Nothing
predict.ivbma <- function(a,X,W)
{
n <- dim(X)[1]
odens <- dim(a$rho)[1]
V <- as.matrix(cbind(X,W))
Y.hat <- matrix(0,100 * odens,1)
for(i in 1:odens)
{
for(j in 1:n)
{
v <- a$Sigma[1,1,i]
eps <- rnorm(100,0,sqrt(v))
rho <- a$rho[i,]
Y.mean <- V[j,] %*% rho
l <- ( (i - 1) * 100 + 1):( (i - 1) * 100 + 100)
Y.hat[l,j] <- Y.mean + eps
}
}
return(Y.hat)
}
ivbma.score <- function(b, y)
{
y.bar <- mean(b)
SE <- (y - y.bar)^2
AE <- abs(y - median(b))
VAR <- var(b)
CRPS <- NULL
nn <- length(b)
f <- mean( abs(y - b))
f <- f - 1/2 * mean( abs(b[1:(nn - 1)] - b[2:nn]))
CRPS <- f
r <- c(SE,AE,VAR,CRPS)
names(r) <- c("SE","AE","VAR","CRPS")
return(r)
}
ivbma.cv.study <- function(d,...)
{
n <- length(d$Y)
R <- matrix(0,n,4)
for(j in 1:n)
{
a <- ivbma(d$Y[-j],d$X[-j,],d$Z[-j,],d$W[-j,],...)
b <- predict(a,d$X[j,],d$W[j,])
R[j,] <- ivbma.score(b,d$Y[j])
}
return(R)
}
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