Skip to contents

Genotype effects

Source: R/g_eff.R
g_eff.Rd

g_eff() computes the genotype main effects, the deviations of the genotype means from the grand mean.

Usage

g_eff(.data, .y, .gen, .env)

# Default S3 method
g_eff(.data, .y, .gen, .env)

Arguments

.data

a data frame with one row per plot.

.y, .gen, .env

the columns of the response, genotypes and environments: bare names, strings or vectors.

Value

A \(G \times 1\) matrix with column GEffs (row names: genotypes).

Details

$$\hat{\tau}_i = \bar{y}_{i\cdot} - \bar{y}, \qquad \bar{y}_{i\cdot} = \frac{1}{E}\sum_j \bar{y}_{ij},$$ the least-squares estimates of the AMMI model (equal to the deviations of the plot means when the data are balanced).

References

Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J. M., Viele, K., Liu, G. and Cornelius, P. L. (2011). Bayesian estimation of the additive main effects and multiplicative interaction model. \emphCrop Science, \strong51(4), 1458–1469. \doi10.2135/cropsci2010.06.0343

See also

e_eff, ge_eff

Author

Muhammad Yaseen (myaseen208@gmail.com)

Examples

data(Maiz)
g_eff(
    .data = Maiz
  , .y    = y
  , .gen  = entry
  , .env  = site
  )
#>        GEffs
#> 1 -241.10903
#> 2 -254.87903
#> 3  -35.22403
#> 4  359.71097
#> 5  388.73597
#> 6  472.03347
#> 7 -185.82153
#> 8 -574.94278
#> 9   71.49597