[R-sig-ME] Hierarchical structure for preservation of observations

[Thierry Onkelinx]

Dear Jacob,

The random effect specification is correct for the model that you have in
mind.

I'd rather think of the effects of site and genus as crossed rather nested.
The formula would become (1|Site) + (1|Genus). Assuming that you have 5 or
more genera. If not, then it better to add Genus to the fixed effects or
keep your current model.

I'm a bit worried about the complexity of your model. 65 observations is
not a lot for a model with 6 parameters.

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

2015-10-18 16:02 GMT+02:00 Jacob Bukoski <jbukoski1 op gmail.com>:

> Dear all,
>
> I am currently trying to develop a predictive model of ecosystem carbon
> stocks for forests of Southeast Asia. The difficult part is that the
> dataset I've compiled (from published data in the peer-reviewed literature)
> only consists of 65 or so observations.
>
> I've resorted to a mixed effects model under the understanding that I can
> specify a grouping structure and maintain spatially correlated observations
> within each study. However, I'm not sure that my specification of the
> hierarchical structure is acceptable, as this is my first attempt at using
> mixed models.
>
> I am using R version 3.2.2., and the lmer() function of the "lme4" package.
>
> I have specified basal area (a metric of tree stem cross sectional area at
> 1.3 meters height), latitude, and categorical variables for small (mean
> stem diameter < 5 cm) and large (mean stem diameter > 15 cm) forests as
> fixed effects.
>
> I have specified the dominant genus of tree in plots and the site as random
> effects. My thinking here is that plots with the same dominant genera of
> tree would not be independent of one another, nor would plots within the
> same site. Thus, by specifying random effects for Genus in Site... I can
> maintain the individual observations.
>
> The model I have specified as follows:
>
> *lmer1 <- lmer(Biomass ~ Basal.area + Latitude + Small + Large +
> (1|Site/Genus), REML=FALSE)*
>
> *My two-part question is*: (i) Does the logic behind my random effect
> specification hold, and (ii) have I specified it under the lmer() function
> correctly?
>
> Additionally, I am unsure of whether the number of groups has an impact on
> the degrees of freedom. The summary reports 34 groups for Genus:Site, and
> 21 for Site. My specified model has 40 residual degrees of freedom. Have I
> violated my degrees of freedom?
>
> Any advice and external resources will be hugely appreciated!
>
> Many kind thanks,
> Jacob
>
> --
> Jacob J. Bukoski
> Master of Environmental Science Candidate, 2016
> School of Forestry and Environmental Studies, Yale University
> jbukoski1 op gmail.com | jacob.bukoski op yale.edu | LinkedIn
> <
> https://www.linkedin.com/profile/view?id=AAIAAAdWVW8BMzqU_2EGNbEkyuy8O7K1Jyhd8ps&trk=nav_responsive_tab_profile_pic
> >
>
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