[R-sig-ME] Small sample Size; repeated measurements binomial glmer

[Ben Bolker]

On Thu, Nov 5, 2015 at 7:16 AM, Quentin Schorpp
<quentin.schorpp at ti.bund.de> wrote:
> Hello,
>
> I searched a lot in the internet, but i didn't find sufficient information.
> I believe I've got a very simple study design, however there are some
> characteristics taking me to the brink of possible.
>
> I have two sampling campaigns, autumn year 1 and autumn year 2,
> I sampled five agricultural fields of different ages, but each age_class
> has got only 3 repetitions.
> My response is proportion of fungal feeding species.

  Each field has a different, i.e. unique age, e.g. field 1 = 1.5,
field 2 = 2.3, field 3 = 3, field 4= 7, field 5 = 10?   3 samples each
in 2 autumns?  (This would be 5 x 3 x 2 = 30, but I'm not sure if
that's the actual experimental design ...)

>
> I am interested in the effect of age classes (increase over time) and if
> this effect is reflected during the time of sampling.
> Since i should be able to observe an increase during a 1 year time
> interval, then.
>
> My Model is:
> glmer(response ~ age_class*autumn + (1|field), family="binomial",
> weights=total number of Individuals, data)
>
> However, I have the following problems:
>
> 1 - My N = 30, but my N(group) = 3

   That doesn't really matter.

> 1 - I don't know the power of my analysis

  To run a power analysis you need to decide what effect sizes you're
expecting.  There aren't simple canned power  analyses for mixed models
like ?power.t.test in base R, but

library("sos"); findFn("lme4 power analysis")

finds the hamlet, longpower, odprism, multiRR, pamm ... packages ...
or look at https://rpubs.com/bbolker/11703 ...


> 2 - I'm not able to drop Outliers from the data (or am I?)

  why not?

> 3 - my random factor has only 2 levels, so N(random) = 2

  I'm confused.  You have 'field' as your grouping variable above.
I thought you said you had 5 fields?

>
> I think in Bolker et al. (2008) und Zuur et al. (2009) st. is said about
> that there is no need to use random factors when N(random) = 2

  Indeed, if you have fewer than about 5 groups, random effect estimates
are going to be low-power/unreliable (unless you do something fancy like
impose a Bayesian prior on the variance)

>
> Since I am quite confused about my opportunities to handle patterns in
> residuals of the above model, I'm asking you about your opinions. Have i
> chosen the right Model formulation?
>
> I think I'd feel more confident with a non parametric test, sth. like a
> rank based estimation of mixed effects nested models (rlme package), for
> which i found not a single example how to use them with repeated
> measures (also for PERMANOVA), or sth. else.
> At least i need to report an Anova table and pairwise comparisons
>
> yours sincerely,
> Quentin
>
> --
> Quentin Schorpp, M.Sc.
> Thünen-Institut für Biodiversität
> Bundesallee 50
> 38116 Braunschweig (Germany)
>
> Tel:  +49 531 596-2524
> Fax:  +49 531 596-2599
> Mail: quentin.schorpp at ti.bund.de
> Web:  http://www.ti.bund.de
>
> Das Johann Heinrich von Thünen-Institut, Bundesforschungsinstitut für Ländliche Räume, Wald und Fischerei – kurz: Thünen-Institut –
> besteht aus 15 Fachinstituten, die in den Bereichen Ökonomie, Ökologie und Technologie forschen und die Politik beraten.
>
> Quentin Schorpp, M.Sc.
> Thünen Institute of Biodiversity
> Bundesallee 50
> 38116 Braunschweig (Germany)
>
> Tel:  +49 531 596-2524
> Fax:  +49 531 596-2599
> Mail: quentin.schorpp at ti.bund.de
> Web:  http://www.ti.bund.de
>
> The Johann Heinrich von Thünen Institute, Federal Research Institute for Rural Areas, Forestry and Fisheries – Thünen Institute in brief –
> consists of 15 specialized institutes that carry out research and provide policy advice in the fields of economy, ecology and technology.
>
>
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>
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