Predict the means for a genotype-vs-environment trial based on a Genotype plus Genotype-vs-Environment interaction (GGE) model.
Usage
# S3 method for class 'gge'
predict(object, naxis = 2, output = "wide", ...)Arguments
- object
An object of class
gge.- naxis
The the number of principal components to be used in the prediction. Generally, two axis may be used. In this case, the estimated values will be those shown in the biplot.
- output
The type of output. It must be one of the
'long'(default) returning a long-format table with the columns for environment (ENV), genotypes (GEN) and response variable (Y); or'wide'to return a two-way table with genotypes in the row, environments in the columns, filled by the estimated values.- ...
Currently not used.
Value
A two-way table with genotypes in rows and environments in columns if
output = "wide" or a long format (columns ENV, GEN and Y) if
output = "long" with the predicted values by the GGE model.
Details
This function is used to predict the response variable of a two-way table (for examples the yielding of g genotypes in e environments) based on GGE model. This prediction is based on the number of principal components used. For more details see Yan and Kang (2007).
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, GEN, ENV, c(GY, HM))
predict(mod)
#> $GY
#> # A tibble: 14 × 10
#> G1 G10 G2 G3 G4 G5 G6 G7 G8 G9
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2.52 2.03 2.62 2.80 2.42 2.32 2.38 2.70 2.97 2.43
#> 2 2.14 1.65 2.24 2.44 2.09 2.01 2.08 2.40 2.59 2.00
#> 3 1.26 0.913 1.39 1.62 1.40 1.36 1.41 1.71 1.71 0.998
#> 4 1.55 1.03 1.66 1.87 1.58 1.52 1.60 1.95 2.02 1.34
#> 5 3.07 1.97 3.09 3.22 2.64 2.51 2.67 3.16 3.59 3.09
#> 6 1.57 1.77 1.77 2.03 1.90 1.84 1.79 1.92 1.94 1.33
#> 7 3.07 3.30 3.24 3.47 3.19 3.06 2.99 3.08 3.41 3.02
#> 8 4.10 4.01 4.22 4.40 3.94 3.76 3.73 3.89 4.47 4.20
#> 9 3.58 4.01 3.77 4.00 3.71 3.56 3.45 3.46 3.88 3.58
#> 10 4.06 3.34 4.11 4.24 3.64 3.47 3.55 3.90 4.52 4.19
#> 11 2.62 2.42 2.75 2.96 2.63 2.52 2.53 2.76 3.03 2.53
#> 12 1.80 1.96 1.98 2.24 2.08 2.01 1.96 2.10 2.17 1.58
#> 13 2.37 2.60 2.56 2.80 2.60 2.50 2.44 2.54 2.73 2.23
#> 14 2.75 3.59 3.01 3.29 3.18 3.07 2.89 2.80 3.02 2.63
#>
#> $HM
#> # A tibble: 14 × 10
#> G1 G10 G2 G3 G4 G5 G6 G7 G8 G9
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 46.4 47.6 46.0 46.9 47.4 48.7 48.0 47.3 48.4 47.1
#> 2 44.5 42.2 47.3 44.4 41.9 50.6 45.9 40.9 43.9 41.4
#> 3 52.9 56.5 51.7 53.6 53.7 53.4 54.8 54.0 55.0 55.6
#> 4 48.2 50.9 46.8 48.9 50.2 49.2 50.0 50.4 51.0 50.3
#> 5 45.1 47.3 43.4 45.8 48.0 46.2 46.7 48.2 48.4 46.9
#> 6 39.8 40.2 38.5 40.4 43.0 42.2 41.1 43.0 43.1 40.1
#> 7 43.3 43.2 43.4 43.7 44.2 46.6 44.7 43.9 45.2 42.8
#> 8 51.8 54.8 50.9 52.4 52.4 52.8 53.6 52.6 53.8 53.9
#> 9 48.9 51.1 48.2 49.5 49.9 50.5 50.7 50.0 51.0 50.4
#> 10 51.8 53.2 52.6 52.1 50.4 54.6 53.6 50.1 52.4 52.2
#> 11 44.7 45.6 44.2 45.2 46.1 47.2 46.2 46.0 47.0 45.1
#> 12 47.4 49.3 46.7 48.0 48.7 49.3 49.1 48.8 49.7 48.7
#> 13 43.9 44.8 43.3 44.5 45.7 46.4 45.5 45.7 46.4 44.4
#> 14 50.5 52.6 50.3 51.0 50.6 52.4 52.3 50.6 52.1 51.7
#>
# }