Calculate the heritability based on the fitted model. The heritability is
calculated as described by Atlin et al. E.g. for a model with trials nested
within locations, which has a random part that looks like this: genotype +
genotype:location + genotype:location:trial the heritability is computed
as
$$\sigma_G^2 / (\sigma_G^2 + \sigma_L^2 / l + \sigma_{LT}^2 / lt +
\sigma_E^2 / ltr)$$
In this formula the \(\sigma\) terms stand for the standard deviations of
the respective model terms, and the lower case letters for the number of
levels for the respective model terms. So \(\sigma_L\) is the standard
deviation for the location term in the model and \(l\) is the number of
locations. \(\sigma_E\) corresponds to the residual standard deviation and
\(r\) to the number of replicates.
References
Atlin, G. N., Baker, R. J., McRae, K. B., & Lu, X. (2000). Selection response in subdivided target regions. Crop Science, 40(1), 7–13. doi:10.2135/cropsci2000.4017
See also
Other Mixed model analysis:
CRDR(),
correlations(),
diagnostics(),
gxeVarComp(),
plot.varComp(),
predict.varComp(),
vc()
Examples
## Fit a mixed model.
geVarComp <- gxeVarComp(TD = TDMaize, trait = "yld")
## Compute heritability.
herit(geVarComp)
#> [1] 0.3489369