[Experimental]

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

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.

  • 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 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.

    • h2: The broad-sense heritability.

    • 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.

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

See also

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{ 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())
#> Evaluating trait GY |====================== | 50% 00:00:00 Evaluating trait HM |============================================| 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 GY HM #> COMPLETE NA NA #> GEN 1.11e-05 5.07e-03 #> GEN:ENV 2.15e-11 2.27e-15 #> --------------------------------------------------------------------------- #> All variables with significant (p < 0.05) genotype-vs-environment interaction
#> Mean performance: blupg
#> Stability: waasb
selection <- mtmps(model)
#> #> ------------------------------------------------------------------------------- #> Principal Component Analysis #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 4 #> PC Eigenvalues `Variance (%)` `Cum. variance (%)` #> <chr> <dbl> <dbl> <dbl> #> 1 PC1 1.37 68.5 68.5 #> 2 PC2 0.631 31.5 100 #> ------------------------------------------------------------------------------- #> Factor Analysis - factorial loadings after rotation- #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 4 #> VAR FA1 Communality Uniquenesses #> <chr> <dbl> <dbl> <dbl> #> 1 GY 0.827 0.685 0.315 #> 2 HM 0.827 0.685 0.315 #> ------------------------------------------------------------------------------- #> Comunalit Mean: 0.6846623 #> ------------------------------------------------------------------------------- #> Selection differential for the mean performance and stability index #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 6 #> VAR Factor Xo Xs SD SDperc #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 GY FA 1 48.3 86.4 38.0 78.7 #> 2 HM FA 1 58.3 79.2 21.0 36.0 #> ------------------------------------------------------------------------------- #> Selection differential for the mean of the variables #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 11 #> VAR Factor Xo Xs SD SDperc h2 SG SGperc sense goal #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> #> 1 GY FA 1 2.67 2.98 0.305 11.4 0.815 0.249 9.31 increase 100 #> 2 HM FA 1 48.1 48.4 0.265 0.551 0.686 0.182 0.378 increase 100 #> ------------------------------------------------------------------------------ #> Selected genotypes #> ------------------------------------------------------------------------------- #> G8 G3 #> -------------------------------------------------------------------------------
# gains for stability selection$sel_dif_stab
#> # A tibble: 2 x 7 #> VAR Xo Xs SD SDperc sense goal #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> #> 1 GY 0.250 0.182 -0.0686 -27.4 decrease 100 #> 2 HM 0.614 0.400 -0.214 -34.9 decrease 100
# gains for mean performance selection$sel_dif_trait
#> # A tibble: 2 x 11 #> VAR Factor Xo Xs SD SDperc h2 SG SGperc sense goal #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> #> 1 GY FA 1 2.67 2.98 0.305 11.4 0.815 0.249 9.31 increase 100 #> 2 HM FA 1 48.1 48.4 0.265 0.551 0.686 0.182 0.378 increase 100
# }