On 04/02/2012 01:26 PM, Mabille, Geraldine wrote:
Hi list,
I'm working with gamm models of this sort, using Simon Wood's mgcv library:
gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1))
gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1))
with a dataset of about 70000 rows and 110 levels for Group
I would be inclined to use something like
gm<- gam(Z~te(x,y)+s(Group,bs="re"),data=DATA,method="REML")
gm1<-gam(Z~te(x,y,by=Factor)+Factor+s(Group,bs="re"),data=DATA,method="REML")
AIC(gm,gm1)
See ?random.effects in the mgcv help for more details.
in order to test whether tensor product smooths vary across factor levels. I
was wondering if comparing those two models would be enough to conclude? I saw
a preceding post on similar issues
http://r.789695.n4.nabble.com/Comparing-and-Interpreting-GAMMs-td2234209.html
but that example used simple s() smooths instead of tensor product smooths. In
this case, the person was comparing between model A and model A1 like:
A<-gamm4(Z~factor+s(x)+s(x,by=factor) ,data=DATA, random=~(1|Group))
A1<- gamm4(Z~factor+s(x) ,data=DATA, random=~(1|Group))
I thus also tried to compare my model gm1, with another gm2 model of that sort:
gm2<-gamm(Z~te(x,y)+te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1))
but this type of models never converge and I obtain error messages of that
sort: Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0,
block 1
- The problem is that te(x,y) and te(x,y,by=Factor) are confounded. You
can get around this by making `Factor' into an ordered factor. See te
`by' variable section in ?gam.models.
2 questions from that:
1) Keeping in mind that my main question is to check whether tensor products
smooths vary across factor levels, would the comparison of models gm and gm1 be
sufficient for me?
- I'd have thought so.
2) Otherwise, is there something wrong in my gm2 model that make it impossible
to converge??
Also, side question that I already posted a few days ago but didn't get an
answer for:
3)I don't manage to use vis.gam() to print 3 different 2D-contour plots for
the three levels of factors I have in model gm1. It works well with model gm
(when only one plot is generated) but not gm1 (3 plots should be generated)
Here is the error message I obtain:
vis.gam(gm1$gam,plot.type="contour",n.grid=200,color="heat",zlim=c(0,4))
Error in predict.gam(x, newdata = newd, se.fit = TRUE, type = type) : number
of items to replace is not a multiple of replacement length
- hmm, possibly a bug. I'll look into it.
best,
Simon
Thanks a lot if someone can help with that,
Geraldine
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