similar to: Quasipoisson with geeglm

event.nab.2 is 0/1 and I dichotomized va to get va.2 to see if I could get geeglm to work. glm has no problem with the data but geeglm chokes. Each subject (patient.id) has at most 2 observations and more than 3/4 of the subjects have 2 observations. I have even worse problems trying to use glmmPQL from MASS and worse still trying to use lmer from lme4. But I figured a marginal model would work.

Dear R users Recently I received advice from this fine group on gee() and sample weights One suggestion was to use geeglm() I hope someone can help me to solve a problem that arises when converting a code from gee to geeglm. *Here is a code that I wrote with the original data, not weighted: * > m1 <- gee( Bin ~ educ+agemean+ residencysize + yearx , id = rad09 , data = Males,

Dear R users, could you please help me figure out why I am getting an error? Initially my data looks like this: > attributes(compl)$names [1] "UserID" "compl_bin" "Sex.x" "PHQ_base" "PHQ_Surv1" "PHQ_Surv2" "PHQ_Surv3" [8] "PHQ_Surv4" "EFE"

Dear all I am struggling with how to deal with missing values using geeglm. I know that geeglm only works with complete datasets, but I cannot seem to get the na.omit function to work. For example assuming DataMiss contains 3 columns, each of which has missing observations, and an id column with no missing info then identifies the clusters. Outcome: 2 level integer Predictor: numeric variable

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

*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

Dear all, We have got data (response and predictor variables) for each country of the world; I started by fitting standard GLM and tested for spatial correlation using variogram models (geoR) fitted to the residuals of the GLM. Spatial autocorrelation is significant. Therefore, I think about using general estimation equations (geeglm or geese in geepack) allowing for residual spatial

Hi A quick question. Standard errors reported by gee/yags differs from the ones in geeglm (geepack). require(gee) require(geepack) require(yags) mm <- gee(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable") mm2 <- geeglm(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable", std.err = "san.se") mm3 <- yags(breaks ~

Hi, I'm using geeglm function to account for the repeated measure. fit1<- geeglm( binary.outcome ~ age + race + gender + fever.yes.no, data=mydata, id=ID, family=binomial, corstr="exchangeable") summary(fit1)$coef gives too large estimates and standard deviation: Estimate Std.err Wald Pr(>|W|) (Intercept) 3.07e+16

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Hi, I managed to make R core dump (linux and Mac OSX versions), but I think I've figured out why. First, here's the message I get on core dump (on linux - no message on Mac): R: ../inst/include/tnt/fmat.h:529: TNT::Vector<T> TNT::matmult(const TNT::Fortran_Matrix<T>&, const TNT::Vector<T>&) [with T = double]: Assertion `A.num_cols() == x.dim()'

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 fitted a GEE model using the function 'geese' (or 'geeglm') with user defined correlation matrix. I want to get the var-cov matrix of the regression coefficients. But the output provides only limited information. I would be very much thankful if you could kindly let me know how to get it..since I am struggling lot getting this. Thanks -- View this message in

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 (

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Hi all I am analyzing a data set containing information about the behaviour of marine molluscs on a vertical wall. Since I have replicate observations on the same individuals I was thinking to use the geepack library. The data are organised in a dataframe with the following variables Date = date of sampling, Size = dimensions (mm) Activity duration of activity (min) Water = duration of

I have included data at the bottom of this email. It can be read in by highlighting the data and then using this command: dat <- read.table("clipboard", header = TRUE,sep="\t") I can obtain solutions with both of these: library(gee) fit.gee<-gee(score ~ chem + time, id=id, family=gaussian,corstr="exchangeable",data=dat) and library(yags) fit.yags <-

Hi, I have count data that have been meddled with enough to make them non integers. Using glm(poisson) returns a "non integer" error but glm(quasipoisson) does not. Just wondering if anyone knows if I am violating the assumptions of a quasipoisson error structure by using these non-integer response data? Thanks! I'd welcome your thoughts and/or references... Mark

Hi, Got my issues sorted. Error message solved: I heard from the guy who developed MuMIn and his suggestion worked. "As for the error you get, it seems you are running an old version of MuMIn. Please update the package first." I did (I was only 1 version behind in both R and in MuMIn) and error disappeared! Running quasipoisson GAM in MuMIn: As for my questions on GAM and " to

Dear fellows, I'm trying to extract the AIC statistic from a GLM model with quasipoisson link. The formula I'm referring to is AIC = -2(maximum loglik) + 2df * phi with phi the overdispersion parameter, as reported in: Peng et al., Model choice in time series studies os air pollution and mortality. J R Stat Soc A, 2006; 162: pag 190. Unfortunately, the function logLik