[R] GLM results different from GAM results without smoothing terms

[Daniel Malter]

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=binomial(
link="probit"))
reg.gam=gam(YES~factor(RoundStart)+DEP+SPD+S.S+factor(LOST),family=binomial(
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. I get NAs in GLM, but I get estimates in GAM. Can anyone explain
why that is?

Thanks much,
Daniel
 

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