search for: geeglm

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 Pre...

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...

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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...

*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 c...

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...ites. 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 following code geeglm(SumOfButterflies ~ RES_YEAR, family = poisson, data = ManijurtNoNA, id = RES_ROTE_ID, corstr = "ar1") I obtain "normal" output. Not surprisingly, overdispersion is present (Estimated Scale Parameters: [1] 185.8571), so changing to quasipoisson is needed. However, the code belo...

...o make the generic function to choice the correct method, or in case that for any method taken it produces an error, to try another one. For example (commented in detail here<https://github.com/tidymodels/broom/issues/714>): # object with multiple classes: the output of function `geepack::geeglm`. The output of `class(object)`: ``` [1] "geeglm" "gee" "glm" "lm" ``` The generic function: `stats::confint`. The S3 method chosen: `confint.glm`. It produces an error. The correct method in this case would be `broom:::confint.geeglm`. Thank you! Iag...

...rs and by definition have limited ranges (e.g. -5 to +5), though within the range they are approximately normally distributed (with ~50 levels). One aim of the analysis is to assess whether the measures are related to certain covariates, and I have tried the generalised estimating equation function geeglm (library geepack) with the 'gaussian' family details like so: geeout <- geeglm(outcome ~ covariate1 + covariate2, id=familyID, family=gaussian, data=dat, corstr="unstructured") But I'm thinking that the limited range of the outcome violates the assumption of normality and t...

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, subset = marp1 == 1 , +...

...ot; "stress1" "stress2" "stress3" "stress4" [22] "hours1" "hours2" "hours3" "hours4" "subject" First I reshape my data to prepare for geeglm: compl$subject <- factor(rownames(compl)) nobs <- nrow(compl) compl_long <- reshape(compl, idvar = "subject",varying = list(c( "PHQ_Surv1" , "PHQ_Surv2" , "PHQ_Surv3" , "PHQ_Surv4" ), c("error1_1.x", "error1_2.x", &q...

...of one year (12 repeated measures) at seven sites, three of those being "continuous forest sites" and four of those being "secondary forest sites". The aim is to compare continuous and secondary forests. Would you define the sites or the forest type as id argument: model1<-geeglm(formula = number ~ type + month, family = poisson, *id = site *, corstr = "ar1") model2<-geeglm(formula = number ~ type + month, family = poisson, *id = type *, corstr = "ar1") or should even almost every count have a special id (e.g. * id=interaction(month,site)* or *id=in...

...esponse 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 correlation . Several questions: 1. Are GEE’s the right choice? 2. How can I specify a user-defined spatial (exponential) correlation structure in geeglm or geese? I did not really get this from the help-sites. I guess I need to set corstr = &...

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...

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: Estimat...

...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()' failed. Abort (core dumped) Second, here's the command: mygeeglm<- geeglm(outcome ~ input , data=mydata, id=mydata$group) Third, my explanation (the dataset is pasted at the very bottom) - initially, mydata$input was an ordered factor, with levels "Med", "Low", "High" BUT the data did not contain any datapoints with value...

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 t...

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 your example): > confint.geeglm <- broom:::confint.geeglm > confint(geefit) lwr...

...ere are differences between the packages. The paper that introduces the geepack package ( https://www.jstatsoft.org/article/view/v015i02) states that the three features that distinguishes this package from others in R and other stats software programs include: 1. There is an interface function geeglm which is designed to be as similar to glm as possible 2. A jackknife variance estimator is available as an alternative to the sandwich estimator 3. Covariates can be incorporated into the scale and correlation parameters in a similar fashion to the mean modeling However from other p...

I'm using the package geepack to fit GEE models. Does anyone know of methods for add1 and drop1 for a 'geeglm' model object, or perhaps a method for extractAIC based on the QIC of Pan 2001? I see there has been some mention of this on R-help a few years ago (RSiteSearch("QIC")). The package does provide an anova method for its model objects, and update() seems to work: library(geepack) d...