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Makes a radar plot showing the multitrait stability index proposed by Olivoto et al. (2019)

Usage

# S3 method for mtsi
plot(
  x,
  SI = 15,
  type = "index",
  position = "fill",
  genotypes = "selected",
  title = TRUE,
  radar = TRUE,
  arrange.label = FALSE,
  x.lab = NULL,
  y.lab = NULL,
  size.point = 2.5,
  size.line = 0.7,
  size.text = 10,
  width.bar = 0.75,
  n.dodge = 1,
  check.overlap = FALSE,
  invert = FALSE,
  col.sel = "red",
  col.nonsel = "black",
  legend.position = "bottom",
  ...
)

Arguments

x

An object of class mtsi

SI

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

type

The type of the plot. Defaults to "index". Use type = "contribution" to show the contribution of each factor to the MGIDI index of the selected genotypes.

position

The position adjustment when type = "contribution". Defaults to "fill", which shows relative proportions at each trait by stacking the bars and then standardizing each bar to have the same height. Use position = "stack" to plot the MGIDI index for each genotype.

genotypes

When type = "contribution" defines the genotypes to be shown in the plot. By default (genotypes = "selected" only selected genotypes are shown. Use genotypes = "all" to plot the contribution for all genotypes.)

title

Logical values (Defaults to TRUE) to include automatically generated titles.

radar

Logical argument. If true (default) a radar plot is generated after using coord_polar().

arrange.label

Logical argument. If TRUE, the labels are arranged to avoid text overlapping. This becomes useful when the number of genotypes is large, say, more than 30.

x.lab, y.lab

The labels for the axes x and y, respectively. x label is set to null when a radar plot is produced.

size.point

The size of the point in graphic. Defaults to 2.5.

size.line

The size of the line in graphic. Defaults to 0.7.

size.text

The size for the text in the plot. Defaults to 10.

width.bar

The width of the bars if type = "contribution". Defaults to 0.75.

n.dodge

The number of rows that should be used to render the x labels. This is useful for displaying labels that would otherwise overlap.

check.overlap

Silently remove overlapping labels, (recursively) prioritizing the first, last, and middle labels.

invert

Logical argument. If TRUE, rotate the plot.

col.sel

The colour for selected genotypes. Defaults to "red".

col.nonsel

The colour for nonselected genotypes. Defaults to "black".

legend.position

The position of the legend.

...

Other arguments to be passed from ggplot2::theme().

Value

An object of class gg, ggplot.

References

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. (in press).

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{
library(metan)
mtsi_model <- waasb(data_ge, ENV, GEN, REP, resp = c(GY, HM))
#> 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
#> -------------------------------------------------------------------------------
plot(mtsi_index)

# }