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Environment effects

Source: R/e_eff.R
e_eff.Rd

e_eff() computes the environment main effects, the deviations of the environment means from the grand mean.

Usage

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

# Default S3 method
e_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

An \(E \times 1\) matrix with column EEffs (row names: environments).

Details

$$\hat{\delta}_j = \bar{y}_{\cdot j} - \bar{y}, \qquad \bar{y}_{\cdot j} = \frac{1}{G}\sum_i \bar{y}_{ij}.$$

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

g_eff, ge_eff

Author

Muhammad Yaseen (myaseen208@gmail.com)

Examples

data(Maiz)
e_eff(
    .data = Maiz
  , .y    = y
  , .gen  = entry
  , .env  = site
  )
#>          EEffs
#> 1  -1245.60722
#> 2   -648.69333
#> 3    246.78167
#> 4    371.85944
#> 5     97.09278
#> 6   1447.16778
#> 7  -1628.79611
#> 8   -830.45444
#> 9    112.50667
#> 10 -1921.71278
#> 11   449.05389
#> 12  2658.36222
#> 13  1473.96222
#> 14  1194.15944
#> 15   190.89000
#> 16   548.02611
#> 17    18.55111
#> 18  -306.59056
#> 19 -2206.03500
#> 20   -20.52389