Hierarchical structure for preservation of observations
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 <https://www.linkedin.com/profile/view?id=AAIAAAdWVW8BMzqU_2EGNbEkyuy8O7K1Jyhd8ps&trk=nav_responsive_tab_profile_pic> [[alternative HTML version deleted]]