Makes a radar plot showing the multi-trait genotype-ideotype distance index

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

Arguments

x

An object of class mgidi

SI

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

radar

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

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/treatments.

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/treatment.

rotate

Logical argument. If rotate = TRUE the plot is rotated, i.e., traits in y axis and value in the x axis.

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.)

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.

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.

title

The plot title when type = "contribution".

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.

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.

col.sel

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

col.nonsel

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

legend.position

The position of the legend.

...

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

Value

An object of class gg, ggplot.

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{
library(metan)
model <- gamem(data_g,
               gen = GEN,
               rep = REP,
               resp = c(KW, NR, NKE, NKR))
#> Evaluating trait KW |===========                                 | 25% 00:00:00 
Evaluating trait NR |======================                      | 50% 00:00:00 
Evaluating trait NKE |================================           | 75% 00:00:00 
Evaluating trait NKR |===========================================| 100% 00:00:00 

#> Method: REML/BLUP
#> Random effects: GEN
#> Fixed effects: REP
#> Denominador DF: Satterthwaite's method
#> ---------------------------------------------------------------------------
#> P-values for Likelihood Ratio Test of the analyzed traits
#> ---------------------------------------------------------------------------
#>     model     KW     NR     NKE   NKR
#>  Complete     NA     NA      NA    NA
#>  Genotype 0.0253 0.0056 0.00952 0.216
#> ---------------------------------------------------------------------------
#> Variables with nonsignificant Genotype effect
#> NKR 
#> ---------------------------------------------------------------------------
mgidi_index <- mgidi(model)
#> 
#> -------------------------------------------------------------------------------
#> Principal Component Analysis
#> -------------------------------------------------------------------------------
#> # A tibble: 4 x 4
#>   PC    Eigenvalues `Variance (%)` `Cum. variance (%)`
#>   <chr>       <dbl>          <dbl>               <dbl>
#> 1 PC1          2.42          60.6                 60.6
#> 2 PC2          1.19          29.8                 90.3
#> 3 PC3          0.32           8                   98.3
#> 4 PC4          0.07           1.66               100  
#> -------------------------------------------------------------------------------
#> Factor Analysis - factorial loadings after rotation-
#> -------------------------------------------------------------------------------
#> # A tibble: 4 x 5
#>   VAR     FA1   FA2 Communality Uniquenesses
#>   <chr> <dbl> <dbl>       <dbl>        <dbl>
#> 1 KW    -0.9   0.04        0.82         0.18
#> 2 NR    -0.92 -0.12        0.87         0.13
#> 3 NKE   -0.7  -0.69        0.96         0.04
#> 4 NKR    0.05 -0.98        0.97         0.03
#> -------------------------------------------------------------------------------
#> Comunalit Mean: 0.9033994 
#> -------------------------------------------------------------------------------
#> Selection differential 
#> -------------------------------------------------------------------------------
#> # A tibble: 4 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 KW    FA1    147.  163.  16.2    11.0  0.659 10.7     7.27 increase   100
#> 2 NR    FA1     15.8  17.4  1.63   10.3  0.736  1.20    7.60 increase   100
#> 3 NKE   FA1    468.  532.  64.0    13.7  0.713 45.6     9.74 increase   100
#> 4 NKR   FA2     30.4  31.2  0.814   2.68 0.452  0.368   1.21 increase   100
#> ------------------------------------------------------------------------------
#> Selected genotypes
#> -------------------------------------------------------------------------------
#> H13 H5
#> -------------------------------------------------------------------------------
plot(mgidi_index)

# }