fit1 <- glm(factor(x1)~factor(Round)+x2,family=binomial(link="probit"))
fit2 <- gam(factor(x1)~factor(Round)+x2,family=binomial(link="probit"))
all.equal(fitted(fit1), fitted(fit2))
[1] TRUE
so the fits to the data are the same: your error was in over-interpreting
the parameters in the presence on non-identifiability.
On Thu, 3 Jan 2008, Daniel Malter wrote:
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
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--
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
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