[R-sig-ME] Confidence intervals in GAMM4
Gavin Simpson
gavin.simpson at ucl.ac.uk
Tue Jun 11 18:08:21 CEST 2013
On Tue, 2013-06-11 at 09:02 -0300, Rodrigo Tardin wrote:
> Hi Gavin and other member of the list
>
> Thanks a lot for your response. You are right about the correlation
> structure. I was not aware, thank you.
> One other question that may look like very basic:
> Without the correlation structure (that as you said, does not exist in
> GAMM4 or lme4) in the mixed model, does it account for autocorrelation or
> no, without any specification of correlation structure it does not account
> for autcorrelation in the residuals. Because my data do have a problem of
> autocorrelation on the residuals.
There is an induced correlation due to the random effect - all
observations within a level of RANDOM have the same estimated
correlation, IIRC. The AR(1) you hoped to specify would have that
correlation decline by the absolute power of the separation in time
(i.e. exponentially), so they are different.
If the residuals are correlated then you should do something about it if
you can as it may change which terms are significant in the model (as
standard errors are to narrow), and also with GAMMs where the smoothness
of the splines is determined from the data the procedure may tend to
under-smooth the data because it assumes that they are less correlated
than they really are.
HTH
G
> Thanks in advance
> Rodrigo
>
>
> 2013/6/5 Gavin Simpson <gavin.simpson at ucl.ac.uk>
>
> > On Thu, 2013-05-30 at 12:12 -0300, Rodrigo Tardin wrote:
> > > Hi all,
> > >
> > <snip />
> > >
> > > I searched in R for confidence intervals in GAMM4 but I did not find it.
> > I
> > > could obtain variance and std deviation for the random and fixed effects
> > > Groups Name Variance Std.Dev.
> > > RANDOM (Intercept) 1.4973e+01 3.8694319
> > > Xr.0 s(DISTCOAST) 4.7361e-02 0.2176263
> > > Xr s(DEPTH) 1.9779e-06 0.0014064
> > >
> > >
> > > Here it is my model.
> > > n3 <- gamm4(OCC_BIN~s(DEPTH)+s(DISTANCE_TO_COAST)+offset(RT),random = ~
> > > (1|RANDOM),correlation=corAR1(),method="ML", family=binomial,data=bryde3)
> >
> > Unfortunately, this is *totally* wrong. There is no `correlation`
> > argument in `gamm4()` nor `glmer()`, which is the underlying fitting
> > function. That this didn't raise an error is due to `gamm4()` etc having
> > argument `...` which silently mops up any left over, non-used arguments.
> >
> > `gamm()` in package *mgcv* does have a `correlation` argument but that
> > will fit your binomial GLMM via PQL which isn't such a good solution for
> > such models.
> >
> > In nlme:::lme there was a function intervals() which could provide the
> > CI on the REs - see if there is an equivalent for lme4:::glmer. IIRC
> > Doug has something on this in his in-prep book on mixed effects models
> > via lme4, see chapter 1 in
> > http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf
> >
> > That presumes that you sort out the AR(1) business; you can't do that in
> > glmer().
> >
> > Options are to move back to mgcv::gamm() but as I said, PQL isn't great
> > of binomial models. If your REs are simple, then mgcv::gam() can be
> > used. Again this doesn't have a correlation argument but ?magic (after
> > loading mgcv) has an example of including the correlation in the fit via
> > some jiggery-pokery. Alternatively and related to gam() is mgcv::bam()
> > which can take a known AR(1) parameter into account during the fitting.
> > This fits the AR(1) given the ordering of the data, which is what
> > `correlation=corAR1()` would have done - perhaps this just plain won't
> > work in nlme:::lme if you don't specify any ordering variable? - but I'm
> > not sure that will be correct given that your data are strictly time
> > ordered.
> >
> > HTH
> >
> > G
> >
> > > The OFFSET is the boat route (the number of times the boat searched for
> > > whales in each 2x2km grid)
> > > RANDOM is the individual whale as done in previous studies (Hazen et al
> > > 2009 - MEPS - doi:10.3354/meps08108)
> > >
> > > Can someone help me, please?
> > > Sincerely,
> > > _______________________________________________
> > > R-sig-mixed-models at r-project.org mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> > --
> > Gavin Simpson, PhD [t] +1 306 337 8863
> > Adjunct Professor, Department of Biology [f] +1 306 337 2410
> > Institute of Environmental Change & Society [e] gavin.simpson at uregina.ca
> > 523 Research and Innovation Centre [tw] @ucfagls
> > University of Regina
> > Regina, SK S4S 0A2, Canada
> >
> >
> >
> >
>
>
> Hi Gavin and other member of the list
>
>
> Thanks a lot for your response. You are right about the correlation
> structure. I was not aware, thank you.
> One other question that may look like very basic:
> Without the correlation structure (that as you said, does not exist in
> GAMM4 or lme4) in the mixed model, does it account for autocorrelation
> or no, without any specification of correlation structure it does not
> account for autcorrelation in the residuals. Because my data do have a
> problem of autocorrelation on the residuals.
>
>
> Thanks in advance
> Rodrigo
>
>
> 2013/6/5 Gavin Simpson <gavin.simpson at ucl.ac.uk>
> On Thu, 2013-05-30 at 12:12 -0300, Rodrigo Tardin wrote:
> > Hi all,
> >
> <snip />
> >
> > I searched in R for confidence intervals in GAMM4 but I did
> not find it. I
> > could obtain variance and std deviation for the random and
> fixed effects
> > Groups Name Variance Std.Dev.
> > RANDOM (Intercept) 1.4973e+01 3.8694319
> > Xr.0 s(DISTCOAST) 4.7361e-02 0.2176263
> > Xr s(DEPTH) 1.9779e-06 0.0014064
> >
> >
> > Here it is my model.
> > n3 <-
> gamm4(OCC_BIN~s(DEPTH)+s(DISTANCE_TO_COAST)+offset(RT),random
> = ~
> > (1|RANDOM),correlation=corAR1(),method="ML",
> family=binomial,data=bryde3)
>
>
> Unfortunately, this is *totally* wrong. There is no
> `correlation`
> argument in `gamm4()` nor `glmer()`, which is the underlying
> fitting
> function. That this didn't raise an error is due to `gamm4()`
> etc having
> argument `...` which silently mops up any left over, non-used
> arguments.
>
> `gamm()` in package *mgcv* does have a `correlation` argument
> but that
> will fit your binomial GLMM via PQL which isn't such a good
> solution for
> such models.
>
> In nlme:::lme there was a function intervals() which could
> provide the
> CI on the REs - see if there is an equivalent for
> lme4:::glmer. IIRC
> Doug has something on this in his in-prep book on mixed
> effects models
> via lme4, see chapter 1 in
> http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf
>
> That presumes that you sort out the AR(1) business; you can't
> do that in
> glmer().
>
> Options are to move back to mgcv::gamm() but as I said, PQL
> isn't great
> of binomial models. If your REs are simple, then mgcv::gam()
> can be
> used. Again this doesn't have a correlation argument
> but ?magic (after
> loading mgcv) has an example of including the correlation in
> the fit via
> some jiggery-pokery. Alternatively and related to gam() is
> mgcv::bam()
> which can take a known AR(1) parameter into account during the
> fitting.
> This fits the AR(1) given the ordering of the data, which is
> what
> `correlation=corAR1()` would have done - perhaps this just
> plain won't
> work in nlme:::lme if you don't specify any ordering variable?
> - but I'm
> not sure that will be correct given that your data are
> strictly time
> ordered.
>
> HTH
>
> G
>
> > The OFFSET is the boat route (the number of times the boat
> searched for
> > whales in each 2x2km grid)
> > RANDOM is the individual whale as done in previous studies
> (Hazen et al
> > 2009 - MEPS - doi:10.3354/meps08108)
> >
> > Can someone help me, please?
> > Sincerely,
>
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> --
> Gavin Simpson, PhD [t] +1 306 337
> 8863
> Adjunct Professor, Department of Biology [f] +1 306 337
> 2410
> Institute of Environmental Change & Society [e]
> gavin.simpson at uregina.ca
> 523 Research and Innovation Centre [tw] @ucfagls
> University of Regina
> Regina, SK S4S 0A2, Canada
>
>
>
>
>
>
>
> --
> Rodrigo Tardin
>
> Doutorando em Ecologia e Conservação - IBRAG - UERJ
> Mestre em Biologia Animal - PPGBA - UFRRJ
> Especialista em Docência do Ensino Superior - IAVM
> Laboratório de Bioacústica e Ecologia de Cetáceos - UFRRJ/ IF/ DCA
--
Gavin Simpson, PhD [t] +1 306 337 8863
Adjunct Professor, Department of Biology [f] +1 306 337 2410
Institute of Environmental Change & Society [e] gavin.simpson at uregina.ca
523 Research and Innovation Centre [tw] @ucfagls
University of Regina
Regina, SK S4S 0A2, Canada
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