Genomic prediction by kinship-BLUP (deprecated)
***This function has been superseded by kin.blup; please refer to its help page.
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 (n.obs \times 1) of observations. Missing values (NA) are omitted. |
G.train |
Matrix (n.train \times m) of unphased genotypes for the training population: n.train lines with m bi-allelic markers. Genotypes should be coded as {-1,0,1}; fractional (imputed) and missing (NA) alleles are allowed. |
G.pred |
Matrix (n.pred \times m) of unphased genotypes for the prediction population: n.pred lines with m bi-allelic markers. Genotypes should be coded as {-1,0,1}; fractional (imputed) and missing (NA) alleles are allowed. |
X |
Design matrix (n.obs \times p) of fixed effects. If not passed, a vector of 1's is used to model the intercept. |
Z.train |
0-1 matrix (n.obs \times n.train) relating observations to lines in the training set. If not passed the identity matrix is used. |
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 θ. |
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). |
BLUP solution for the training set
BLUP solution for the prediction set (when G.pred != NULL)
ML estimate of fixed effects
For GAUSS or EXP, function also returns
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.
#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])Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.