[R-sig-ME] dyads nested: confused on interpretation

[Thierry Onkelinx]

Dear Dexter,

(1 | Site / City) is City nested in Site. In you case it is better to give
each site a unique id and the just use (1|Site).

Best regards,



ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2017-07-15 19:05 GMT+02:00 Dexter Locke <dexter.locke op gmail.com>:

> Dear Tom, Thank you.  I have seen O’Hara and  Kotze's (2010) Methods in
> Ecology and Evolution piece. Thank you for reminding me about that with
> respect to the left-hand side of the model.
>
> I am also fitting parallel models for different soils measures, which are
> normally distributed and are bonafide continuous variables.
>
> Dear List (including Tom), am I interpreting the right-hand side correctly?
> I'm still confused about the contrasts, their interpretation, and how the
> tabular (regression-derived) results and the plotting of the raw data seem
> to suggest the opposite relationships.
>
> All the best,
> Dexter
>
>
>
> On Sat, Jul 15, 2017 at 12:50 PM, Tom Wilding <Tom.Wilding op sams.ac.uk>
> wrote:
>
> > Hi Dexter - is there a good reason why you are not using a Poisson
> > /quasi-Poisson or negative binomial regression model?  This would be a
> much
> > more elegant solution to your count-date analysis (regardless of anything
> > else).  If you Google 'why not log-transform count data' you'll find
> plenty
> > of evidence to that effect.
> >
> > Best
> >
> > Tom.
> >
> > -----Original Message-----
> > From: R-sig-mixed-models [mailto:r-sig-mixed-models-
> bounces op r-project.org]
> > On Behalf Of Dexter Locke
> > Sent: 15 July 2017 14:34
> > To: r-sig-mixed-models op r-project.org
> > Subject: [R-sig-ME] dyads nested: confused on interpretation
> >
> > Greetings mixed modelers,
> >
> > I'm fitting a three-level mixed model with lme4::lmer and struggling with
> > the interpretation.
> >
> > The dependent variable is species richness. There are zeros, and its not
> > normal, so I've added one and then logged it. Observations are collected
> as
> > pairs at sites, and therefore not independent. As Wickham (2014) notes -
> > referencing Bolker - there is an equivalence between a t-test a mixed
> model
> > in this type of case. Sites are also uniquely nested within one of two
> > cities, hence the third level. My syntax is:
> >
> > AAA <- lmer(log(richness + 1) ~fb*City + (1 | Site / City),
> > data=wy_GardenC, REML = F)
> >
> > "fb" indicates the location of the observation within the site: either
> > front (Front) or back (Back).
> >
> > Using sjPlot::sjt.lmer the p-values are calculated and formatted neatly
> in
> > a table (I do understand the controversies and assumptions around using
> > t-stats as Walk Z-stats..)
> >
> > The estimated intercept is 2.77 (or 15.96 once back-transformed), the fb
> > variable becomes "fbBack", its beta is 0.42 (or 1.52 once
> > back-transformed). The City and fb*City interaction terms are not
> > significant.
> >
> > Can I conclude that back yards are on average ~10% (1.52/ 15.92 =  0.095)
> > more species-rich? My confusion is that I'd think R takes b as in Back
> > first as the base case and makes f as in Front the contrast. Plotting the
> > data suggests that indeed back yards in Los Angeles (one of the two
> cities
> > is higher):
> >
> > http://dexterlocke.com/wp-content/uploads/2017/07/unnamed-1.png
> >
> >
> > I'm not interested in if all backs (on average) are greater than all
> > fronts (on average). I'm interested in if at each site, the back is
> > generally greater than the front. Am I specifying a corresponding model
> to
> > this question? Is the front being taken as the referent, and back as
> > reference?
> > Given the factors, what is being contrasted with what base-case?
> >
> > Thank you for your consideration,
> > Dexter
> >
> >
> > Wickham, H. (2014). Tidy Data. Journal Of Statistical Software, 59(10).
> > Retrieved from https://www.jstatsoft.org/article/view/v059i10/v59i10.pdf
> >
> > [[alternative HTML version deleted]]
> >
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