zozio32 <remy.pascal <at> gmail.com> writes: > > > Hi > > I had to use a glm instead of my basic lm on some data due to unconstant > variance. > > now, when I plot the model over the data, how can I easily get the 95% > confidence interval that sormally coming from: > > > yv <- predict(modelVar,list(aveLength=xv),int="c") > > matlines(xv,yv,lty=c(1,2,2)) > > There is no "interval" argument to pass to the predict function when using a > glm, so I was wondering if I had to use an other function >
You need to use predict with se=TRUE; construct the confidence intervals by computing predicted values +- 1.96 times the standard error returned; and apply the inverse link function for your model. If heteroscedasticity is your main problem, and not a specific (known) non-normal distribution, you might consider using the gls function from the nlme package with an appropriate 'weights' argument. ______________________________________________ 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.
