Hi Raeanne,
gamm fits using PQL, which doesn't always converge, however looking at
your data, a couple of things stand out...
1. Trying a Tweedie seemed to give nicer residual plots than the
negative binomial, and also converges with gamm. e.g.
SB.gam3<-gam(count~offset(vol_offset)+
s(Depth_m, by=StnF, bs="cs")+StageF*RegionF+s(StnF,bs="re"),
family=Tweedie(p=1.4),
data=Zoop_2011[Zoop_2011$SpeciesF=='SB',],method="ML")
SB.gam3
plot(fitted(SB.gam3)^.5,residuals(SB.gam3))
qq.gam(SB.gam3)
... I just searched over p=1.2-1.6 in steps of .1 to find the optimum
value for the Tweedie p parameter according to the ML score. see ?Tweedie
2. If you only need a random effect for StnF, and don't have to have the
spatial autocorrelation structure then you can add the random effect in
gam, as above - this will be much more efficient than using gamm
(doesn't use PQL). See ?random.effects.
best,
Simon
On 07/12/12 12:00, Raeanne Miller wrote:
Hi there,
My question is about the 'theta' parameter in specification of a NB GAMM.
I have fit a GAM with an optimum structure of:
SB.gam4<-gam(count~offset(vol_offset)+
s(Depth_m, by=StnF, bs="cs")+StageF*RegionF,
family=negbin(1, link=log),
data=Zoop_2011[Zoop_2011$SpeciesF=='SB',])
However, this GAM shows heterogeneity in the residuals, and the model fit is
not great. I would like to add a random effect to this GAM, for station (StnF),
and a spatial correlation structure, for Depth_m. When trying to fit an
equivalent GAMM to start with, I used the formula:
SB.gamm1<-gamm(count~offset(vol_offset)+
s(Depth_m,by=StnF,bs="cs")+StageF*RegionF,
family=negative.binomial(theta=1.41), niterPQL=50,
data=Zoop_2011[Zoop_2011$SpeciesF=='SB',])
The theta=1.41 came from SB.gam4, using the theta.ml() command. However, this
model does not converge, even with 5000 iterations. When I increase theta to 6,
the model converges, but I'm not sure why. I have also tried theta=c(1,10),
which converges, but gives me lots of warnings:
Warning messages:
1: In y + .Theta :
longer object length is not a multiple of shorter object length
Can anyone tell me why this might be?
The model also does not ever converge if I replace family=negative.binomial()
with family=negbin(). I've looked at the help files for GAM and GAMM, and I'm
not sure what the difference between these is?
I have attached code to create my dataset, for those interested.
With many thanks,
Raeanne
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