R help - Apr 2012 - gamm: tensor product and interaction

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

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

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?
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

Thanks a lot if someone can help with that,
Geraldine





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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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>
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