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
)

## Arguments

model Should be an object of class waas or waasb. A numeric value that will be the new maximum value after rescaling. By default, the variable in resp is rescaled so that the original maximum and minimum values are 100 and 0, respectively. Let us consider that for a specific trait, say, lodging incidence, lower values are better. In this case, you should use mresp = 0 to rescale the response variable so that the lowest values will become 100 and the highest values 0. The increment in the weight ratio for stability and mean performance. Se the Details section for more information. Automatically save the WAASY values when the weight for stability is saveWAASY. Default is 50. Please, note that saveWAASY The p-value for considering an interaction principal component axis significant. must be multiple of increment. If this assumption is not valid, an error will be occur. A logical argument to define if a progress bar is shown. Default is TRUE.

## Value

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.

## Details

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.

## References

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

resca

## Examples

# \donttest{
library(metan)
model <- waasb(data_ge2,
env = ENV,
gen = GEN,
rep = REP,
resp = PH)#> Method: REML/BLUP#> Random effects: GEN, GEN:ENV#> Fixed effects: ENV, REP(ENV)#> Denominador DF: Satterthwaite's method#> ---------------------------------------------------------------------------
#> 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)
# }