rAMMI: AMMI biplots with 'ggplot2'

In geneticae: Statistical Tools for the Analysis of Multi Environment Agronomic Trials

AMMI biplots with ggplot2

Description

Produces classical or robust AMMI biplot as an object of class 'ggplot', with options for customization.

Usage

rAMMI(
  Data,
  genotype = "gen",
  environment = "env",
  response = "Y",
  rep = NULL,
  Ncomp = 2,
  type = "AMMI",
  colGen = "gray47",
  colEnv = "darkred",
  sizeGen = 4,
  sizeEnv = 4,
  titles = TRUE,
  footnote = TRUE,
  axis_expand = 1.2,
  limits = TRUE,
  axes = TRUE,
  axislabels = TRUE
)

Arguments

Data	a dataframe with genotypes, environments, repetitions (if any) and the phenotypic trait of interest. Other variables that will not be used in the analysis can be included.
genotype	column name containing genotypes.
environment	column name containing environments.
response	column name containing the phenotypic trait of interest.
rep	column name containing replications. If this argument is 'NULL' (default), replications are not considered for the analysis.
Ncomp	number of principal components that will be used in the analysis.
type	method for fitting the AMMI model: '"AMMI"', '"rAMMI"', '"hAMMI"', '"gAMMI"', '"lAMMI"' or '"ppAMMI"' (see References). Defaults to '"AMMI"'.
colGen	genotype attributes colour. Defaults to "gray".
colEnv	environment attributes colour. Defaults to "darkred".
sizeGen	genotype labels text size. Defaults to 4.
sizeEnv	environment labels text size. Defaults to 4.
titles	logical, if this argument is 'TRUE' a plot title is generated. Defaults to 'TRUE'.
footnote	logical, if this argument is 'TRUE' a footnote is generated. Defaults to 'TRUE'.
axis_expand	multiplication factor to expand the axis limits by to enable fitting of labels. Defaults to 1.2.
limits	logical. If 'TRUE' axes are automatically rescaled. Defaults to 'TRUE'.
axes	logical, if this argument is 'TRUE' axes passing through the origin are drawn. Defaults to 'TRUE'.
axislabels	logical, if this argument is 'TRUE' labels axes are included. Defaults to 'TRUE'.

Details

To overcome the problem of data contamination with outlying observations, Rodrigues, Monteiro and Lourenco (2015) propose a robust AMMI model based on the M-Huber estimator and in robusts SVD/PCA procedures. Several SVD/PC methods were considered, briefly described below, thus conveying a total of five robust AMMI candidate models:

• R-AMMI: uses the L1 norm instead of the more usual least squares L2 norm, to compute a robust approximation to the SVD of a rectangular matrix.

• H-AMMI: Combines projection-pursuit and robust covariance estimation techniques to compute the robust loadings. It is most adequate for high-dimensional data.

• G-AMMI: Uses projection-pursuit to compute PCA estimators. The optimization is done via the grid search algorithm in the plane instead of the p-dimensional space.

• L-AMMI: The idea behind this approach is to perform classical PCA on the data but projected onto a unit sphere. When the data are elliptically distributed the estimates of the eigenvectors are consistent

• PP-AMMI: Uses projection-pursuit calculating the robust eigenvalues and eigenvectors without going through robust covariance estimation. The principal components can be sequentially computed and thus this method is very appealing when few genotypes are evaluated under a wide range of environmental and/or experimental conditions.

Value

A biplot of class ggplot

References

Rodrigues P.C., Monteiro A., Lourenco V.M. (2015). A robust AMMI model for the analysis of genotype-by-environment data. Bioinformatics 32, 58–66.

Examples

library(geneticae)
# Data without replication
library(agridat)
data(yan.winterwheat)
BIP_AMMI <- rAMMI(yan.winterwheat, genotype = "gen", environment = "env",
                  response = "yield", type = "AMMI")
BIP_AMMI

# Data with replication
data(plrv)
BIP_AMMI2 <- rAMMI(plrv, genotype = "Genotype", environment = "Locality",
                   response="Yield", rep = "Rep", type = "AMMI")
BIP_AMMI2

geneticae documentation built on July 20, 2022, 5:06 p.m.