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b Variable cluster size.
Variable cluster analysis identified five cluster groups (Table 2).
Variable cluster analysis identified five chronobiological clusters in 105 BDI subjects.
In a BDI sample, variable cluster analysis was able to group related chronobiological variables.
Variable cluster analysis was able to successfully identify and cluster chronobiological variables with related characteristics.
Variable cluster analysis proved to be a statistical methodology able to group chronobiological variables based on related characteristics in BDI.
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First, a cluster analysis was performed, using model-based (Mclust) and variable clustering.
We explored the relationships among these phenotypes using variable clustering and then estimated their genetic heritabilities and cross-trait correlations.
Specifically, an iterative approach is developed by integrating the technical strengths of Variable Clustering and Random Forest to remove collinearity, redundancy and nonrelevance.
By classifying outcomes as a categorical variable, clusters of 'potentially low' outcomes can also be mapped, thereby identifying populations whose recovery status may decrease.
Several molecular methods have been used for typing Y. pestis, with variable clustering and discriminating ability.
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