Description Usage Arguments Details Value Author(s) References See Also Examples
S3 predict
method for objects of class gpMod
. A genomic prediction model is used
to predict the genetic performance for e.g. unphenotyped individuals using an object of class
gpMod
estimated by a training set.
1 2 |
object |
object of class |
newdata |
for |
... |
not used |
For models, model="RR"
and "BL"
, the prediction for the unphenotyped individuals
is given by
ghat=muhat + W mhat
with the estimates taken
from the gpMod
object. For the prediction using model="BLUP"
, the full
relationship matrix including individuals of the training set and the prediction set must be
specified in the gpMod
. This model is used to predict the unphenotyped individuals of
the prediction set by solving the corresponding mixed model equations using the variance
components of the fit in gpMod
.
a named vector with the predicted genetic values for all individuals in newdata
.
Valentin Wimmer
Henderson C (1977) Best linear unbiased prediction of breeding values not in the model for records. Journal of Dairy Science 60:783-787
Henderson CR (1984). Applications of linear models in animal breeding. University of Guelph.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | # Example from Henderson (1977)
dat <- data.frame(y=c(132,147,156,172),time=c(1,2,1,2),row.names=c("ID1","ID2","ID3","ID4"))
ped <- create.pedigree(ID=c("ID6","ID5","ID1","ID2","ID3","ID4"),
Par1=c(0,0,"ID5","ID5","ID1","ID6"),
Par2=c(0,0,0,0,"ID6","ID2"))
gp <- create.gpData(pheno=dat,pedigree=ped)
A <- kin(gp,ret="add")
# assuming h2=sigma2u/(sigma2u+sigma2)=0.5
# no REML fit possible due to the limited number of observations
y <- c(132,147,156,172)
names(y) <- paste("ID", 1:4, sep="")
mod1 <- list(fit=list(sigma=c(1,1),X = matrix(1,ncol=1,nrow=4)),kin=A,model="BLUP",y=y,m=NULL)
# matrix A included all individuals (including those which should be predicted)
class(mod1) <- "gpMod"
predict(mod1,c("ID5","ID6"))
# prediction 'by hand'
X <- matrix(1,ncol=1,nrow=4)
Z <- diag(6)[-c(1,2),]
AI <- solve(A)
RI <- diag(4)
res <- MME(X,Z,AI,RI,y)
res$u[1:2]
## Not run:
# prediction of genetic performance of the last 50 individuals in the maize data set
data(maize)
maizeC <- codeGeno(maize)
U <- kin(maizeC,ret="realized")
maizeC2 <- discard.individuals(maizeC,rownames(maizeC$pheno)[1201:1250])
modU <- gpMod(maizeC2,model="BLUP",kin=U)
predict(modU,rownames(maizeC$pheno)[1201:1250])
## End(Not run)
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