[R-sig-eco] which factor to nest?

[Ben Bolker]

  My two cents:

  * a GLMM if parasite numbers are small enough to
have to deal with them as count data (e.g. lots of zeros).
Otherwise (if you're lucky, as GLMMs are harder) most
likely a lognormal -- log-transform data or log(1+x) if
there are some zeros, and treat as a LMM (nlme or lmer).

  * "Nesting" is more or less a red herring here, only
really has to do with multiple *random* factors (and
then more to do with the coding of the random factors
than with fundamental experimental design distinctions).

  * so: antiG vs control is fixed, Beekeeper is probably
best treated as random (7 units is enough to make a
random effect plausible: if you had only 2 or 3 you
would probably have to treat as a fixed effect to
make progress)

  * because unbalanced (and possibly GLMM), aov/sums
of squares approaches are probably not viable

  * fairly straightforward with nlme (something like
lme(logparasites ~ antiG, random = ~1|Beekeeper) or
lme4:

lmer(logparasites ~ antiG + (1|Beekeeper)) or
(for GLMM)

glmer(logparasites ~ antiG + (1|Beekeeper), family=poisson)

 * Two more things to watch out for:

   - lme (nlme package) will give you p-values, lmer (lme4 package)
will not
   - if you end up fitting a GLMM you should definitely
worry about/check for overdispersion

  Ben Bolker


tavery wrote:
> Hi all,
> Maybe an expert of this particular design could provide insights into a 
> interesting question (or possibly just a derailed view). Possibly 
> outside of the R world, but has to be sorted out before R code can be 
> generated - which should be trivial...
> 
> - 7 beekeepers each with several hives
> - some hives treated with antiG, others left as controls
> - unbalanced design (not an equal number of treated or control sites 
> among or within beekeepers)
> - measured parasite numbers (average per hive)
> Q: want to know if antiG reduces parasite load
> 
> The initial reaction (from a student) was to consider Beekeeper as a 
> random factor (although it could be considered fixed), and nest 
> Treatment (antiG or control) within Beekeeper. This design is intuitive 
> as Beekeepers are 'groups' and hives are 'subgroups' to which treatments 
> are applied. Upon some investigation, it appears that the model could be 
> flipped i.e. consider Treatment as a fixed factor and nest Beekeeper 
> within Treatment. In this latter case, each Beekeeper would be 
> represented in each Treatment and a crossed design results i.e. not 
> nested at all. Various texts appear to 'arbitrarily' designate factors 
> in similar models (see Zar on drug/drugstore example).
> 
> a) What design is correct?
> b) What am I missing in way of determining groups and the ultimate design?
> 
> thanks in advance,
> trevor
> biology department
> acadia
> 
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-- 
Ben Bolker
Associate professor, Biology Dep't, Univ. of Florida
bolker at ufl.edu / www.zoology.ufl.edu/bolker
GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc