Thanks much for your response. My apologies for not putting sample code in the first place. Here it comes:
Round=rep(1:10,each=10) x1=rbinom(100,1,0.3) x2=rep(rnorm(10,0,1),each=10) summary(glm(factor(x1)~factor(Round)+x2,family=binomial(link="probit"))) library(mgcv) summary(gam(factor(x1)~factor(Round)+x2,family=binomial(link="probit"))) Cheers, Daniel ------------------------- cuncta stricte discussurus ------------------------- -----Ursprüngliche Nachricht----- Von: Prof Brian Ripley [mailto:[EMAIL PROTECTED] Gesendet: Thursday, January 03, 2008 2:13 AM An: Daniel Malter Cc: [EMAIL PROTECTED] Betreff: Re: [R] GLM results different from GAM results without smoothing terms On Wed, 2 Jan 2008, Daniel Malter wrote: > Hi, I am fitting two models, a generalized linear model and a > generalized additive model, to the same data. The R-Help tells that "A > generalized additive model (GAM) is a generalized linear model (GLM) > in which the linear predictor is given by a user specified sum of > smooth functions of the covariates plus a conventional parametric > component of the linear predictor." I am fitting the GAM without > smooth functions and would have expected the parameter estimates to be equal to the GLM. > > I am fitting the following model: > > reg.glm=glm(YES~factor(RoundStart)+DEP+SPD+S.S+factor(LOST),family=bin > omial( > link="probit")) > reg.gam=gam(YES~factor(RoundStart)+DEP+SPD+S.S+factor(LOST),family=bin > omial( > link="probit")) > > DEP, SPD, S.S, and LOST are invariant across the observations within > the same RoundStart. Therefore, I would expect to get NAs for these > parameter estimates. So your design matrix is rank-deficient and there is an identifiability problem. > I get NAs in GLM, but I get estimates in GAM. Can anyone explain why > that is? Because there is more than one way to handle rank deficiency. There are two different 'gam' functions in contributed packages for R (and none in R itself), so we need more details: see the footer of this message. In glm() the NA estimates are treated as zero for computing predictions. > Thanks much, > Daniel > > 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. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ 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.
