Skip to contents

Plot WAASBY values for genotype ranking

Source: R/plot_waasby.R
plot_waasby.Rd

[Stable]

Plot heat maps with genotype ranking in two ways.

Usage

plot_waasby(
  x,
  var = 1,
  export = F,
  file.type = "pdf",
  file.name = NULL,
  plot_theme = theme_metan(),
  width = 6,
  height = 6,
  size.shape = 3.5,
  size.tex.lab = 12,
  col.shape = c("blue", "red"),
  x.lab = "WAASBY",
  y.lab = "Genotypes",
  x.breaks = waiver(),
  resolution = 300,
  ...
)

Arguments

x

The WAASBY object

var

The variable to plot. Defaults to var = 1 the first variable of x.

export

Export (or not) the plot. Default is T.

file.type

The type of file to be exported. Default is pdf, Graphic can also be exported in *.tiff format by declaring file.type = "tiff".

file.name

The name of the file for exportation, default is NULL, i.e. the files are automatically named.

plot_theme

The graphical theme of the plot. Default is plot_theme = theme_metan(). For more details, see ggplot2::theme().

width

The width "inch" of the plot. Default is 8.

height

The height "inch" of the plot. Default is 7.

size.shape

The size of the shape in the plot. Default is 3.5.

size.tex.lab

The size of the text in axis text and labels.

col.shape

A vector of length 2 that contains the color of shapes for genotypes above and below of the mean, respectively. Default is c("blue", "red").

x.lab

The label of the x axis in the plot. Default is "WAASBY".

y.lab

The label of the y axis in the plot. Default is "Genotypes".

x.breaks

The breaks to be plotted in the x-axis. Default is authomatic breaks. New arguments can be inserted as x.breaks = c(breaks)

resolution

The resolution of the plot. Parameter valid if file.type = "tiff" is used. Default is 300 (300 dpi)

...

Currently not used.

Value

An object of class gg, ggplot.

See also

plot_scores()

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{
library(metan)
library(ggplot2)
waasby <- waasb(data_ge,
                resp = GY,
                gen = GEN,
                env = ENV,
                rep = REP)
#> Evaluating trait GY |============================================| 100% 00:00:02 

#> 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
#>  COMPLETE       NA
#>       GEN 1.11e-05
#>   GEN:ENV 2.15e-11
#> ---------------------------------------------------------------------------
#> All variables with significant (p < 0.05) genotype-vs-environment interaction
waasby2 <- waas(data_ge,
                resp = GY,
                gen = GEN,
                env = ENV,
                rep = REP)
#> variable GY 
#> ---------------------------------------------------------------------------
#> AMMI analysis table
#> ---------------------------------------------------------------------------
#>     Source  Df  Sum Sq Mean Sq F value   Pr(>F) Proportion Accumulated
#>        ENV  13 279.574 21.5057   62.33 0.00e+00         NA          NA
#>   REP(ENV)  28   9.662  0.3451    3.57 3.59e-08         NA          NA
#>        GEN   9  12.995  1.4439   14.93 2.19e-19         NA          NA
#>    GEN:ENV 117  31.220  0.2668    2.76 1.01e-11         NA          NA
#>        PC1  21  10.749  0.5119    5.29 0.00e+00       34.4        34.4
#>        PC2  19   9.924  0.5223    5.40 0.00e+00       31.8        66.2
#>        PC3  17   4.039  0.2376    2.46 1.40e-03       12.9        79.2
#>        PC4  15   3.074  0.2049    2.12 9.60e-03        9.8        89.0
#>        PC5  13   1.446  0.1113    1.15 3.18e-01        4.6        93.6
#>        PC6  11   0.932  0.0848    0.88 5.61e-01        3.0        96.6
#>        PC7   9   0.567  0.0630    0.65 7.53e-01        1.8        98.4
#>        PC8   7   0.362  0.0518    0.54 8.04e-01        1.2        99.6
#>        PC9   5   0.126  0.0252    0.26 9.34e-01        0.4       100.0
#>  Residuals 252  24.367  0.0967      NA       NA         NA          NA
#>      Total 536 389.036  0.7258      NA       NA         NA          NA
#> ---------------------------------------------------------------------------
#> 
#> All variables with significant (p < 0.05) genotype-vs-environment interaction
#> Done!
plot_waasby(waasby)

plot_waasby(waasby2) +
            theme_gray() +
            theme(legend.position = "bottom",
                  legend.background = element_blank(),
                  legend.title = element_blank(),
                  legend.direction = "horizontal")

# }