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

Compute the Weighted Average of Absolute Scores for AMMI analysis.

1 2 3 |

`.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 |

`prob` |
The p-value for considering a IPCA significant. |

`naxis` |
The number of IPCAs to be used for computing the WAAS index. Default is |

`verbose` |
Logical argument. If |

This function compute the weighted average of absolute scores, estimated as follows:

* WAAS_i = ∑_{k = 1}^{p} |IPCA_{ik} \times EP_k|/ ∑_{k = 1}^{p}EP_k*

where *WAAS_i* is the weighted average of absolute scores of the *i*th genotype; *PCA_{ik}* is the score of the *i*th genotype in the *k*th IPCA; and *EP_k* is the explained variance of the *k*th IPCA for *k = 1,2,..,p*, considering *p* the number of significant PCAs, or a declared number of PCAs. For example if `prob = 0.05`

, all axis that are significant considering this probability level are used. The number of axis can be also informed by declaring `naxis = x`

. This comand ignores the `p.valuePC`

comand.

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

`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. |

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

`PCA` |
Principal Component Analysis. |

`anova` |
Joint analysis of variance for the main effects and Principal Component analysis of the interaction effect. |

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

Tiago Olivoto [email protected]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
library(METAAB)
# Considering p-value <= 0.05 to compute the WAAS
model <- WAAS.AMMI(data_ge,
resp = GY,
gen = GEN,
env = ENV,
rep = REP)
# Declaring the number of axis to be used for computing WAAS
# and assigning a larger weight for the response variable when
# computing the WAASBY index.
model2 <- WAAS.AMMI(data_ge,
resp = GY,
gen = GEN,
env = ENV,
rep = REP,
naxis = 3,
wresp = 60)
# Analyzing multiple variables (GY and HM) at the same time
# 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 <- WAAS.AMMI(data_ge,
resp = c(GY, HM),
gen = GEN,
env = ENV,
rep = REP,
mresp = c(100, 0),
wresp = c(60, 40))
``` |

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