[R] lme with nested factor and random effect

R. Michael Weylandt <michael.weylandt@gmail.com> michael.weylandt at gmail.com
Thu Dec 15 21:10:19 CET 2011

On Dec 15, 2011, at 2:07 PM, Ben Bolker <bbolker at gmail.com> wrote:

> Mari Pesek <marifrances <at> gmail.com> writes:
>> Hello all,
>> I'm having difficulty with setting up a mixed model using lme in the
>> nlme package. To summarize my study, I am testing for effects of
>> ornamentation on foraging behavior of wolf spiders. I tested spiders
>> at two different ages (penultimate vs. mature) and of two different
>> phenotypes (one species tested lacks ornamentation throughout life
>> [non-ornamented males] while the other acquires ornamentation upon
>> maturation [i.e. brush-legged males]). I tested a sample of
>> brush-legged and non-ornamented males (as both penultimates and
>> matures) in 2009, and an additional sample of brush-legged males in
>> 2010 (as both penultimates and matures again) because I had a very
>> small sample of brush-legged males in 2009.
>> I would like to set up my lme so the fixed effects are "age"
>> (penultimate vs mature), "phenotype" (non-ornamented vs brush-legged),
>> and "year" (2009 vs 2010) nested within "phenotype" to test for
>> differences between the two samples of brush-legged males.
>> Additionally I want to include "id" (a unique identification number
>> given to each spider tested) as a random factor to account for testing
>> each individual twice (once as a penultimate and once as a mature).
>> So far I have the following code: lme(behavior ~ age*phenotype,
>> random=~1|maturity/id, data)
>> But I don't know how to include the code to nest year within phenotype
>> while testing for all possible interactions. Any help would be greatly
>> appreciated.
>   I have some thoughts on this.  I think your best bet is
> lme(behavior~age*phenotype*year, random=~age|id, data)
> or possibly
> lme(behavior~age*phenotype + phenotype:year, random=~age|id, data)
> ("crossing" for fixed effects is more or less equivalent to
> creating an interaction.  You should also make sure that you
> have converted 'year' to a factor rather than a numeric variable ...)
>  but if you re-post this to the r-sig-mixed models at r-project.org list I will
> answer more fully ...

Note a hyphen got lost along the way (or at least it didn't make it to my machine): the email address is r-sig-mixed-models at r-project.org


>  Ben Bolker
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