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 at gmail.com | jacob.bukoski at yale.edu | LinkedIn
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