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 usemresp = 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()
orwaasb()
.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% 00:00:01
#> 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)
# }