similar to: How to model repeated measures negative binomial data with GEE or GLMM

Hi folks, I love the Phia package andwant to use it with glmmTMB, but when i try to use the interactionMeans command, i get the below error modelrepeatedmain2 <- glmmTMB(counts ~ cluster*nominated*nominator*junior_senior+Ltime+ (1|school)+(1|id), data=d_shortf, family=nbinom1)

Dunno ... But you might do better posting this on the r-sig-mixed-models list where it both should fit better and where you are more likely to find the relevant expertise. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu,

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,

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 ~

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, 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

Hi, I'm running GEE using geepack. I set corstr = "ar1" as below: > m.ar <- geeglm(L ~ O + A, + data = firstgrouptxt, id = id, + family = binomial, corstr = "ar1") > summary(m.ar) Call: geeglm(formula = L ~ O + A, family = binomial, data = firstgrouptxt, id = id, corstr = "ar1") Coefficients:

I'm writing a paper aimed at motivating the use of GEE within the field of economics. However, after computing using the geeglm function, I noticed there's one intercept in the summary output. I assume this means the function is pooling the data. That means my code is not what I want. I want a "fixed effects" model, meaning I want the intercept to vary by cluster. Here's

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

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 (

Dear all, Is it possible to incorporate a nested design in GEE? I have measurements on trees that where measured in two years. The trees are nested in plots. Each plot contains 24 trees. The number of plots is 72. Hence we would expect 2 * 24 * 72 = 3456 data points. A few are missing, so we end up wih 3431 data points. This is what I have tried until now. #assuming independence between trees

Hi, I'm analyzing my data using GEE, which looks like below: > interact <- geeglm(L ~ O + A + O:A, + data = data1, id = id, + family = binomial, corstr = "ar1") > summary(interact) Call: geeglm(formula = lateral ~ ontask + attachment + ontask:attachment, family = binomial, data = firstgroupnowalking, id = id, corstr = "ar1") Coefficients:

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 all, My data are binary and within-subject correlated; there are three factors of interest and two of them are within-subject. So, I have considered modelling the data using a binomial regression with a GEE approach (which can be achieved using the function geeglm). One problem is that I got some empty cells, so I can't simply use logit as a link function. I was wondering if you know any

Dear all, I am trying to do a GEE on count data and I am having problems with how to order the data. Below is a simplified example of what my data looks like.. Route Time Day Pass Distance 1 30 1 4 0 1 60 1 12 200 1 120 1 25 600 1 30 2 8 0 1 60 2 17 200 1

Hello Fellow R Users,I have spent the last week trying to find a work around to this problem and I can't seem to solve it. I simply want to plot my GEE model result with 95% confidence bands. I am using the geepack package to run a basic GEE model involving nestling weights, to a Gaussian distribution, with "exchangeable" error structure. I am examining how nestling weight varies

Dear all, I have had some rather serious difficulties getting graphics and analysis packages to load in R on my new Ubuntu Install. The trouble seems to begin when attempting to install TMB. I have done a full purge of r-base r-base-dev and r-base-core and reinstalled the newest version of RStudio (not that this should matter). I'm not sure what to try next. many thanks! Brandon Z >

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

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.

*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