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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.

Usage

# S3 method for mtsi
print(x, export = FALSE, file.name = NULL, digits = 4, ...)

Arguments

x

An object of class mtsi.

export

A logical argument. If TRUE|T, a *.txt file is exported to the working directory

file.name

The name of the file if export = TRUE

digits

The significant digits to be shown.

...

Options used by the tibble package to format the output. See tibble::print() for more details.

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \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 

#> 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

MTSI_index <- mtsi(MTSI_MODEL)
#> 
#> -------------------------------------------------------------------------------
#> Principal Component Analysis
#> -------------------------------------------------------------------------------
#> # A tibble: 2 × 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 × 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 × 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 × 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
#> -------------------------------------------------------------------------------
print(MTSI_index)
#> -------------------- 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 × 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 × 2
#>   VAR   initial_loadings
#>   <chr>            <dbl>
#> 1 GY              0.8274
#> 2 HM              0.8274
#> 
#> -------------------------- Loadings after varimax rotation ---------------------------
#> # A tibble: 2 × 2
#>   VAR      FA1
#>   <chr>  <dbl>
#> 1 GY    0.8274
#> 2 HM    0.8274
#> 
#> --------------------------- Scores for genotypes-ideotype -----------------------------
#> # A tibble: 11 × 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 × 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 × 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
# }