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Genotype-by-environment means

Source: R/ge_mean.R
ge_mean.Rd

ge_mean() computes the table of genotype-by-environment cell means and the grand mean, the data of the AMMI model.

Usage

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

# Default S3 method
ge_mean(.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 list (class c("bam_ge_mean", "bam_result", "list")) with ge_means, the \(G \times E\) matrix of means (row and column names: genotypes and environments), and grand_mean.

Details

With \(y_{ijl}\) the response of genotype \(i\) in replicate \(l\) of environment \(j\) and \(n_{ij}\) the number of replicates of the cell, $$\bar{y}_{ij} = \frac{1}{n_{ij}}\sum_l y_{ijl}, \qquad \bar{y} = \frac{1}{GE}\sum_{ij}\bar{y}_{ij}.$$ Genotypes and environments are in the order of their factor levels (sorted values when the columns are not factors).

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

ge_var, ge_eff, ge_ammi

Author

Muhammad Yaseen (myaseen208@gmail.com)

Examples

data(Maiz)
ge_mean(
    .data = Maiz
  , .y    = y
  , .gen  = entry
  , .env  = site
  )
#> <bam_ge_mean> Genotype-by-environment means
#> 
#> $ge_means
#>      1    2    3    4    5    6    7    8    9   10   11   12   13   14   15
#> 1 3622 3728 5554 4566 4380 6437 2832 6011 4647 3100 4433 6873 6721 5849 4601
#> 2 3426 3919 4937 4963 5201 6036 2515 5278 4714 2972 4349 7571 5627 5932 4126
#> 3 3446 4082 5117 5136 4178 6459 3529 4731 5448 2785 4526 7727 6294 5886 4537
#> 4 3720 4539 4542 6030 5672 6678 2998 2516 4864 2843 7117 8385 7332 6439 6331
#> 5 3165 4079 6173 5831 5414 6882 3556 2732 5588 2688 5995 8106 7174 6359 6328
#> 6 4116 4878 5205 5980 5591 6916 3949 2983 5603 3024 6150 7637 7262 6380 5961
#> 7 3354 4767 5389 4342 4277 6745 3537 4206 4318 2889 5052 7444 5544 5820 4346
#> 8 4529 3393 5248 4442 4476 4986 3088 4484 4001 3353 3713 5816 4117 5522 4321
#> 9 3136 4500 3780 5781 5407 5610 3061 3309 5553 2774 6430 8091 6920 6282 4889
#>     16   17   18   19   20
#> 1 5010 4415 3344 1632 4587
#> 2 5196 4211 4415 2282 4396
#> 3 5455 4749 4295 3059 5018
#> 4 6351 5161 5618 2233 4988
#> 5 6070 5454 4498 3073 5776
#> 6 5730 5807 5333 3011 5088
#> 7 5013 3862 5276 3211 4056
#> 8 4551 5243 2940 2634 4806
#> 9 5278 4989 5244 2735 4822
#> 
#> $grand_mean
#> [1] 4858