R help - Feb 2011 - GAM quasipoisson in MuMIn

Hi,

 I have a GAM quasipoisson that I'd like to run through MuMIn package


   - dredge
   - gettop.models
   - model.avg


However, I'm having no luck with script from an example in MuMIn help file.
In MuMIn help they advise "include only models with smooth OR linear term
(but not both) for each variable". Their example is:

# Example with gam models (based on "example(gam)")

require(mgcv)



dat <- gamSim(1, n = 500, dist="poisson", scale=0.1)



gam1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3) + (x1+x2+x3)^2, family poisson,
data = dat, method = "REML")



cat(dQuote(getAllTerms(gam1)), "\n")





# include only models with smooth OR linear term (but not both) for each
variable:





dd <- dredge(gam1, subset=(!`s(x1)` | !x1) & (!`s(x2)` | !x2) &
(!`s(x3)` |
!x3))


But I'm not sure at all how to apply these instructions to my data


My formula is

GAM<-gam(Species richness (count) ~ Categorical +  Continous + Continous + *
s*(Continous ) + Continous : Continous + Continous : Continous,
family=quasipoisson, data =)


Thanks for any advice on script


Karen

	[[alternative HTML version deleted]]

On Fri, 2011-02-04 at 12:31 +0000, Karen Moore wrote:
> Hi,
> 
<snip />
> My formula is
> 
> GAM<-gam(Species richness (count) ~ Categorical +  Continous + Continous
+ *
> s*(Continous ) + Continous : Continous + Continous : Continous,
> family=quasipoisson, data =)
Ok, I'm reasonably certain that that is *not* your gam() call. Why not
post the *actual* code you would enter for your GAM model?

dd <- dredge(GAM)

should evaluate all possible models from the global model you specified.
The example you quote is for a global model with both smooth and linear
terms for the same variables, hence in dredge() they need to select
models that include only the smooth for a particular variable or the
linear term for that variable, but not both.

By the looks of it, you don't have this problem.

G
> 
> Thanks for any advice on script
> 
> 
> Karen
> 
> 	[[alternative HTML version deleted]]
> 
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