Print a mtsi
object in two ways. By default, the results are shown in
the R console. The results can also be exported to the directory.
# S3 method for mtsi print(x, export = FALSE, file.name = NULL, digits = 4, ...)
x | An object of class |
---|---|
export | A logical argument. If |
file.name | The name of the file if |
digits | The significant digits to be shown. |
... | Options used by the tibble package to format the output. See
|
Tiago Olivoto tiagoolivoto@gmail.com
# \donttest{ library(metan) # Based on stability only MTSI_MODEL <- waasb(data_ge, resp = c(GY, HM), gen = GEN, env = ENV, rep = REP )#> Evaluating trait GY |====================== | 50% 00:00:00 Evaluating trait HM |============================================| 100% 00:00:01#>#>#>#>#> --------------------------------------------------------------------------- #> 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 1.37 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 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 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 #> -------------------------------------------------------------------------------#> -------------------- Correlation matrix used used in factor analysis ----------------- #> GY HM #> GY 1.0000000 0.3693246 #> HM 0.3693246 1.0000000 #> #> ---------------------------- Principal component analysis ----------------------------- #> # A tibble: 2 x 4 #> PC Eigenvalues `Variance (%)` `Cum. variance (%)` #> <chr> <dbl> <dbl> <dbl> #> 1 PC1 1.369 68.47 68.47 #> 2 PC2 0.6307 31.53 100 #> #> --------------------------------- Initial loadings ----------------------------------- #> # A tibble: 2 x 2 #> VAR initial_loadings #> <chr> <dbl> #> 1 GY 0.8274 #> 2 HM 0.8274 #> #> -------------------------- Loadings after varimax rotation --------------------------- #> # A tibble: 2 x 2 #> VAR FA1 #> <chr> <dbl> #> 1 GY 0.8274 #> 2 HM 0.8274 #> #> --------------------------- Scores for genotypes-ideotype ----------------------------- #> # A tibble: 11 x 2 #> GEN FA1 #> <chr> <dbl> #> 1 G1 2.862 #> 2 G10 0.9893 #> 3 G2 1.828 #> 4 G3 4.028 #> 5 G4 2.666 #> 6 G5 2.610 #> 7 G6 3.430 #> 8 G7 2.448 #> 9 G8 4.268 #> 10 G9 2.019 #> 11 ID1 5.035 #> #> ---------------------------- Multitrait stability index ------------------------------ #> # A tibble: 10 x 2 #> Genotype MTSI #> <chr> <dbl> #> 1 G8 0.7679 #> 2 G3 1.008 #> 3 G6 1.606 #> 4 G1 2.173 #> 5 G4 2.369 #> 6 G5 2.425 #> 7 G7 2.587 #> 8 G9 3.016 #> 9 G2 3.208 #> 10 G10 4.046 #> #> ------------------------- Selection differential (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 2.674 2.979 0.3052 11.41 0.8152 0.2488 9.305 increase 100 #> 2 HM FA 1 48.09 48.35 0.2648 0.5507 0.6862 0.1817 0.3779 increase 100 #> #> -------------------------------- Selected genotypes ----------------------------------- #> G8 G3# }