R help - Mar 2012 - Help with confidence intervals for gam model using mgcv

Hi,

I would be very grateful for advice on getting confidence 
intervals for the ordinary (non smoothed) parameter 
estimates from a gam.

Motivation
I am studying hospital outcomes in a large data set. The 
outcomes of interest to me are all binary variables. The one 
in the example here, Dead30d, is death within 30 days of 
admission. Sexf is gender (M or F), Age is age in years at 
the start of the admission. The standard glm is a logistic 
regression :-

glmDead.AS <- glm(Dead30d~Sexf+Age, 
data=HIPE,family=binomial(link="logit"))

The corresponding GAM, with a smooth for age, is :-

gamDead.AS <- gam(Dead30d~Sexf+s(Age), 
data=HIPE,family=binomial(link="logit"))

For my work, age is a nuisance. We already know exactly the 
effect of age (which has an odd shape). I have no interest 
in this parameter, nor in CIs for it. The GAM fits notably 
better than the GLM. The substantive interest is in the 
effects of the other variables, Sexf, and many more.

For the GLM, the confidence intervals are simple matter of 
confint(glmDead.AS). For my discipline CI's are required, 
and the profile CI's that confint produces are ideal.

There doesn't seem to be an analogous function for mgcv. The 
advice most commonly given is to use predict.gam with 
se.fit=TRUE. This does not seem to produce CI's for the 
non-smoothed parameters, which is what I need to calculate. 
CIs for the smooth, which are the focus of interest in many 
other cases are not of interest to me.

Any suggestions? Am I missing something very obvious?

Best wishes,
Anthony Staines
-- 
Anthony Staines, Professor of Health Systems,
School of Nursing and Human Sciences, DCU, Dublin 9,Ireland.
Tel:- +353 1 700 7807. Mobile:- +353 86 606 9713
http://astaines.eu/