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The latent variable modeling approach enabled presentation of synthetic results rather than separate regression models and detailed analysis of the causal effects of interest.
However, utilizing a finite-mixture modeling approach enabled us to identify distinct types of visitors with divergent visitation behaviors under altered climate and environmental conditions.
Our modeling approach enabled us to produce regional maps of OLT and OLC, which will be useful in understanding risks and feedbacks associated with fires and climate feedbacks.
Whereas previous studies have used similar machine learning approaches to predict novel hosts for a single taxa group19,20, our modeling approach enabled evaluation of macro-ecological patterns shared by viruses from ecologically diverse viral genus across all mammal and bird species.
Our modeling approach enabled identification of key system-level signaling interactions that contribute to the observed responses.
The two-level modeling approach enabled us to control for confounding of one factor by the other: individual and contextual SEM, and physical environment.
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This tradeoff relationship, which is easily explored using the spatial resource allocation modeling approach, enables an MPA design to allow decision makers to select various non-inferior solutions to meet their decision preferences.
The main conclusion in the paper is that due to the low computation time, the convex modeling approach enables optimization of problems with two or more state variables, e.g. allowing for thermal models of the components; or to include more sizing variables, e.g. sizing of the engine and the electric machine simultaneously.
Additionally, modelling approaches enable eloquent exploration of large-scale and long-term datasets to test predictions and ultimately construct further hypotheses.
We chose to analyze the five probe means for each gene, rather than take an overall mean (or median), because our mixed-model approach enabled us to consider all this information in the model rather than discard the variation that may be present among different probes targeting the same gene.
This modelling approach enables to provide a predictive model involving very rapid calculation.
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