similar to: strange gee behavior

Dear all, I am working on modifying the R working matrix to commodate some other correlations that not included in the package. I am having problem to locate where the R matrix are defined for regular matrices, i.e. independence, exchangeable, AR and unstructure. it might have something within .C("Cgee",but don't understand it well enough to know. Can you anyone help? /*gee source

I'm learning to use R/Sweave/LaTeK to write my stat reports. Is there a way to have an executive summary in the beginning while still having the analysis code embedded? For example, a study has three independent objectives so I want my report to have three sections. Ideally the code chunk that answers the first objective would be embedded in the first section and so on.

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

Using the 64-bit Solaris compilers make check dumps core in La_rs at 73 F77_CALL(dsyev)(jobv, uplo, &n, rx, &n, rvalues, work, &lwork, &info); I can't reproduce it easily, but example(eigen) occasionally stops with Error: abs(sm - V %*% diag(lam) %*% t(V)) < 60 * Meps is not TRUE which might be connected. The tiny rounding errors in example(eigen) aren't

hello, i want to compute the top k eigenvalues+eigenvectors of a (large) real symmetric matrix. since it doesn't look like any top-level R function does this, i'll call LAPACK from a C shlib and then use .Call. the only LAPACK function i see to do this in R_ext/Lapack.h is dsyevx. however, i know that in LAPACK dsyevr can also return a partial eigendecomposition. why is dsyevr not

hello, i want to compute the top k eigenvalues+eigenvectors of a (large) real symmetric matrix. since it doesn't look like any top-level R function does this, i'll call LAPACK from a C shlib and then use .Call. the only LAPACK function i see to do this in R_ext/Lapack.h is dsyevx. however, i know that in LAPACK dsyevr can also return a partial eigendecomposition. why is dsyevr not

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

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

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

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

Hi, I would like to fit a GLM model with GEE on clustered data. I tried to use gee in the GEE package on a twin data set. All cluster are of size 2. I removed the missing data and ordered by IDENTIF2 first. library(gee) mod.pc <- gee(Y ~ X1 + X2 , id = IDENTIF2, family = binomial, corstr = "unstructured", data = na.omit(df)) gives the following result : Beginning Cgee S-function,

Behavioral science data is often collected from nested structures (students in schools, in districts, etc.). This can produce nonindependence among responses from individuals in the same groups. Consequently, researchers are advised to model the nested nature of the data to avoid biases in SE estimates. Failing to account for nonindependence can lead to SE estimates that are too large or too

Dear R-help, I was recently wanting to use GEE with the negative binomial "family". It seems that this is lacking in the otherwise excellent implementations of the GEE methodology ( packages: gee, yags, geepack). I would have thought it a simple step to allow the creation of a family, i.e providing the link function (log mu) and the variance function (mu + mu^2/theta) , assuming theta

A bug (misfeature) has been found in the S gee library (and thus in the R gee library). The problem, which is shared by nearly all gee implementations, involves the calculation of working correlations when some clusters have only one observation. For compatibility reasons nearly everyone uses the computing formula from the first SAS macro by Karim, rather than the formula from the original GEE

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 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 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

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 =

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.