[R-sig-eco] R-sig-ecology Digest, Vol 34, Issue 3

[Saskia Otto]

Thanks Valerio for the note and thanks again Gavin for your detailed 
reply. I will try that out then. No problem with the timing. I realized 
myself that christmas wasn't the best time to get immediate replies :-)
Best regards,
Saskia

Am 06.01.2011 12:00, schrieb r-sig-ecology-request at r-project.org:
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> Today's Topics:
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>     1. species label for ordicoeno (LeRoy Elliott)
>     2. Re: bootstrapping and 2-dimensional smoothers in GAMs
>        (Gavin Simpson)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Wed, 5 Jan 2011 14:52:45 +0000
> From: LeRoy Elliott<lfelliott at hotmail.com>
> To:<r-sig-ecology at r-project.org>
> Subject: [R-sig-eco] species label for ordicoeno
> Message-ID:<SNT113-W9ABDA3F66E1312BF0CDFDA6090 at phx.gbl>
> Content-Type: text/plain
>
>
> I'm trying to determine the species represented in a plot produced using ordicoeno from BiodiversityR.
> plot1<- ordiplot(mdsresults, type = "t")ordicoeno(plotdata, plot1)
> It produces a nice coenocline, but I can't figure out how to identify the species represented by the curves. Any help? 		 	   		
> 	[[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> Message: 2
> Date: Thu, 06 Jan 2011 09:28:40 +0000
> From: Gavin Simpson<gavin.simpson at ucl.ac.uk>
> To: Saskia Otto<Saskia.Otto at uni-hamburg.de>
> Cc: r-sig-ecology at r-project.org
> Subject: Re: [R-sig-eco] bootstrapping and 2-dimensional smoothers in
> 	GAMs
> Message-ID:<1294306120.2341.75.camel at desktop.localdomain>
> Content-Type: text/plain; charset="UTF-8"
>
> On Tue, 2010-12-28 at 13:05 +0100, Saskia Otto wrote:
>> Dear all,
>>
>> I'm currently doing some GAM analysis and would like to include 2-way
>> interactions between my continuous, explanatory variables. I know that
>> for this, I need to include 2-dimensional smoothers but I'm not sure
>> how to include them.
>> For linear models I learned that I should always include the variables
>> that are in the interaction term as main terms, i.e. y = b0 + b1*x +
>> b2*z + b3*xz (usually everybody cites Underwood, 1997)
>>
>> How is it with GAMs if I want to include a 2-dimensional smoother for
>> interactions? In some papers I see the same approach, i.e. y = b0 +
>> s1(x) + s2(z) + s3(x,z). But in some papers I see only models like
>> this: y = a + s(x,z), i.e. only the 2-dimensional smoother. Is there a
>> general rule with GAMs?
> (Sorry for coming to this late - catching up from the Christmas break)
>
> I depends what you want to do with the models, and how strict you want
> to be about nesting them. Technically, to do model comparison using
> likelihood ratio tests or AIC, the models compared should be nested one
> within the other; i.e. you can go from the more complex model to the
> simpler model by setting one or more coefficients to zero in the more
> complex model.
>
> Firstly, s(x,y) produces a 2-d thin plate spline smoother of variables x
> and y. This assumes equal smoothing and the same basis in each of x and
> y. If x and y are not in the same units or are on different scales or
> have different degrees of smoothness, tensor product smooths are ideal,
> using te(x, y).
>
> With the two models:
>
> mod1<- gam(y ~ s(x1) + s(x2))
> mod2<- gam(y ~ s(x1, x2))
>
> (Note the intercept is implicit above.) We might want to test if mod2,
> the more complex model, is an improvement over mod1. As I mentioned
> above, these two models are not strictly nested. Simon Wood's GAM book
> suggests that mod2 be written as:
>
> mod2<- gam(y ~ s(x1) + s(x2) + s(x1, x2))
>
> However, the individual terms (might?) use a different basis when
> included on their own to the one used to represent them in the s(x1, x2)
> term. So again, the above is better, but still not quite strictly
> nested.
>
> Simon Wood, in newer material on his website (newer than his book),
> suggests the following models are strictly nested:
>
> mod1<- gam(y ~ te(x1) + te(x2))
> mod2<- gam(y ~ te(x1) + te(x2) + te(x1, x2))
>
> Note that the default basis here is a cubic regression spline basis
> where as the default for s() terms is a thin-plate spline basis; you can
> change this though, see ?te
>
>> Also, some of my smoothers have in the models p-values between 0.01
>> and 0.05. Since p-values in this range should not be trusted it is
>> always advised to do bootstrapping to get better p-values. Does
>> anybody know how to do that?
> See Simon Wood's book or papers cited in ?gam for details on coverage
> properties of confidence intervals and p values. Yes, p values are only
> approximate in the GAM case, but they are also approximate in the GLM
> case, just less so. So we must take care when close to 0.05.
>
> Bootstrapping is difficult for GAMs. See Slide 5 of:
>
> http://people.bath.ac.uk/sw283/mgcv/tampere/mgcv-advanced.pdf
>
> The idea of parametric bootstrapping for various lambda_i is covered on
> page 261 of Simon Wood's GAM book, but as Slide 5 says, this generally
> doesn't do too much, and is quite some effort.
>
> Simon has suggested to me in the past that if you worry about p-values,
> then REML or ML fitting gives better coverage properties, so set method
> = "ML" or method = "REML" in you call to gam().
>
> HTH
>
> G
>
>> My model looks like this:
>>
>> GAM1<- gam(cop  ~ s(Temp) + s(Sal) + s(Chl a), data = daten.sub)
>>
>>
>> Thanks for your help and a happy new year!!
>>
>> Saskia Otto
>>
>>
>>
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>>
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-- 

Saskia A. Otto

Mail: Saskia.Otto at uni-hamburg.de
Phone: +49(0)40-42838 6648
Hamburg University
Institute for Hydrobiology and Fisheries Science
Grosse Elbstrasse 133
22767 Hamburg
Germany