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 as a function of distance from a plot boundary. The response variable (WEIGHT) and the explanatory variable (DISTANCE) are both continuous. The clustering factor (MOTHER) is entered into my model to account for similarity of nestlings from the same nest produced by a given mother. My simplified code is as follows: summary(model1<-geeglm(WEIGHT~DISTANCE, id=MOTHER,data=df,corstr="exchangeable"))) #I've included part of the model output here <Coefficients: < Estimate Std.err Wald Pr(>|W|) <(Intercept) 15.8702 0.4416 1291.8 < 2e-16 *** <Initiationdate -0.0664 0.0157 17.9 2.4e-05 *** < <Estimated Scale Parameters: < < Estimate Std.err <(Intercept) 5.78 2.46 plot(df$DISTANCE,df$WEIGHT) abline(model1) x<-seq(min(df$DISTANCE),max(df$DISTANCE),l=1000) y<-predict(model1,data.frame(DISTANCE=x)) Everything is fine up until this last line of code, when I get the following error message: "Warning message: In predict.lm(object, newdata, se.fit, scale = 1, type = ifelse(type == : calling predict.lm(<fake-lm-object>) ..." This isn't a geepack problem because I get the same error message using the gee package as well. The above simplified code is how I usually create and plot 95% CI bands for a linear model, followed by: matlines(x,y) I have read through the package PDFs and searched the web and archives of various listservs without success. Any help would be most appreciative. Seems like there should be a simple work-around. Thank you, Jason [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.