| gge {gge} | R Documentation |
GGE and GGB biplots
Description
Fit a GGE (genotype + genotype * environment) model and display the results.
Usage
gge(x, ...)
## S3 method for class 'data.frame'
gge(x, formula, gen.group = NULL, env.group = NULL, ggb = FALSE, ...)
## S3 method for class 'formula'
gge(formula, data, gen.group = NULL, env.group = NULL, ggb = FALSE, ...)
## S3 method for class 'matrix'
gge(
x,
center = TRUE,
scale = TRUE,
gen.group = NULL,
env.group = NULL,
ggb = FALSE,
comps = c(1, 2),
method = "svd",
...
)
## S3 method for class 'gge'
plot(x, main = substitute(x), ...)
## S3 method for class '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 class '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
x |
A matrix or data.frame. |
... |
Other arguments (e.g. maxiter, gramschmidt) |
formula |
A formula |
gen.group |
genotype group |
env.group |
env group |
ggb |
If TRUE, fit a GGB biplot model. |
data |
Data frame |
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. Either "svd" or "nipals". |
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 genotype labels, default 0.6. Use 0 to omit genotype labels. |
cex.env |
Character expansion for environment labels/symbols. Use lab.env=FALSE to omit labels. |
col.gen |
Color for genotype labels. May be a single color for all genotypes, or a vector of colors for each genotype. |
col.env |
Color for environments. May be a single color for all environments, or a vector of colors for each environment. |
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. |
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.
Value
A list of class gge containing:
x |
The filled-in data |
x.orig |
The original data |
genCoord |
genotype coordinates |
locCoord |
loc coordinates |
blockCoord |
block coordinates |
gen.group |
If not NULL, use this to specify a column of the data.frame to classify genotypes into groups. |
env.group |
If not NULL, use this to specify a column of the data.frame to classify environments into groups. |
ggb |
If TRUE, create a GGB biplot |
genMeans |
genotype means |
mosdat |
mosaic plot data |
R2 |
variation explained by each 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 |
Author(s)
Kevin Wright, Jean-Louis Laffont
Jean-Louis Laffont, Kevin Wright
References
Jean-Louis Laffont, Kevin Wright and Mohamed Hanafi (2013). Genotype + Genotype x Block of Environments (GGB) Biplots. Crop Science, 53, 2332-2341. doi: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. https://three-mode.leidenuniv.nl/document/biplot.pdf
Yan, W. and Kang, M.S. (2003). GGE Biplot Analysis. CRC Press.
Examples
# 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")
library(gge)
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
m2 <- gge(dat2, yield~gen*loc, env.group=locgroup, scale=FALSE)
plot(m2)
biplot(m2, lab.env=TRUE, main="crossa.wheat")
# biplot3d(m2)
}