search for: gees

I was trying to learn cross building an R pcakage for windows on my linux machine. I picked a relatively small library gee to test. I downloaded the cross-tools and put them in my path, jyan at ludwig:/a3/jyan/src/R-1.2.1/src/gnuwin32$ echo $PATH /home/jyan/cross-tools/bin:/home/jyan/cross-tools/i386-mingw32msvc/bin: /usr/lib:/usr/local/bin:/usr/bin:/bin:/usr/bin/X11:/usr/games Following the

Greetings, I have a very, very, simple research question. I want to predict one dichotomous variable using another dichotomous variable. Straightforward, right? The issue is that the dataset has two issues causing some complications for me. 1) The subjects are not independent -- they are sibling pairs. Every person in the dataset has a sibling in the dataset. This needs to be treated a

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

Hi, I'm attempting a comparative analysis using the function compare.gee, package (ape), which uses phylogeny as a correlation matrix in gee (package gee), in R version 2.2.0 on a Windows XP PC. I'm looking to model the relationship between a continuous explanatory variable and a binary response variable for 600 species, taking into account the phylogeny of those species. Here's

Hi, I am fitting a GEE model using gee R package, but I am not sure how to get OR and its CI? Could anyone give me some hints? Here are some output: > gee.obj <- gee(Affection~Sibsex+Probandsex,id = FAMID,family = binomial,corstr = "independence",data =seldata) Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 running glm to get initial regression estimate

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

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 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 (

I'm not sure if the gee package is still actively maintained, but I for one find it extremely useful. However, I've come across a few infelicities that I'm hoping could be resolved for future versions. Hope it's okay to list them all in one post! They are: (1) AR(1) models don't fit when clustsize = 1 for any subject, even if some subjects have clustsize > 1. (2) If the

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 a dataset in which the output Y is observed on two groups of patients (treatment factor T with 2 levels). Every subject in each group is observed three times (not time points but just technical replication). I am interested in estimating the treatment effect and take into account the fact that I have repeated measurements for every subject. If I do this with repeated measures

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,]

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,

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

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, 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

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 <-