mtmps: Multi-trait mean performance and stability index In metan: Multi Environment Trials Analysis

Description

Computes the multi-trait stability index proposed by Olivoto et al. (2019) considering different parametric and non-parametric stability indexes.

Usage

 1 mtmps(model, SI = 15, mineval = 1, verbose = TRUE)

Arguments

 model An object of class mtmps 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), some results are shown in the console.

Value

An object of class mtmps 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.

• 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 mean performance and 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.

• contri_fac_rank, contri_fac_rank_sel The rank for the contribution of each factor for all genotypes and selected genotypes, respectively.

• sel_dif_trait, sel_dif_stab, sel_dif_mps A data frame containing the selection differential (gains) for the mean performance, stability index, and mean performance and stability index, respectively. The following variables are shown.

• VAR: the trait's name.

• Factor: The factor that traits where grouped into.

• Xo: The original population mean.

• Xs: The mean of selected genotypes.

• SD and SDperc: The selection differential and selection differential in percentage, respectively.

• SG and SGperc: The selection gains and selection gains in percentage, respectively.

• sense: The desired selection sense.

• goal: selection gains match desired sense? 100 for yes and 0 for no.

• stat_dif_trait, stat_dif_stab, stat_dif_mps A data frame with the descriptive statistic for the selection gains for the mean performance, stability index, and mean performance and stability index, respectively. The following columns are shown by sense.

• sense: The desired selection sense.

• variable: the trait's name.

• min: the minimum value for the selection gain.

• mean: the mean value for the selection gain.

• ci: the confidence interval for the selection gain.

• sd.amo: the standard deviation for the selection gain.

• max: the maximum value for the selection gain.

• sum: the sum of the selection gain.

• sel_gen The selected genotypes.

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com

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/agronj2019.03.0220

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 library(metan) # The same approach as mtsi() # mean performance and stability for GY and HM # mean performance: The genotype's BLUP # stability: the WAASB index (lower is better) # weights: equal for mean performance and stability model <- mps(data_ge, env = ENV, gen = GEN, rep = REP, resp = everything()) selection <- mtmps(model) # gains for stability selection$sel_dif_stab # gains for mean performance selection$sel_dif_trait