On Apr 15, 2012, at 01:37 , David Winsemius wrote: > > On Apr 14, 2012, at 6:47 PM, smfa wrote: > >> Hi, >> >> I know this is probably a basic question... But I don't seem to find the >> answer. >> >> I'm fitting a GLM with a Poisson family, and then tried to get a look at the >> predictions, however the offset does seem to be taken into consideration: >> >> model_glm=glm(cases~rhs(data$year,2003)+lhs(data$year,2003), >> offset=(log(population)), data=data, subset=28:36, family=poisson()) >> >> predict (model_glm, type="response") >> >> I get cases not rates... >> >> I've tried also >> >> model_glm=glm(cases~rhs(data$year,2003)+lhs(data$year,2003)+ >> offset(log(population)), data=data, subset=28:36, family=poisson()) >> >> with the same results. However when I predict from GAM, using mgcv, the >> predictions consider the offset (I get rates). > > The beta coefficients are the log-rate-estimates when you use log(population) > as the offset.
But they are not the log predicted rates if you are describing many rates using a few parameters. > >> I'm missing something? > > You are most definitely missing the part where you include 'data'. True. (cases ~ rhs(year, 2003) + lhs(year, 2003) is right, the other way only even works if you predict on the same data set). More to the point: does the OP realize how easy it is to go from fitted cases to rates by dividing with the population size? A logical way to get predicted rates would be to make predictions for a new data set where the poulation size was set to 1 (or 100000, maybe), but it seems easier to "divide and conquer" (pardon the pun). -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ 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.
