similar to: GEE with Inverse Probability Weights

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

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 List: I found an error when I called the 'gee' function. I cannot solve and explain it. There are no errors when I used the 'geeglm' function. Both functions fit the gee model. The project supervisor recommends me to use the 'gee' function. But I cannot explain to him why this error happens. Would you help me solve this problem? I appreciate your help. In

For a simulation study, I need to fit a GEE with a IG distribution and using a log link function. As far as I know, there are two GEE packages available ('gee' and 'geepack') but none of them supports IG. I've also tried using family=quasi("log","mu^3") (without luck!).' Any guidance is highly appreciated Cheers, Ren?

Hi, I am trying to install gee on our server but I get the error below. I do not have root on this machine so no control on how R was installed itself. It looks like it cannot find blas libs, the only ones i can find on the machine are: /usr/lib64/libblas.so.3 -> libblas.so.3.0.3 /usr/lib64/libblas.so.3.0 -> libblas.so.3.0.3 /usr/lib64/libblas.so.3.0.3 and : $ R CMD config BLAS_LIBS

windows Vista R 2.10.1 Is it possible to get p values from gee? Summary(geemodel) does not appear to produce p values.: > fit4<- gee(y~time, id=Subject, data=data.frame(data)) Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 running glm to get initial regression estimate (Intercept) time 1.1215614 0.8504413 > summary(fit4) GEE: GENERALIZED LINEAR MODELS FOR

Does anyone know how could I get the significant codes from mixed model output fitted with a GEE package? The output I got is the following: GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA gee S-function, version 4.13 modified 98/01/27 (1998) Model: Link: Logit Variance to Mean Relation: Binomial Correlation Structure: Exchangeable Call: gee(formula = bru

Hi all, I have fitted a gee model with the gee package and included restricted cubic spline functions. Here is the model: chol.g <- gee(SKIN ~ rcs(CHOLT, 3), id=ID, data=chol, family=binomial(link="logit"), corstr="exchangeable") To extract the log odds I use: predict.glm(chol.g, type = "link") Now I want to compute the logg odds for specific CHOLT values

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

Dear all, I don't understand the following behaviour: Running compar.gee (in library ape ) with and without the option 'data', it give me different results Example: .... Start R .... > load("eiber.RData") > ls() [1] "gee.na" "mydata" "mytree" > library(ape) > # runnig with the option data= mydata > compar.gee(alt ~ R,

Dear R-helpers, I am using the package "gee" to run a marginal model. Here is the output. In my simulated data, both x and z are time-varying, so I include their interaction terms with time indicator (i.e. tind=0, if time 1, and 1 if time 2) The data is simulated, so the true parameter of z both at time 1 and time 2 is 5, which is very close from the model output for time 1, z =

I'm using the function gee from the library(gee) gee(Y~X,id=clust.id,corstr="exchangeable",b=tmc$coef,family=binomial(link=logit),silent=T) Every time it runs, it dutifully prints out Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 user's initial regression estimate [,1] [1,] -4.5278335 [2,] -0.2737999 [3,] -0.9528306 [4,] 0.9393861 [5,]

Hi, my dependent variable is a proportion ("prob.bind"), and the independent variables are factors for group membership ("group") and a covariate ("capacity"). I am interested in the effects of group, capacity, and their interaction. Each subject is observed on all (4) levels of capacity (I use capacity as a covariate because the effect of this variable is normatively

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 all, 1. Can nonlinear models be used in gee? For example, I have a dataset which contains 2 variables x and y, I wrote data(ex) atttach(ex) a<-100 b<- -0.5 c<-4.5 d<-20 Then: a. y~gee(y~d+(a-d)/(1+(x/c)^b)) Error in terms.formula(formula, data = data) : invalid power in formula b. y~gee(y~d+(a-d)/(1+(x/c)) Error in model.frame(formula, rownames, variables, varnames, extras,

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

I am trying to learn the gee function in R. So I try to generate some data and use this function. I have the following lines: ######################################## Gee # Generating lny=10+2*Si-Si^2+eta # eta ~ N(0,1) # Si ~ U(0,11) eta <- vector(mode="numeric",100) eta <- rnorm(100) Si <- vector(mode="numeric",100) Si <- runif(100, min=0, max=11) lny <-

I'm running some GEE models but when I request the summary(pcb.gee) all I get are rows and rows of intercorelations and they fill up the screen buffer so I can not even scroll back to see what else might be in the summary. How do I get the summary function to NOT print the intercorrelations? Thanks, -- Dean Sonneborn Programmer Analyst Department of Public Health Sciences University of

Hello, I am trying to run the APE program COMPAR.GEE with a model containing a categorical response variable and a mixture of continuous and categorical independent variables. The model runs when I have categorical (binary) response and two continuous independent variables (VAR1 and VAR2), but when I include a categorical (binary) independent variable (VAR3), I receive the following output with

Hi, The standard error from logistic regression is slightly different from the naive SE from GEE under independence working correlation structure. Shouldn't they be identical? Anyone has insight about this? Thanks, Qiong a<-rbinom(1000,1) b<-rbinom(1000,2,0.1) c<-rbinom(1000,10,0.5) summary(gee(a~b, id=c,family="binomial",corstr="independence"))$coef