correlations function

Compute different types of correlations.

Compute three types of correlations for models fitted with a nesting factor.

• correlation between scenarios or environment types: [REMOVE_ME]$\sigma_G^2 / (\sigma_G^2 + \sigma_{GS}^2) [REMOVE_ME_2]$
• correlation between trials within scenarios or environment types: [REMOVE_ME]$(\sigma_G^2 + \sigma_{GS}^2) / (\sigma_G^2 + \sigma_{GS}^2 +\sigma_E^2) [REMOVE_ME_2]$
• correlation between trials that belong to different scenarios/environment types: [REMOVE_ME]$\sigma_G^2 / (\sigma_G^2 + \sigma_{GS}^2 + \sigma_E^2) [REMOVE_ME_2]$

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.

correlations(varComp)

Arguments

• varComp: An object of class varComp.

Returns

A list with three correlations.

Description

Compute three types of correlations for models fitted with a nesting factor.

• correlation between scenarios or environment types:

• correlation between trials within scenarios or environment types:

• correlation between trials that belong to different scenarios/environment types:

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()