Compute three types of correlations for models fitted with a nesting factor.
correlation between scenarios or environment types: $$\sigma_G^2 / (\sigma_G^2 + \sigma_{GS}^2)$$
correlation between trials within scenarios or environment types: $$(\sigma_G^2 + \sigma_{GS}^2) / (\sigma_G^2 + \sigma_{GS}^2 + \sigma_E^2)$$
correlation between trials that belong to different scenarios/environment types: $$\sigma_G^2 / (\sigma_G^2 + \sigma_{GS}^2 + \sigma_E^2)$$
In these formulas the \(\sigma\) terms stand for the standard deviations of the respective model terms. So \(\sigma_S\) is the standard deviation for the scenario term in the model, \(\sigma_{GS}\) for the standard deviation of the genotype by scenario term and \(\sigma_E\) corresponds to the residual standard deviation.
See also
Other Mixed model analysis:
CRDR(),
diagnostics(),
gxeVarComp(),
herit(),
plot.varComp(),
predict.varComp(),
vc()