[R-sig-ME] combining varIdent variance function with mixed effects model
bbolker at gmail.com
Thu Jan 17 17:21:39 CET 2013
Belinda Burns <10517197 at ...> writes:
> Dear all,
> I hope this is the correct place for my question, if not, my apologies! I
> am analysing several behaviour variables obtained by observing captive
> gibbons. The raw values are in the form proportion of ten minutes spent
> doing the behaviour, and most of the behaviours are zero-inflated and
> negatively skewed.
> At the moment I am interested in modelling the proportion of time that
> adult gibbons spend grooming their mates, such that the models take the
> where species is a factor with 3 levels, family composition is a binary
> variable (they either have offspring or not) and repro_phase is the
> reproductive phase of the female (4 levels).
> Ideally I should be including individual and group as random effects
> (individuals are nested within groups) and so I would like to use a
> mixed model approach; however, diagnostic plots of residuals vs
> fitted values show heteroscedasticity (increasing spread with
> increasing fitted values) and plots of residuals vs predictors
> suggests that one species is less variable than the other two and
> gibbons with offspring are more variable than those without. The
> inclusion of a species*family_composition weighted variance function
> (using the weights= varIdent(form~1|Species*Family_composition) in a
> gls model) seems to improve the homogeneity of the residuals...
> I therefore have two questions (among a million others!): Can I
> include the two random effects in gls, or, vice versa, a varIdent
> structure in lmer? (the only contact I know doing mixed modelling in
> R uses lmer with MCMC estimation of p-values and so I am most
> comfortable using that to include the random effects) How do I write
> individual and group in as random effects considering individual is
> nested in group?
lmer does not handle "R-side" effects (heteroscedasticity/varStruct/etc.)
at present. You should be able to use random=~1|group/individual
in lme to account for individuals nested in groups. However,
heteroscedasticity is also a common feature of lognormal data: could
you get away with some transformation of the form log(small_number+proportion)
(realizing that picking small_number is a bit of a can of worms)?
Or plogis(small_number+proportion)? (Should be roughly equivalent if
the proportions are typically small.)
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