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
form:

Grooms_mate~Age+Species*Sex+Family_composition+Repro_phase*Sex

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?

I should add that my regression knowledge is fairly basic and I am relying
heavily on Zuur et al's Mixed Effects Models and Extensions in Ecology with
R to help me understand both what I am doing and how to do it in R.

Thankyou for reading!

-- 
Belinda Lee Burns,
PhD student
Email: burnsb02@student.uwa.edu.au

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