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AMMI Biplots with ggplot2

Source: R/rAMMIPlot.R
rAMMIPlot.Rd

Generates a high-quality biplot (PC1 vs PC2) for classical or robust AMMI models using the ggplot2 framework.

Usage

rAMMIPlot(
  model_res,
  colGen = "gray47",
  colEnv = "darkred",
  sizeGen = 6,
  sizeEnv = 6,
  titles = TRUE,
  footnote = TRUE,
  axis_expand = 1.2,
  limits = TRUE,
  axes = TRUE,
  axislabels = TRUE
)

Arguments

model_res

an object of class rAMMI generated by rAMMIModel.

colGen

color for genotype labels. Defaults to `"gray47"`.

colEnv

color for environment labels and vectors. Defaults to `"darkred"`.

sizeGen

text size for genotype labels. Defaults to 6.

sizeEnv

text size for environment labels. Defaults to 6.

titles

logical. If `TRUE` (default), a title indicating the model type is added to the plot.

footnote

logical. If `TRUE` (default), a caption with the percentage of explained GxE variation is added at the bottom.

axis_expand

numeric value to expand the axis limits. Useful to prevent labels from being cut off. Defaults to 1.2.

limits

logical. If `TRUE` (default), uses coord_fixed() to maintain a 1:1 aspect ratio between axes.

axes

logical. If `TRUE` (default), draws dashed horizontal and vertical lines passing through the origin (0,0).

axislabels

logical. If `TRUE` (default), includes axis titles with the percentage of variance explained by each principal component.

Value

A ggplot2 object. This allows further customization using standard ggplot layers (e.g., + theme_bw()).

Details

the biplot is constructed using a scaling factor of 0.5 (symmetric scaling), which allows representing both genotypes and environments in the same algebraic space. Genotypes are displayed as points (text), and environments are represented as vectors from the origin.

Examples

library(agridat)
data(yan.winterwheat)

# Classical AMMI
mod_ammi <- rAMMIModel(yan.winterwheat, genotype = "gen", 
                       environment = "env", response = "yield", type = "AMMI")

rAMMIPlot(mod_ammi,sizeGen=4,sizeEnv=4)


data(plrv)
mod_ammi_rep <- rAMMIModel(plrv, genotype="Genotype", environment="Locality", 
                        response="Yield", rep="Rep", type="AMMI")
rAMMIPlot(mod_ammi_rep,sizeGen=4,sizeEnv=4)