metan (version 1.5.0)

mgidi: Genotype-Ideotype Distance Index

Description

Computes the multi-trait genotype-ideotype distance index (MGIDI). MGIDI can be seen as the multi-trait stability index (Olivoto et al., 2019) computed with weight for mean performance equals to 100.

Usage

mgidi(.data, SI = 15, mineval = 1, ideotype = NULL, verbose = TRUE)

Arguments

.data

An object fitted with gamem, gamem_met, or a two-way table with BLUPs for genotypes in each trait (genotypes in rows and traits in columns). In the last case, row names must contain the genotypes names.

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.

ideotype

A vector of length nvar where nvar is the number of variables used to plan the ideotype. Use 'h' to indicate the variables with increase in selction or 'l' to indicate the variables with reduction in selection. For example, ideotype = c("h, h, l, h, l").

verbose

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

Value

An object of class mgidi 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.

• MGIDI The multi-trait stability index.

• contri.fac The relative contribution of each factor on the MGIDI 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 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. 111:2961-2969. doi:10.2134/agronj2019.03.0221.

Examples

# NOT RUN {
library(metan)

model <- gamem(data_g,
               gen = GEN,
               rep = REP,
               resp = c(NR, KW, CW, CL, NKE, TKW, PERK, PH))
# Selection for increase all variables
mgidi_model <- mgidi(model)

# }