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Since habit and fruit consumption are highly correlated, variance inflation factors (VIF) and tolerance values were examined to assess multicollinearity.
Models were checked using Q-plots to assess the normality of residuals (with a mean of zero) from the linear models, and analyses of collinearity between variables were performed to avoid the inclusion of two variables highly correlated (Variance Inflation Factor values below 10 indicates no collinearity between variables).
CAPS compares the correlated variance of the evolutionary rates at two sites corrected by the time since the divergence of the protein sequences [43].
However, all the variables considered by Sharp et al. were examined individually, without a multivariate analysis that could enable detection of correlated variance components.
In one of them, the correlated variance of the evolutionary rates between amino acid sites was calculated in CAPS software [ 67].
Broadly, CAPS compares the correlated variance of the evolutionary rates at two sites corrected by the time since the divergence of the two sequences.
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Excluding factors with low correlations and allowing for several correlated variances resulted in a good fitting model explaining 95% of the variance in the data.
Additionally, Scree plot is used to correlate variance in each component as a function of the component's number.
Interaction mean was highly correlated with variance (supplementary table S1, Supplementary Material online) but was not a statistically significant factor in our analyses (supplementary table S2, Supplementary Material online).
Although predictors were correlated, all variance inflation factors (vif) were less than 5 (vif summer temperature = 2.57; vif latitude = 3.76; vif longitude = 2.13) which indicated that multicolinearity was probably not the problem.
The structural variance is correlated with the variance in properties and structure/property maps are produced in the PCA space.
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