R help - Mar 2007 - gam parameter predictions --Sorry for double posting

R-help,

Sorry for posting again the same question (dated 26-03-2007) but 
all my mails have been sent to the recycle bin without possibility
of recovering and thus I don't know if anyone has answer my query.

Here is the original message:

I'm applying a gam model (package mgcv) to predict
relative abundances of a fish species.

The covariates are year, month, vessel and statistical rectangle.


The model looks like this:

g1 <- gam(log(cpue) ~  s(rekt1) + s(year) + s(mon) + s(reg1), data dataTest)


Once the model is fitted to the data I want to get the mean model
estimates by year.

I do the following:

obsPred <- data.frame(year = dataTest$year, pred = predict(g1, type
"response"))

gamFit <- tapply(obsPred$pred, list(year = obsPred$ar), mean)



Is this correct?



Thanks in advance

> version
               _                           
platform       i386-pc-mingw32             
arch           i386                        
os             mingw32                     
system         i386, mingw32               
status                                     
major          2                           
minor          4.1                         
year           2006                        
month          12                          
day            18                          
svn rev        40228                       
language       R                           
version.string R version 2.4.1 (2006-12-18)

Looks ok to me, provided that you want averages (on log scale) taken over the 
the observed covariate values. If you want variances for the means you could 
always do something like the following...

Xa <- model.matrix(~as.factor(year)-1)
Xa <- t(Xa)/colSums(Xa)  ## Xa%*%fitted(g1) gives required averages

Xp <- predict(g1,type="lpmatrix") ## Xp%*%coef(g1) gives fitted
values
Xm <- Xa%*%Xp  ## now Xm%*%coef(g1) gives required averages

yearly.means <- Xm%*%coef(g1)
ym.cov <- Xm%*%vcov(g1,freq=FALSE)%*%t(Xm) ## cov matrix of yearly means

(help file for predict.gam has a bit more information on this sort of thing, 
or section 5.2.6 of my book)

best,
Simon

-- 
> Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK
> +44 1225 386603  www.maths.bath.ac.uk/~sw283 
>
> The covariates are year, month, vessel and statistical rectangle.
>
>
> The model looks like this:
>
> g1 <- gam(log(cpue) ~  s(rekt1) + s(year) + s(mon) + s(reg1), data >
dataTest)
>
>
> Once the model is fitted to the data I want to get the mean model
> estimates by year.
>
> I do the following:
>
> obsPred <- data.frame(year = dataTest$year, pred = predict(g1, type >
"response"))
>
> gamFit <- tapply(obsPred$pred, list(year = obsPred$ar), mean)
>
>
>
> Is this correct?
>
>
>
> Thanks in advance
>
> > version
>
>                _
> platform       i386-pc-mingw32
> arch           i386
> os             mingw32
> system         i386, mingw32
> status
> major          2
> minor          4.1
> year           2006
> month          12
> day            18
> svn rev        40228
> language       R
> version.string R version 2.4.1 (2006-12-18)
>
> ______________________________________________
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> http://www.R-project.org/posting-guide.html and provide commented, minimal,
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