Form mega environments based on fitted values from an AMMI model

This function fits an AMMI model and then using the fitted values produces a new factor clustering the trials. This factor is added as a column megaEnv to the input data. If a column megaEnv already exists this column is overwritten with a warning.

Mega environments are created by grouping environments based on their best performing genotype; i.e. environments that share the same best genotype belong to the same mega environment.

Usage

gxeMegaEnv(
  TD,
  trials = names(TD),
  trait,
  method = c("max", "min"),
  byYear = FALSE
)

Arguments

TD: An object of class TD.
trials: A character string specifying the trials to be analyzed. If not supplied, all trials are used in the analysis.
trait: A character string specifying the trait to be analyzed.
method: A character string indicating the criterion to determine the best genotype per environment, either "max" or "min".
byYear: Should the analysis be done by year? If TRUE the data is split by the variable year, analysis is performed and the results are merged together and returned.

Value

An object of class megaEnv, a list consisting of

TD: An object of class TD, the TD object used as input to the function with an extra column megaEnv.
summTab: A data.frame, a summary table containing information on the trials in each mega environment.
trait: The trait used for calculating the mega environments.

References

Atlin, G. N., R. J. Baker, K. B. McRae, and X. Lu. 2000. Selection Response in Subdivided Target Regions. Crop Sci. 40:7-13. doi:10.2135/cropsci2000.4017

Examples

## Calculate mega environments for TDMaize.
gemegaEnv <- gxeMegaEnv(TD = TDMaize, trait = "yld")

## Calculate new mega environments based on the genotypes with the lowest
## value per environment.
gemegaEnv2 <- gxeMegaEnv(TD = TDMaize, trait = "yld", method = "min")