[R-sig-eco] What is the best way to express the goodness of fit for a model obtained by generalized least squares (using gls function from nlme package)?

[Rob Robinson]

 A good place to start with all GL(M)M questions is Ben Bolker's FAQ which
provides thoughts on many commonly asked questions. In particular
http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#how-do-i-compute-a-coefficient-of-determination-r2-or-an-analogue-for-glmms

An approach I have used in the past (for non-mixed models) is to compare
the deviance of a given model to that of the 'full' model (ie maximal time
and/or space variation) and the null (intercept only) model - see
http://www.cebc.cnrs.fr/publipdf/2008/GBR83.pdf (the FAQ attributes the
approach to Xu 2003)

Best wishes
Rob



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Dr Rob Robinson, Associate Director - Research
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On 25 January 2018 at 00:15, Diego Podadera <diegopodadera at gmail.com> wrote:

> Hi everyone,
>
> What is the best way to express the goodness of fit for a model obtained
> by generalized least squares (using gls function from nlme package)? I
> don’t want to compare this model with other models, so AIC is not a good
> choice in this case. Is it right to use the approach proposed by NAKAGAWA,
> S.; SCHIELZETH, H. A general and simple method for obtaining R2 from
> generalized linear mixed-effects models. Methods in Ecology and Evolution,
> v. 4, n. 2, p. 133–142, 2013 (rsquared{piecewiseSEM}) to calculate the
> marginal squared r even if my model is a simple regression instead a mixed
> model? For mixed models we get, in the output, values for marginal and
> conditional squared r. For models only with fixed effects we get a value
> for only one squared r.
>
> Cheers,
> Diego Sotto Podadera
>         [[alternative HTML version deleted]]
>
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