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. The MGIDI indes is computed as follows: \[MGIDI_i = \sqrt{\sum\limits_{j = 1}^f(F_{ij} - {F_j})^2}\]
where \(MGIDI_i\) is the multi-trait genotype-ideotype distance index for the ith genotype; \(F_{ij}\) is the score of the ith genotype in the jth factor (i = 1, 2, ..., g; j = 1, 2, ..., f), being g and f the number of genotypes and factors, respectively, and \(F_j\) is the jth score of the ideotype. The genotype with the lowest MGIDI is then closer to the ideotype and therefore presents desired values for all the analyzed traits.
mgidi( .data, use_data = "blup", SI = 15, mineval = 1, ideotype = NULL, use = "complete.obs", verbose = TRUE )
.data | An object fitted with the function |
---|---|
use_data | Define which data to use if |
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 |
use | The method for computing covariances in the presence of missing
values. Defaults to |
verbose | If |
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.
gen_ide The distance between the scores of each genotype with the ideotype.|
MGIDI The multi-trait genotype-ideotype distance 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.
total_gain The selection differential for the variables.
sel_gen The selected genotypes.
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
Tiago Olivoto tiagoolivoto@gmail.com
# \donttest{ library(metan) model <- gamem(data_g, gen = GEN, rep = REP, resp = c(NR, KW, CW, CL, NKE, TKW, PERK, PH))#>#>#>#>#> --------------------------------------------------------------------------- #> P-values for Likelihood Ratio Test of the analyzed traits #> --------------------------------------------------------------------------- #> model NR KW CW CL NKE TKW PERK PH #> Complete NA NA NA NA NA NA NA NA #> Genotype 0.0056 0.0253 1.24e-05 2.25e-06 0.00952 0.00955 4.65e-07 0.051 #> --------------------------------------------------------------------------- #> Variables with nonsignificant Genotype effect #> PH #> ---------------------------------------------------------------------------# Selection for increase all variables mgidi_model <- mgidi(model)#> #> ------------------------------------------------------------------------------- #> Principal Component Analysis #> ------------------------------------------------------------------------------- #> # A tibble: 8 x 4 #> PC Eigenvalues `Variance (%)` `Cum. variance (%)` #> <chr> <dbl> <dbl> <dbl> #> 1 PC1 3.89 48.6 48.6 #> 2 PC2 3.09 38.6 87.1 #> 3 PC3 0.52 6.48 93.6 #> 4 PC4 0.27 3.4 97.0 #> 5 PC5 0.18 2.23 99.2 #> 6 PC6 0.06 0.7 100. #> 7 PC7 0 0.04 100. #> 8 PC8 0 0.01 100 #> ------------------------------------------------------------------------------- #> Factor Analysis - factorial loadings after rotation- #> ------------------------------------------------------------------------------- #> # A tibble: 8 x 5 #> VAR FA1 FA2 Communality Uniquenesses #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 NR -0.07 -0.9 0.81 0.19 #> 2 KW -0.44 -0.84 0.91 0.09 #> 3 CW -0.94 -0.28 0.95 0.05 #> 4 CL -0.95 0.03 0.91 0.09 #> 5 NKE 0.41 -0.85 0.89 0.11 #> 6 TKW -0.91 0 0.83 0.17 #> 7 PERK 0.93 -0.15 0.89 0.11 #> 8 PH 0.02 -0.88 0.77 0.23 #> ------------------------------------------------------------------------------- #> Comunalit Mean: 0.8713971 #> ------------------------------------------------------------------------------- #> Selection differential #> ------------------------------------------------------------------------------- #> # A tibble: 8 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 CW FA 1 20.8 23.7 2.95 14.2 0.880 2.60 12.5 increa~ 100 #> 2 CL FA 1 28.4 28.7 0.300 1.06 0.901 0.270 0.950 increa~ 100 #> 3 TKW FA 1 318. 314. -3.67 -1.16 0.712 -2.62 -0.824 increa~ 0 #> 4 PERK FA 1 87.6 87.6 -0.0589 -0.0672 0.916 -0.0539 -0.0615 increa~ 0 #> 5 NR FA 2 15.8 17.4 1.63 10.3 0.736 1.20 7.60 increa~ 100 #> 6 KW FA 2 147. 163. 16.2 11.0 0.659 10.7 7.27 increa~ 100 #> 7 NKE FA 2 468. 532. 64.0 13.7 0.713 45.6 9.74 increa~ 100 #> 8 PH FA 2 2.17 2.28 0.110 5.07 0.610 0.0670 3.09 increa~ 100 #> ------------------------------------------------------------------------------ #> Selected genotypes #> ------------------------------------------------------------------------------- #> H13 H5 #> -------------------------------------------------------------------------------# }