Compute BLUPS based on a set of mega environments

This function calculates Best Linear Unbiased Predictors (BLUPS) and associated standard errors based on a set of mega environments.

Usage

# S3 method for megaEnv
predict(
  object,
  ...,
  trials = names(object$TD),
  useYear = FALSE,
  engine = c("lme4", "asreml")
)

Arguments

object: An object of class megaEnv.
...: Further parameters passed to either asreml or lmer.
trials: A character string specifying the trials to be analyzed. If not supplied, all trials are used in the analysis.
useYear: Should year be used for modeling (as years within trials). If TRUE, TD should contain a column "year".
engine: A character string specifying the engine used for modeling.

Value

A list consisting of two data.frames, predictedValue

containing BLUPs per genotype per mega environment and standardError

containing standard errors for those BLUPs.

Examples

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

## Compute BLUPS and standard errors for those mega environments.
megaEnvPred <- predict(geMegaEnv)
#> Warning: One should be cautious with the interpretation of predictions for mega environments that are based on less than 10 trials.
#> boundary (singular) fit: see help('isSingular')
head(megaEnvPred$predictedValue)
#>      megaEnv_1 megaEnv_2 megaEnv_3
#> G001  489.0515  483.5317  497.8785
#> G002  472.3096  491.5258  498.1123
#> G003  468.2102  457.0879  462.5265
#> G004  363.6653  361.1352  321.1292
#> G005  462.8374  465.0800  467.9159
#> G006  435.4026  419.0985  410.4387
head(megaEnvPred$standardError)
#>      megaEnv_1 megaEnv_2 megaEnv_3
#> G001  32.09261  38.36256  45.40008
#> G002  32.09261  38.36256  45.40008
#> G003  32.09261  38.36256  45.40008
#> G004  32.09261  38.36256  45.40008
#> G005  32.09261  38.36256  45.40008
#> G006  32.09261  38.36256  45.40008

Usage

Arguments

Value

See also

Examples