Genotype plus genotype-by-environment model

Produces genotype plus genotype-by-environment model based on a multi-environment trial dataset containing at least the columns for genotypes, environments and one response variable or a two-way table.

Usage

gge(
  .data,
  env,
  gen,
  resp,
  centering = "environment",
  scaling = "none",
  svp = "environment",
  by = NULL,
  ...
)

Arguments

.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 resp = c(var1, var2, var3). Select helpers are also supported.
centering: The centering method. Must be one of the 'none | 0', for no centering; 'global | 1', for global centered (E+G+GE); 'environment | 2' (default), for environment-centered (G+GE); or 'double | 3', for double centered (GE). A biplot cannot be produced with models produced without centering.
scaling: The scaling method. Must be one of the 'none | 0' (default), for no scaling; or 'sd | 1', where each value is divided by the standard deviation of its corresponding environment (column). This will put all environments roughly he same rang of values.
svp: The method for singular value partitioning. Must be one of the 'genotype | 1', (The singular value is entirely partitioned into the genotype eigenvectors, also called row metric preserving); 'environment | 2', default, (The singular value is entirely partitioned into the environment eigenvectors, also called column metric preserving); or 'symmetrical | 3' (The singular value is symmetrically partitioned into the genotype and the environment eigenvectors This SVP is most often used in AMMI analysis and other biplot analysis, but it is not ideal for visualizing either the relationship among genotypes or that among the environments).
by: One variable (factor) to compute the function by. It is a shortcut to dplyr::group_by().This is especially useful, for example, when the researcher want to produce GGE biplots for each level of a categorical variable. In this case, an object of class gge_grouped is returned.
...: Arguments passed to the function impute_missing_val() for imputation of missing values in case of unbalanced data.

Value

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'.

References

Yan, W., and M.S. Kang. 2003. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press.

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{
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 two-way table with the mean values for
# genotypes and environments

table <- make_mat(data_ge, GEN, ENV, GY) %>% round(2)
table
#>       E1  E10  E11  E12  E13  E14   E2   E3   E4   E5   E6   E7   E8   E9
#> G1  2.37 2.31 1.36 1.34 3.00 1.53 3.04 4.08 3.49 4.17 2.81 1.90 2.27 2.78
#> G10 1.97 1.54 0.90 1.02 1.83 1.86 3.15 4.11 4.27 3.37 2.48 2.24 2.70 3.15
#> G2  2.90 2.30 1.49 1.99 3.03 1.43 3.23 4.57 3.72 3.83 2.54 1.99 2.05 3.36
#> G3  2.89 2.34 1.57 1.76 3.47 2.06 3.61 4.13 4.13 4.13 2.98 2.16 2.85 3.29
#> G4  2.59 2.17 1.37 1.53 2.64 1.86 3.19 3.85 3.30 3.78 2.70 1.98 2.30 3.72
#> G5  2.19 2.14 1.33 1.69 2.57 1.78 3.14 3.74 3.38 3.47 2.43 1.66 2.71 3.30
#> G6  2.30 2.21 1.50 1.39 2.91 1.80 3.29 3.43 3.40 3.57 2.34 1.76 2.54 3.04
#> G7  2.77 2.44 1.36 1.95 3.18 1.94 2.61 4.10 3.02 4.05 2.67 2.55 2.58 3.14
#> G8  2.90 2.57 1.68 2.00 3.52 1.99 3.44 4.11 4.14 4.81 2.91 2.26 2.88 2.83
#> G9  2.33 1.74 1.13 1.41 2.95 1.57 3.09 4.51 3.90 3.93 2.77 1.39 2.49 1.94
make_long(table) %>%
gge(ENV, GEN, Y) %>%
plot()

# }