metan (version 1.2.1)

mtsi: Multi-trait stability index

Description

Computes the multi-trait stability index proposed by Olivoto et al. (2019)

Usage

mtsi(.data, index = "waasby", SI = 15, mineval = 1, verbose = TRUE)

Arguments

.data

An object of class waasb or waas.

index

If index = 'waasby' (default) both stability and mean performance are considered. If index = 'waasb' the multi-trait index will be computed considering the stability of genotypes only. More details can be seen in waasb and waas functions.

SI

An integer (0-100). The selection intensity in percentage of the total number of genotypes.

mineval

The minimum value so that an eigenvector is retained in the factor analysis.

verbose

If verbose = TRUE (Default) then some results are shown in the console.

Value

An object of class mtsi with the following items:

• data The data used to compute the factor analysis.

• cormat The correlation matrix among the environments.

• PCA The eigenvalues and explained variance.

• FA The factor analysis.

• KMO The result for the Kaiser-Meyer-Olkin test.

• MSA The measure of sampling adequacy for individual variable.

• communalities The communalities.

• communalities.mean The communalities' mean.

• initial.loadings The initial loadings.

• finish.loadings The final loadings after varimax rotation.

• canonical.loadings The canonical loadings.

• scores.gen The scores for genotypes in all retained factors.

• scores.ide The scores for the ideotype in all retained factors.

• MTSI The multi-trait stability index.

• contri.fac The relative contribution of each factor on the MTSI value. The lower the contribution of a factor, the close of the ideotype the variables in such factor are.

• sel.dif The selection differential for the WAASBY or WAASB index.

• mean.sd The mean for the differential selection.

• sel.dif.var The selection differential for the variables.

• Selected The selected genotypes.

References

Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, B.G. Sari, and M.I. Diel. 2019. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. Agron. J. (in press).

Examples

# NOT RUN {
library(metan)

# Based on stability only, for both GY and HM, higher is better
mtsi_model <- waasb(data_ge,
                    env = ENV,
                    gen = GEN,
                    rep = REP,
                    resp = c(GY, HM))
mtsi_index <- mtsi(mtsi_model, index = 'waasb')


# Based on mean performance and stability (using pipe operator %>%)
# GY: higher is better
# HM: lower is better

mtsi_index2 <- data_ge %>%
 waasb(ENV, GEN, REP,
       resp = c(GY, HM),
       mresp = c(100, 0)) %>%
 mtsi()
# }