[R-sig-eco] gam variable selection

[Gavin Simpson]

On Tue, 2011-09-27 at 14:40 +0200, Marco Helbich wrote:
> thank you for clarifying.
> so I can remove them all at once.

Given their effects are already removed you could just work with the
model *as is*. If you refit, you might have to be careful to ensure that
the same model (and smooth complexities) are selected when the redundant
variables do not take part in any of the fitting.

Just be careful to check the model with and without the redundant terms
really is the same.

G

> best
> marco
> 
> Am 27.09.2011 13:50, schrieb Gavin Simpson:
> > On Tue, 2011-09-27 at 13:42 +0200, Marco Helbich wrote:
> >> Gavin,
> >>
> >> thank you for your reply, I appreciate it!
> >>
> >> After consulting the proposed paper, I have tried your suggestion
> >> setting "select = T", which results again in another question:
> >>
> >> If the p-value is "NA" does this mean that the smoothing term is droped
> >> (or shrank to zero)? Independent of its high edf, is this predictor
> >> (e.g. s(x1)) not relevant to explain y?
> >
> > Those NA terms are ones that have effectively been penalised out of the
> > model - the EDF are effectively zero for these terms and they explain no
> > variance in the response. These predictors s(x1) and s(x4) appear to
> > have no relationships with y.
> >
> > You should also check out if there is concurvity - the multi
> > collinearity problem but for additive models. There is a function in
> > mgcv to see if this is a problem or not.
> >
> > HTH
> >
> > G
> >
> >>
> >> E.g.:
> >>                       edf    Ref.df      F p-value
> >> s(x1)   7.521e-09 1.402e-08  0.000      NA
> >> s(x2)    5.408e+00 6.448e+00  3.049 0.00462 **
> >> s(x3)    6.287e-09 1.217e-08  0.000      NA
> >> s(x4)    2.152e+00 2.754e+00  5.037 0.00248 **
> >>
> >> Best
> >> Marco
> >>
> >>
> >> Am 27.09.2011 11:40, schrieb Gavin Simpson:
> >>> On Tue, 2011-09-27 at 08:54 +0200, Marco Helbich wrote:
> >>>> Dear list,
> >>>>
> >>>> I am studying the influence of several environmental factors (numeric&
> >>>> dummies) on species densities (= numeric) using the gam()
> >>>> function with a gaussian link function in the mgcv package. As stated in
> >>>> Wood (2006) there is no variable selection algorithm.
> >>>>
> >>>> Is it an appropriate (iterative) approach to drop the predictor being
> >>>> least significant (eg. p>   0.05), refit the model, compare the GCV/AIC
> >>>> score and so forth. Should I first focus on the smoothing functions or
> >>>> fixed effects? Or is such a distinction not important at all?
> >>>>
> >>>> Perhaps someone has more experience with GAMs and can give me a helping
> >>>> hand? Thanks in advance!
> >>>
> >>> You could do that, but I would be sceptical of the results.
> >>>
> >>> Marra and Wood (2011, Computational Statistics and Data Analysis 55;
> >>> 2372-2387) compare various approaches for feature selection in GAMs.
> >>> IIRC, they concluded that an additional penalty term in the smoothness
> >>> selection procedure gave the best results. This can be activated in
> >>> mgcv::gam() by using the `select = TRUE` argument/setting.
> >>>
> >>> HTH
> >>>
> >>> G
> >>>
> >>>> Best
> >>>> Marco
> >>>
> >>
> >
> 

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