Formula: Y = (1_IJ (x) I_nT) mu + X beta + Z b + eps (eq. 5.5a), the
multi-trait model of eq. (5.5) extended with a fixed-effects term X beta.
Usage
Msm028(Y, Z, G, Sigma_T, R_T, X = NULL)
Arguments
- Y
Lines-by-traits response matrix (J x n_T).
- Z
Random-effects incidence matrix over lines.
- G
Genomic relationship matrix.
- Sigma_T
Genetic covariance between traits.
- R_T
Residual covariance between traits.
- X
Extra fixed-effects design in the stacked ordering.
Value
List with estimate, mu, beta, b, method.
References
Montesinos Lopez, Montesinos Lopez & Crossa (2022),
Multivariate Statistical Machine Learning Methods for Genomic Prediction,
Springer, eq. (5.5a) p.153. DOI 10.1007/978-3-030-89010-0.
Examples
set.seed(12)
J <- 6; nT <- 2
G <- crossprod(matrix(rnorm(J * J), J)) / J
Y <- matrix(rnorm(J * nT, 5), J, nT)
Msm028(Y, Z = diag(J), G = G, Sigma_T = diag(nT), R_T = diag(nT),
X = matrix(rnorm(J * nT), J * nT, 1))$beta
#> [1] 4.7251095 4.5446783 0.3744103
