Predictions of every genotype-environment cell

bam_predict() gives the posterior distribution of the expected response of every genotype in every environment, including the cells that were not observed, and of the genotype means over the environments.

Usage

bam_predict(fit, .level = NULL)

Arguments

fit

a result of bayes_ammi() or bayes_stability() (bam_stability() needs bayes_ammi()).

.level

probability of the HPD intervals (default: that of the fit).

Value

An object of class c("bam_predict", "bam_result", "list"):

cells

data.table: Gen, Env, n (plots observed), observed (their mean, NA when missing), mean, sd, lower, upper.

genotypes

data.table: posterior mean, sd and HPD interval of every genotype mean.

Methods: print(), plot() (which = c("cells", "genotypes")), bam_interpret().

Details

For draw \(s\), $$\eta^{(s)}_{ij} = \mu^{(s)} + \tau^{(s)}_i + \delta^{(s)}_j + \sum_{k=1}^{K}\lambda^{(s)}_k \alpha^{(s)}_{ik}\gamma^{(s)}_{jk}$$ (without \(\tau_i\) in the GGE model); the genotype mean over the environments is \(\bar{\eta}_{i\cdot} = E^{-1}\sum_j\eta_{ij}\). A missing cell is predicted from the main effects and from the interaction pattern its genotype and environment show in the other cells; the data augmentation of bayes_ammi() has already propagated its uncertainty into all parameters.

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. Crop Science, 51(4), 1458–1469. doi:10.2135/cropsci2010.06.0343

See also

bam_winners, bam_superiority

Other decisions: bam_ranks(), bam_stability(), bam_superiority(), bam_winners()

Author

Muhammad Yaseen (myaseen208@gmail.com)

Examples

data(Maiz)
d <- Maiz[!(Maiz$entry == 8 & Maiz$site == 8), ]
fit <-
  bayes_ammi(
      .data   = d
    , .y      = y
    , .gen    = entry
    , .env    = site
    , .rep    = rep
    , .nIter  = 1000
    , .k      = 2
    , .burnin = 200
    , .thin   = 1
    , .chains = 1
    , .priors = bam_priors(
          mu_mean     = 0
        , mu_var      = 1e+8
        , tau_mean    = 0
        , tau_var     = 1e+8
        , delta_mean  = 0
        , delta_var   = 1e+8
        , lambda_mean = 0
        , lambda_var  = 1e+8
        , nu          = 1
        , s2          = NULL
        )
    , .level  = 0.95
    , .seed   = 1
    , .align  = TRUE
    , .model  = "AMMI"
    , .het    = FALSE
    , .waic   = TRUE
    )
pr <-
  bam_predict(
      fit    = fit
    , .level = 0.95
    )
pr$cells[n == 0]
#>       Gen    Env     n observed     mean       sd    lower    upper
#>    <fctr> <fctr> <int>    <num>    <num>    <num>    <num>    <num>
#> 1:      8      8     0       NA 4986.848 2063.612 815.0165 7808.311