Nothing
predict.mvdareg <- function(object, newdata, ncomp = object$ncomp, na.action = na.pass, ...)
{
#Adapted from 'pls' package
if (missing(newdata) || is.null(newdata)) {
new.X <- model.matrix(object)
} else if (is.matrix(newdata)) {
if (ncol(newdata) != length(object$Xmeans))
stop("'newdata' does not have the correct number of columns")
new.X <- newdata
} else {
Terms <- delete.response(terms(object))
options(contrasts = c("contr.niets", "contr.poly"))
m <- model.frame(Terms, newdata, na.action = na.action)
if (!is.null(cl <- attr(Terms, "dataClasses")))
.checkMFClasses(cl, m)
new.X <- no.intercept(model.matrix(Terms, m))
}
nobs <- dim(new.X)[1]
if (!is.null(object$scale))
new.X <- new.X/rep(object$scale, each = nobs)
if (missing(newdata)) {
object$iPreds[, ncomp]
} else {
B <- object$coefficients[, ncomp]
new.X <- new.X - rep(object$Xmeans, each = nobs)
pred <- new.X %*% B + object$Ymean
if (missing(newdata) && !is.null(object$na.action))
pred <- napredict(object$na.action, pred)
pred
}
}
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