Analysis of Molecular Variance (AMOVA) partitioned 96% genetic variance within species and 4% among species.
Still, residual analysis showed unique common variance within the social memory domain.
The terrain analysis variables did not explain a large amount of variance within the models.
This method maximizes the variance between classes and reduces the variance within classes.
In PESA the first two axes explain 66% of the variance within the phenological metrics.
Between 19 and 78 % of the variance within the observed variables are explained by the factors.
There is much less variance within each quadrant than when the data are pooled.
The feature vectors of corresponding classes are whitened to obtain unit variance within each class.
Comparatively speaking, the variance between marker genotypes decreased most drastically, followed by the polygenic variance within marker genotypes and then the recombinant variance within marker genotypes, which suggested a decrease of power for QTL linkage analysis.
