Description Usage Arguments Value Author(s) References Examples
Produces genotype plus genotypebyenvironment model based on a multienvironment trial dataset containing at least the columns for genotypes, environments and one response variable or a twoway table.
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.data 
The dataset containing the columns related to Environments, Genotypes and the response variable(s). 
env 
The name of the column that contains the levels of the environments. 
gen 
The name of the column that contains the levels of the genotypes. 
resp 
The response variable(s). To analyze multiple variables in a
single procedure a vector of variables may be used. For example 
centering 
The centering method. Must be one of the 
scaling 
The scaling method. Must be one of the 
svp 
The method for singular value partitioning. Must be one of the 
by 
One variable (factor) to compute the function by. It is a shortcut
to 
... 
Arguments passed to the function

The function returns a list of class gge
containing the following objects
coordgen The coordinates for genotypes for all components.
coordenv The coordinates for environments for all components.
eigenvalues The vector of eigenvalues.
totalvar The overall variance.
labelgen The name of the genotypes.
labelenv The names of the environments.
labelaxes The axes labels.
ge_mat The data used to produce the model (scaled and centered).
centering The centering method.
scaling The scaling method.
svp The singular value partitioning method.
d The factor used to generate in which the ranges of genotypes and environments are comparable when singular value partitioning is set to 'genotype' or 'environment'.
grand_mean The grand mean of the trial.
mean_gen A vector with the means of the genotypes.
mean_env A vector with the means of the environments.
scale_var The scaling vector when the scaling method is 'sd'
.
Tiago Olivoto tiagoolivoto@gmail.com
Yan, W., and M.S. Kang. 2003. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  library(metan)
mod < gge(data_ge, ENV, GEN, GY)
plot(mod)
# GGE model for all numeric variables
mod2 < gge(data_ge2, ENV, GEN, resp = everything())
plot(mod2, var = "ED")
# If we have a twoway table with the mean values for
# genotypes and environments
table < make_mat(data_ge, GEN, ENV, GY) %>% round(2)
table
make_long(table) %>%
gge(ENV, GEN, Y) %>%
plot()

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