***This function has been superseded by `kin.blup`

; please refer to its help page.

1 2 | ```
kinship.BLUP(y, G.train, G.pred=NULL, X=NULL, Z.train=NULL,
K.method="RR", n.profile=10, mixed.method="REML", n.core=1)
``` |

`y` |
Vector ( |

`G.train` |
Matrix ( |

`G.pred` |
Matrix ( |

`X` |
Design matrix ( |

`Z.train` |
0-1 matrix ( |

`K.method` |
"RR" (default) is ridge regression, for which K is the realized additive relationship matrix computed with |

`n.profile` |
For K.method = "GAUSS" or "EXP", the number of points to use in the log-likelihood profile for the scale parameter |

`mixed.method` |
Either "REML" (default) or "ML". |

`n.core` |
Setting n.core > 1 will enable parallel execution of the Gaussian kernel computation (use only at UNIX command line). |

- $g.train
BLUP solution for the training set

- $g.pred
BLUP solution for the prediction set (when G.pred != NULL)

- $beta
ML estimate of fixed effects

For GAUSS or EXP, function also returns

- $profile
log-likelihood profile for the scale parameter

Endelman, J.B. 2011. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250-255.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
#random population of 200 lines with 1000 markers
G <- matrix(rep(0,200*1000),200,1000)
for (i in 1:200) {
G[i,] <- ifelse(runif(1000)<0.5,-1,1)
}
#random phenotypes
g <- as.vector(crossprod(t(G),rnorm(1000)))
h2 <- 0.5
y <- g + rnorm(200,mean=0,sd=sqrt((1-h2)/h2*var(g)))
#split in half for training and prediction
train <- 1:100
pred <- 101:200
ans <- kinship.BLUP(y=y[train],G.train=G[train,],G.pred=G[pred,],K.method="GAUSS")
#correlation accuracy
r.gy <- cor(ans$g.pred,y[pred])
``` |

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