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

[Dexter Locke]

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 at 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 at r-project.org]
> On Behalf Of Dexter Locke
> Sent: 15 July 2017 14:34
> To: r-sig-mixed-models at 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
>
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>
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