Plot scores of genotypes and environments in different graphical interpretations.
plot_scores(
x,
var = 1,
type = 1,
polygon = FALSE,
title = TRUE,
plot_theme = theme_metan(),
axis.expand = 1.1,
x.lim = NULL,
y.lim = NULL,
x.breaks = waiver(),
y.breaks = waiver(),
x.lab = NULL,
y.lab = NULL,
shape.gen = 21,
shape.env = 23,
size.shape = 2.2,
size.bor.tick = 0.3,
size.tex.lab = 12,
size.tex.pa = 3.5,
size.line = 0.5,
size.segm.line = 0.5,
col.bor.gen = "black",
col.bor.env = "black",
col.line = "black",
col.gen = "blue",
col.env = "forestgreen",
col.alpha.gen = 0.9,
col.alpha.env = 0.9,
col.segm.gen = transparent_color(),
col.segm.env = "forestgreen",
repulsion = 1,
leg.lab = c("Env", "Gen"),
line.type = "solid",
line.alpha = 0.9,
resolution = 300,
file.type = "pdf",
export = FALSE,
file.name = NULL,
width = 8,
height = 7,
color = TRUE,
...
)An object fitted with the functions performs_ammi,
waas or waasb.
The variable to plot. Defaults to var = 1 the first
variable of x.
type of biplot to produce
type = 1 Produces an AMMI1 biplot (Y x PC1) to make inferences
related to stability and productivity.
type = 2 The default, produces an AMMI2 biplot (PC1 x PC2) to make
inferences related to the interaction effects.
type = 3 Valid for objects of class waas or waasb,
produces a biplot showing the GY x WAASB.
type = 4 Produces a plot with the Nominal yield x Environment PC.
Logical argument. If TRUE, a polygon is drawn when
type = 2.
Logical values (Defaults to TRUE) to include
automatically generated titles
The graphical theme of the plot. Default is
plot_theme = theme_metan(). For more details, see
theme.
Multiplication factor to expand the axis limits by to
enable fitting of labels. Default is 1.1.
The range of x and y axes, respectively. Default is
NULL (maximum and minimum values of the data set). New values can be
inserted as x.lim = c(x.min, x.max) or y.lim = c(y.min,
y.max).
The breaks to be plotted in the x and y axes,
respectively. Defaults to waiver() (automatic breaks). New values
can be inserted, for example, as x.breaks = c(0.1, 0.2, 0.3) or
x.breaks = seq(0, 1, by = 0.2)
The label of x and y axes, respectively. Defaults to
NULL, i.e., each plot has a default axis label. New values can be
inserted as x.lab = 'my label'.
The shape for genotypes and environments
indication in the biplot. Default is 21 (circle) for genotypes and
23 (diamond) for environments. Values must be between 21-25:
21 (circle), 22 (square), 23 (diamond), 24 (up
triangle), and 25 (low triangle).
The size of the shape (both for genotypes and
environments). Default is 2.2.
The size of tick of shape. Default is 0.3. The
size of the shape will be size.shape + size.bor.tick
The size of the text for labels (Defaults to 12) and plot area (Defaults to 3.5), respectively.
The size of the line that indicate the means in the biplot.
Default is 0.5.
The size of the segment that start in the origin of the
biplot and end in the scores values. Default is 0.5.
The color of the shape's border for genotypes and environments, respectively.
The color of the line that indicate the means in the biplot.
Default is 'gray'
The shape color for genotypes (Defaults to
'blue') and environments ('forestgreen'). Must be length
one or a vector of colors with the same length of the number of
genotypes/environments.
The alpha value for the color for
genotypes and environments, respectively. Default is 0.9. Values
must be between 0 (full transparency) to 1 (full color).
The color of segment for genotypes (Defaults
to transparent_color()) and environments (Defaults to 'forestgreen'),
respectively. Valid arguments for plots with type = 1 or type
= 2 graphics.
Force of repulsion between overlapping text labels. Defaults to 1.
The labs of legend. Default is Gen and Env.
The type of the line that indicate the means in the biplot.
Default is 'solid'. Other values that can be attributed are:
'blank', no lines in the biplot, 'dashed', 'dotted',
'dotdash', 'longdash', and 'twodash'.
The alpha value that combine the line with the background
to create the appearance of partial or full transparency. Default is
0.4. Values must be between '0' (full transparency) to '1' (full
color).
The resolution of the plot. Parameter valid if
file.type = 'tiff' is used. Default is 300 (300 dpi)
The type of file to be exported. Valid parameter if
export = T|TRUE. Default is 'pdf'. The graphic can also be
exported in *.tiff format by declaring file.type = 'tiff'.
Export (or not) the plot. Default is FALSE.
The name of the file for exportation, default is
NULL, i.e. the files are automatically named.
The width 'inch' of the plot. Default is 8.
The height 'inch' of the plot. Default is 7.
Should type 4 plot have colors? Default to TRUE.
Currently not used.
An object of class gg, ggplot.
Biplots type 1 and 2 are well known in AMMI analysis. In the plot type 3, the scores of both genotypes and environments are plotted considering the response variable and the WAASB, an stability index that considers all significant principal component axis of traditional AMMI models or all principal component axis estimated with BLUP-interaction effects (Olivoto et al. 2019). Plot type 4 may be used to better understand the well known 'which-won-where' pattern, facilitating the recommendation of appropriate genotypes targeted for specific environments, thus allowing the exploitation of narrow adaptations.
Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019. Mean performance and stability in multi-environment trials I: Combining features of AMMI and BLUP techniques. Agron. J. 111:2949-2960. doi:10.2134/agronj2019.03.0220
# NOT RUN {
library(metan)
# AMMI model
ammi_model = performs_ammi(data_ge, ENV, GEN, REP,
resp = c(GY, HM))
# GY x PC1 (variable GY)
plot_scores(ammi_model,
col.env = 'olivedrab',
col.gen = 'orange2',
x.lab = 'My own x label')
# PC1 x PC2 (variable HM)
plot_scores(ammi_model,
var = "HM", # or var = 2
type = 2,
polygon = TRUE)
# PC1 x PC2 (variable HM)
# Draw a convex hull polygon
plot_scores(ammi_model,
var = "HM",
type = 2,
polygon = TRUE)
# WAASB index
waasb_model = waasb(data_ge, ENV, GEN, REP, GY)
# GY x WAASB
plot_scores(waasb_model,
type = 3,
size.tex.pa = 2,
size.tex.lab = 16)
# }
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