Computes the multi-trait stability index proposed by Olivoto et al. (2019)
mtsi(.data, index = "waasby", SI = 15, mineval = 1, verbose = TRUE)
.data | An object of class |
---|---|
index | 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. |
verbose | If |
An object of class mtsi
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 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.
sel.dif The selection differential for the WAASBY or WAASB index.
mean.sd The mean for the differential selection.
sel.dif.var The selection differential for the variables.
Selected 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) # Based on stability only, for both GY and HM, higher is better mtsi_model <- waasb(data_ge, env = ENV, gen = GEN, rep = REP, resp = c(GY, HM))#>#>#>#>#> --------------------------------------------------------------------------- #> 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 interactionmtsi_index <- mtsi(mtsi_model, index = 'waasb')#> #> ------------------------------------------------------------------------------- #> Principal Component Analysis #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 4 #> PC Eigenvalues `Variance (%)` `Cum. variance (%)` #> <chr> <dbl> <dbl> <dbl> #> 1 PC1 1.66 82.8 82.8 #> 2 PC2 0.343 17.2 100 #> ------------------------------------------------------------------------------- #> Factor Analysis - factorial loadings after rotation- #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 4 #> VAR FA1 Communality Uniquenesses #> <chr> <dbl> <dbl> <dbl> #> 1 GY 0.910 0.828 0.172 #> 2 HM 0.910 0.828 0.172 #> ------------------------------------------------------------------------------- #> Comunalit Mean: 0.8283129 #> ------------------------------------------------------------------------------- #> Selection differential for the waasb index #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 6 #> VAR Factor Xo Xs SD SDperc #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 GY FA 1 0.250 0.117 -0.134 -53.5 #> 2 HM FA 1 0.614 0.373 -0.241 -39.2 #> ------------------------------------------------------------------------------ #> Mean of selection differential #> ------------------------------------------------------------------------------- #> Xo Xs SD SDperc #> 0.4323231 0.2448764 -0.1874467 -46.3570842 #> ------------------------------------------------------------------------------- #> Selection differential for the mean of the variables #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 8 #> VAR Factor xo Xs SD SDperc sense goal #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> #> 1 GY FA 1 2.67 2.78 0.105 3.94 increase 100 #> 2 HM FA 1 48.1 47.3 -0.747 -1.55 increase 0 #> ------------------------------------------------------------------------------ #> Selected genotypes #> ------------------------------------------------------------------------------- #> G3 G1 #> -------------------------------------------------------------------------------# Based on mean performance and stability (using pipe operator %>%) # GY: higher is better # HM: lower is better mtsi_index2 <- data_ge %>% waasb(ENV, GEN, REP, resp = c(GY, HM), mresp = c(100, 0)) %>% mtsi()#>#>#>#>#> --------------------------------------------------------------------------- #> 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 #> #> ------------------------------------------------------------------------------- #> Principal Component Analysis #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 4 #> PC Eigenvalues `Variance (%)` `Cum. variance (%)` #> <chr> <dbl> <dbl> <dbl> #> 1 PC1 1.58 79.2 79.2 #> 2 PC2 0.415 20.8 100 #> ------------------------------------------------------------------------------- #> Factor Analysis - factorial loadings after rotation- #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 4 #> VAR FA1 Communality Uniquenesses #> <chr> <dbl> <dbl> <dbl> #> 1 GY 0.890 0.792 0.208 #> 2 HM 0.890 0.792 0.208 #> ------------------------------------------------------------------------------- #> Comunalit Mean: 0.7922501 #> ------------------------------------------------------------------------------- #> Selection differential for the waasby index #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 6 #> VAR Factor Xo Xs SD SDperc #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 GY FA 1 48.3 76.5 28.2 58.3 #> 2 HM FA 1 54.1 86.2 32.1 59.3 #> ------------------------------------------------------------------------------ #> Mean of selection differential #> ------------------------------------------------------------------------------- #> Xo Xs SD SDperc #> 51.22793 81.37416 30.14624 58.82034 #> ------------------------------------------------------------------------------- #> Selection differential for the mean of the variables #> ------------------------------------------------------------------------------- #> # A tibble: 2 x 8 #> VAR Factor xo Xs SD SDperc sense goal #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> #> 1 GY FA 1 2.67 2.78 0.105 3.94 increase 100 #> 2 HM FA 1 48.1 47.3 -0.747 -1.55 decrease 100 #> ------------------------------------------------------------------------------ #> Selected genotypes #> ------------------------------------------------------------------------------- #> G3 G1 #> -------------------------------------------------------------------------------# }