The error budget resulting from this example closely resembles the error budget obtained by another study conducted for the same process model and forest stand using a fractional factorial design.
Meta-analyses were conducted in Review Manager (RevMan 5), using a random effects model and forest plots were generated.
Pooled measurement of association was obtained using the random effects model and forest plots were generated to display summarized results.
Heterogeneity between studies, subgroup analyses, meta-analyses (fixed-effects model), and forest plots were calculated using a commercially available software program (Comprehensive Meta-Analysis V2; Biostat, Englewood, NJ, USA).
Gonzalez [ 25], for example, assessed the behavior of the Geographical Modeling (GEOMOD) and Forest Restoration Carbon Analysis (FRCA) models in providing deforestation baselines, however only for the local scale.
The prediction models were created using both logistic models and random forest models, as some might argue that using random forest imputation methods might favour the random forest prediction models.
Nonutilitarian constructions alternately evoke mathematical models and Shinto forest spirits.
Through generalized linear models and random forest models, we have further identified a set of clinical end-points that are strongly associated with MTB-L presence.
The trends remain the same for imputation error, figure 4A,B representing the logistic models and random forest models respectively, with MICE coming out second best followed by NN and mean imputation.
Vetter et al. referred to a combination of model simulations and forest inventories of coniferous forests in Thuringia (a federal state of Germany) and found that "… older forests had the strongest relative increase in carbon sequestration …" by comparing their growth rates to a baseline scenario [ 42].
