similar to: Problem with geeglm

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 trying to use the GEE methodology to fit a trend for the number of butterflies observed at several sites. In total, there are 66 sites, and 19 years for which observations might be available. However, only 326 observations are available (instead of 1254). For the time being, I ignore the large number of missing values, and the fact that GEE is only valid under MCAR. When I run the

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

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

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

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 ~

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 <-

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

*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

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

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()'

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

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 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

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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Dear Iago, The R S3 object system works as expected here, using the first available method processing the class vector from left to right. The problem is that the broom package doesn't export the confint.geeglm() method but rather reserves it for internal use. I can't think why the package authors chose to do that but you could ask them. The following therefore works (following on with

Hello R-users: I am using "yags" for fitting GEE which is giving me the same result as "Proc GENMOD". Now I have couple of questions related to yags output. (By the way, someone told me to run the geeglm for the same analysis and I did run but did not get the same result as of genmod and don't know how to correct the geeglm codes so that all three will be same!)