Description Usage Arguments Details Value Author(s) See Also Examples

Compute the Weighted Average of Absolute Scores for quantifying the stability in multienvironment trials using mixed-effect models.

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`.data` |
The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s). |

`resp` |
The response variable(s). To analyze multiple variables in a single procedure a vector of variables may be used. For example |

`gen` |
The name of the column that contains the levels of the genotypes. |

`env` |
The name of the column that contains the levels of the environments. |

`rep` |
The name of the column that contains the levels of the replications/blocks. |

`mresp` |
A numeric vector of the same length of |

`wresp` |
The weight for the response variable(s) for computing the WAASBY index. Must be a numeric vector of the same length of |

`random` |
The effects of the model assumed to be random. Default is |

`prob` |
The probability for estimating confidence interval for BLUP's prediction. |

`verbose` |
Logical argument. If |

This function compute the weighted average of absolute scores considering all principal component axis from the Singular Value Decomposition (SVD) of the BLUP'S GxE effects matrix generated by a linear mixed-effect model. The main advantage of this procedure in relation to the `WAAS.AMMI`

function is that random effects can be included in the model. In addition, unbalanced datasets can also be modeled.

The function returns the results in a list for each analyzed variable. For each variable, the following objects are returned.

`individual` |
A within-environments ANOVA considering a fixed-effect model. |

`fixed` |
Test for fixed effects. |

`random` |
Variance components for random effects. |

`LRT` |
The Likelihood Ratio Test for the random effects. |

`model` |
A data frame with the response variable, the scores of all Principal Components, the estimates of Weighted Average of Absolute Scores, and WAASY (the index that consider the weights for stability and productivity in the genotype ranking. |

`blupGEN` |
The estimated BLUPS for genotypes (If |

`BLUPenv` |
The estimated BLUPS for environments, (If |

`BLUPge` |
The estimated BLUPS of all genotypes in all environments "BLUPij". |

`PCA` |
The results of Principal Component Analysis with eigenvalues and explained variance of BLUP-interaction matrix. |

`MeansGxE` |
The phenotypic means of genotypes in the environments, with observed, predicted and OLS residual prediction. |

`Details` |
A list summarizing the results. The following information are showed. |

`ESTIMATES` |
A list with the following values: |

`residuals` |
The residuals of the model. |

Tiago Olivoto [email protected]

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library(METAAB)
# Genotypes as random effects and equal weights for both
# response variable and stability
model <- WAASB(data_ge,
resp = GY,
gen = GEN,
env = ENV,
rep = REP,
wresp = 70)
# Higher weight for response variable
model2 <- WAASB(data_ge,
resp = GY,
gen = GEN,
env = ENV,
rep = REP,
wresp = 65)
# Environment as random effects analyzing more than one variables
# considering that smaller values of HM are better and higher
# values of GY are better, assigning a larger weight for the GY
# and a smaller weight for HM when computing WAASBY index.
model3 <- WAASB(data_ge,
random = "env",
resp = c(GY, HM),
gen = GEN,
env = ENV,
rep = REP,
mresp = c(100, 0),
wresp = c(60, 40))
``` |

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