gge: GGE and GGB biplots

Description

GGE and GGB biplots

Fit a GGE (genotype + genotype * environment) model and display the results.

Usage

gge(x, ...)
# S3 method for formula
gge(formula, data = NULL, gen.group = NULL,
  env.group = NULL, ...)
# S3 method for matrix
gge(x, center = TRUE, scale = TRUE, gen.group = NULL,
  env.group = NULL, comps = c(1, 2), method = "svd", ...)
# S3 method for gge
plot(x, main = substitute(x), ...)
# S3 method for gge
biplot(x, main = substitute(x), subtitle = "",
  xlab = "auto", ylab = "auto", cex.gen = 0.6, cex.env = 0.5,
  col.gen = "darkgreen", col.env = "orange3", pch.gen = 1,
  lab.env = TRUE, comps = 1:2, flip = "auto", origin = "auto",
  res.vec = TRUE, hull = FALSE, zoom.gen = 1, zoom.env = 1, ...)
biplot3d(x, ...)
# S3 method for gge
biplot3d(x, cex.gen = 0.6, cex.env = 0.5,
  col.gen = "darkgreen", col.env = "orange3", comps = 1:3,
  lab.env = TRUE, res.vec = TRUE, zoom.gen = 1, ...)

Arguments

A matrix or data.frame.

...

Other arguments (e.g. maxiter, gramschmidt)

formula

A formula

data

Data frame

gen.group

genotype group

env.group

env group

center

If TRUE, center values for each environment

scale

If TRUE, scale values for each environment

comps

Principal components to use for the biplot. Default c(1,2).

method

method used to find principal component directions

main

Title, by default the name of the data. Use NULL to suppress the title.

subtitle

Subtitle to put in front of options. Use NULL to suppress the subtitle.

xlab

Label along axis. Default "auto" shows percent of variation explained. Use NULL to suppress.

ylab

Label along axis. Default "auto" shows percent of variation explained. Use NULL to suppress.

cex.gen

Character expansion for genotypes, default 0.6. Use 0 to omit genotypes.

cex.env

Character expansion for environments

col.gen

Color for genotypes

col.env

Color for environments

pch.gen

Plot character for genotypes

lab.env

Label environments if TRUE.

flip

If "auto" then each axis is flipped so that the genotype ordinate is positively correlated with genotype means. Can also be a vector like c(TRUE,FALSE) for manual control.

origin

If "auto", the plotting window is centered on genotypes, otherwise the origin is at the middle of the window.

res.vec

If TRUE, for each group, draw residual vectors from the mean of the locs to the individual locs.

hull

If TRUE, show a which-won-where polygon.

zoom.gen

Zoom factor for manual control of genotype xlim,ylim The default is 1. Values less than 1 may be useful if genotype names are long.

zoom.env

Zoom factor for manual control of environment xlim,ylim. The default is 1. Values less than 1 may be useful if environment names are long. Not used for 3D biplots.

Value

A list of class gge containing:

The filled-in data

x.orig

The original data

genCoord

genotype coordinates

locCoord

loc coordinates

blockCoord

block coordinates

gen.group

If not NULL, this specifies a classification of genotypes into groups.

env.group

If not NULL, this specifies a classification of environments into groups.

genMeans

genotype means

mosdat

mosaic plot data

variation explained by eact PC

center

Data centered?

scale

Data scaled?

method

Method used to calculate principal components.

pctMiss

Percent of x that is missing values

maxPCs

Maximum number of PCs

Details

If there is replication in G*E, then the replications are averaged together before constructing the biplot.

The singular value decomposition of x is used to calculate the principal components for the biplot. Missing values are NOT allowed.

The argument method can be either 'svd' for complete-data or 'nipals' for missing-data.

References

Jean-Louis Laffont, Kevin Wright and Mohamed Hanafi (2013). Genotype + Genotype x Block of Environments (GGB) Biplots. Crop Science, 53, 2332-2341. https://doi.org/10.2135/cropsci2013.03.0178.

Kroonenberg, Pieter M. (1997). Introduction to Biplots for GxE Tables, Research Report 51, Centre for Statistics, The University of Queensland, Brisbane, Australia. http://three-mode.leidenuniv.nl/document/biplot.pdf

Yan, W. and Kang, M.S. (2003). GGE Biplot Analysis. CRC Press.

Examples

Run this code

# NOT RUN {
# Example 1.  Data is a data.frame in 'matrix' format
B <- matrix(c(50, 67, 90, 98, 120,
              55, 71, 93, 102, 129,
              65, 76, 95, 105, 134,
              50, 80, 102, 130, 138,
              60, 82, 97, 135, 151,
              65, 89, 106, 137, 153,
              75, 95, 117, 133, 155), ncol=5, byrow=TRUE)
rownames(B) <- c("G1","G2","G3","G4","G5","G6","G7")
colnames(B) <- c("E1","E2","E3","E4","E5")

m1 = gge(B)
plot(m1)
biplot(m1, main="Example biplot")
# biplot3d(m1)

if(require(agridat)){
  # crossa.wheat biplot

  # Specify env.group as column in data frame
  data(crossa.wheat)
  dat2 <- crossa.wheat
  dat2$eg <- ifelse(is.element(dat2$loc,
                               c("KN","NB","PA","BJ","IL","TC", "JM","PI","AS","ID","SC","SS",
                                 "SJ","MS","MG","MM")), "Grp1", "Grp2")
  m2 <- gge(yield~gen*loc, dat2, env.group=eg, scale=FALSE)
  plot(m2)
  biplot(m2, lab.env=TRUE, main="crossa.wheat")
  # biplot3d(m2)
}

# }

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