[R-sig-ME] Overdispersion and R2 in GLMM
mteresaoliveira92 at gmail.com
Sat Jul 2 21:29:17 CEST 2016
1) A RSF is any function proportional to the probability of selection of
habitat, so it is used to examine habitat selection. The coefficients with
a random intercept for individual (to account for, for instance,
differences in behaviour) will provide coefficients for each resource
variable included with which it is possible to evaluate selection or
avoidance of each variable.
2) I am using glmer() function in "lme4" package. Generalized linear mixed
model fit by maximum likelihood (Laplace Approximation) ['glmerMod'];
Family: binomial ( logit ).
3) Which values (a) are you referring to?
How do I know if ratio value is normal?
4) Yes, I am doing some preliminary analyses to my data and I was trying
different random factors, some of them nested, and those different values
only appear using nested random factors, so I guess I am not using the
2016-07-01 1:28 GMT+01:00 Ben Bolker <bbolker at gmail.com>:
> On 16-06-30 12:40 PM, Teresa Oliveira wrote:
> > Dear all,
> > I want to estimate RSF
> Can you tell us/remind us what RSF is? (Remember that this is a very
> general mailing list about mixed models, with readers in many different
> > and to obtain the relative probabilities I will use
> > the coefficients obtained with GLMMs. I am new in this so I hope I can
> > express myself well.
> > To construct the GLMM's, I am using glmer(). I will to use the MuMIn
> > package to perform model selection [using dredge()].
> I'm getting less and less enthusiastic about this approach, but
> comments will have to wait for another day.
> > I have two doubts:
> > 1) For the top models I get, in order to understand if they are really
> > meaningful, I need to estimate R2 [r.squaredGLMM()] and to test for
> > overdispersion [overdisp.glmer() with "RVAideMemoire" package], right? Is
> > there anything else I must consider (besides de AIC)?
> I would generally suggest looking at graphical diagnostics (e.g. see
> ?plot.merMod) and plotting the predicted responses of the model along
> with the original data. R^2 values are not bad, but they're very much
> harder to interpret in a (G)LMM context.
> (the same base URL also has more information about computing
> > 2) How do I interpret the results in both tests?
> > For a model with all variables I wanted to include (so, before performing
> > model selection), I estimated R2 and overdispersion.
> > For overdispersion I got this:
> > "> overdisp.glmer(lm_set3)
> > Residual deviance: 8537.397 on 32658 degrees of freedom (ratio: 0.261)"
> > Which value for ratio is acceptable?
> What kind of model are you fitting?
> In general overdispersion values (a) are only relevant
> > For R2 I got this:
> > "> r.squaredGLMM(lm_set3)
> > The result is correct only if all data used by the model has not changed
> > since model was fitted.
> > R2m R2c
> > 0.006139516 0.788967246 "
> > Is it normal to get values so different? Should I consider both?
> This means that your fixed effects explain almost nothing, but the
> combination of fixed and random effects explains almost everything.
> Does seem a little concerning.
> Explore your data graphically!
> Ben Bolker
> > Thank you very much for your time and help!
> > Best regards,
> > Teresa
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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