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predict.ecoX <- function(object, newdraw = NULL, subset = NULL,
newdata = NULL, cond = FALSE, verbose = FALSE, ...){
if (is.null(newdraw) && is.null(object$mu))
stop("Posterior draws of mu and Sigma must be supplied")
else if (!is.null(newdraw)){
if (is.null(newdraw$mu) && is.null(newdraw$Sigma))
stop("Posterior draws of both mu and Sigma must be supplied.")
object <- newdraw
}
if (cond) { ## conditional prediction
mu <- coef(object, subset = subset)
n.draws <- nrow(mu)
if (is.null(subset))
subset <- 1:n.draws
Sigma <- object$Sigma[subset,]
if (is.null(newdata))
X <- object$X
else {
mf <- match.call()
if (is.matrix(eval.parent(mf$newdata)))
data <- as.data.frame(data)
tt <- terms(object)
attr(tt, "intercept") <- 0
X <- model.matrix(tt, newdata)
}
n <- nrow(X)
res <- .C("preBaseX", as.double(X), as.double(mu), as.double(t(Sigma)),
as.integer(length(c(X))), as.integer(nrow(mu)),
as.integer(verbose),
pdStore = double(n.draws*n*2), PACKAGE="eco")$pdStore
res <- array(res, c(2, n, n.draws), dimnames=list(c("W1", "W2"),
rownames(X), 1:n.draws))
class(res) <- c("predict.ecoX", "array")
}
else {
res <- predict.eco(object, newdraw = newdraw, subset = subset,
newdata = newdata, verbose = verbose, ...)
colnames(res) <- c("W1", "W2", "X")
}
return(res)
}
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