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Instead of treating different markers as components and constructing PCs to represent the main variations from all markers as was traditional ([10] e.g)., in this new approach, Patterson et al. [8] indexed the random vector by individuals, taking genotype data at different markers as its realizations.
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Under ANOVA, statistical tests use an estimate of within-group variation from all samples.
Additionally, heterogeneity (measured as I) was used to assess the percentage of the total variation from all studies.
The objective of the method proposed here is to describe the relevant differences gradually instead of explaining all variation from all classes at once.
Instead, a measure that incorporates variation from all modes of inheritance will offer more explanatory power (Danchin and Wagner 2010; Danchin et al. 2011).
The measure of heterogeneity was evaluated by using I, which was used to assess the percentage of the total variation from all studies to define the heterogeneity.
This last point is important as it allows a user to understand when their assumed model accounts for variation from all relevant study-specific variables.
As for bristle traits, we calculated the trace of the total covariance matrix for among-individual variation from all the components of PCA, and used it as an index of among-individual variation.
The active responses are captured by determining all possible state variations resulting from all possible parameters perturbations.
Variations in discharges from all treatments were closely correlated to soil moisture status, 30 days antecedent rainfall and rainfall event intensity.
A monthly averaged diurnal variation was obtained from all the observations and model output in the months studied, and the corresponding difference was also calculated.
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