mtmps: Multi-trait mean performance and stability index

View source: R/mtmps.R

mtmpsR Documentation

Multi-trait mean performance and stability index



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)



An object of class mtmps


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


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


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


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.


Tiago Olivoto


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

mgidi(), mps(), get_model_data()


# 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 <-
    env = ENV,
    gen = GEN,
    rep = REP,
    resp = everything())
selection <- mtmps(model)

# gains for stability

# gains for mean performance

metan documentation built on March 7, 2023, 5:34 p.m.