This function computes the WAASY or WAASBY indexes (Olivoto et al., 2019) considering different scenarios of weights for stability and mean performance.

wsmp( model, mresp = 100, increment = 5, saveWAASY = 50, prob = 0.05, progbar = TRUE )

model | Should be an object of class |
---|---|

mresp | A numeric value that will be the new maximum value after
rescaling. By default, the variable in |

increment | The increment in the weight ratio for stability and mean
performance. Se the |

saveWAASY | Automatically save the WAASY values when the weight for
stability is |

prob | The p-value for considering an interaction principal component
axis significant. must be multiple of |

progbar | A logical argument to define if a progress bar is shown.
Default is |

An object of class `wsmp`

with the following items for each
variable:

**scenarios**A list with the model for all computed scenarios.**WAASY**The values of the WAASY estimated when the weight for the stability in the loop match with argument`saveWAASY`

.**hetdata, hetcomb**The data used to produce the heatmaps.**Ranks**All the values of WAASY estimated in the different scenarios of WAAS/GY weighting ratio.

After fitting a model with the functions `waas`

or
`waasb`

it is possible to compute the superiority indexes WAASY
or WAASBY in different scenarios of weights for stability and mean
performance. The number of scenarios is defined by the arguments
`increment`

. By default, twenty-one different scenarios are computed. In
this case, the the superiority index is computed considering the following
weights: stability (waasb or waas) = 100; mean performance = 0. In other
words, only stability is considered for genotype ranking. In the next
iteration, the weights becomes 95/5 (since increment = 5). In the third
scenario, the weights become 90/10, and so on up to these weights become
0/100. In the last iteration, the genotype ranking for WAASY or WAASBY
matches perfectly with the ranks of the response variable.

Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019. Mean performance and stability in multi-environment trials I: Combining features of AMMI and BLUP techniques. Agron. J. doi:10.2134/agronj2019.03.0220

#>#>#>#>#> --------------------------------------------------------------------------- #> P-values for Likelihood Ratio Test of the analyzed traits #> --------------------------------------------------------------------------- #> model PH #> COMPLETE NA #> GEN 9.39e-01 #> GEN:ENV 1.09e-13 #> --------------------------------------------------------------------------- #> All variables with significant (p < 0.05) genotype-vs-environment interactionscenarios <- wsmp(model) # }