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

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.

## Arguments

- model
- mresp
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.- increment
The increment in the weight ratio for stability and mean performance. Se the

**Details**section for more information.- saveWAASY
Automatically save the WAASY values when the weight for stability is

`saveWAASY`

.- prob
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.- progbar
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

When computed with

`waas()`

or`waasb()`

.**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.

When computed with

`mps()`

**hetcomb**showing the rank for mean performance and stability in the different weights.

## 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

## Author

Tiago Olivoto tiagoolivoto@gmail.com

## Examples

```
# \donttest{
library(metan)
# using the WAASB as statistic and BLUP as mean performance
# the same as using waasb()
model <- mps(data_ge2,
env = ENV,
gen = GEN,
rep = REP,
resp = PH)
#> Evaluating trait PH |============================================| 100% 00:00:00
#> 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 interaction
#> Mean performance: blupg
#> Stability: waasb
scenarios <- wsmp(model)
# }
```