Nothing
`predict.decorana` <-
function (object, newdata, type = c("response", "sites", "species"),
rank = 4, ...)
{
type <- match.arg(type)
u <- object$rproj[, 1:rank, drop = FALSE]
v <- object$cproj[, 1:rank, drop = FALSE]
orig <- object$origin[1:rank]
u <- sweep(u, 2, orig, "-")
v <- sweep(v, 2, orig, "-")
rs <- object$aidot
cs <- object$adotj
tot <- sum(rs)
rs <- rs/tot
cs <- cs/tot
lam <- object$evals[1:rank]
if (type == "response") {
if (!object$ira && rank > 1)
stop("Prediction is unavailable in detrended CA beyond first axis\n")
Xbar <- 0
if (rank > 0) {
if (!object$ira) {
tmp <- sweep(v, 1, sqrt(cs), "*")
tmp <- rbind(tmp, sweep(u, 1, sqrt(rs), "*"))
rot <- svd(tmp)$v
v <- v %*% rot
u <- u %*% rot
fac <- colSums(sweep(v^2, 1, cs, "*"))
lam <- (fac - 1)/fac
}
Xbar <- u %*% diag(1 - lam, nrow = rank) %*% t(v)
}
out <- (Xbar + 1) * outer(rs, cs) * tot
}
else if (type == "sites") {
if (!missing(newdata)) {
Xbar <- as.matrix(newdata)
if (!is.null(object$v))
Xbar <- sweep(Xbar, 2, object$v, "*")
rs <- rowSums(Xbar)
Xbar <- (Xbar - outer(rs, cs))/sqrt(outer(rs, cs))
v <- sweep(v, 1, sqrt(cs), "*")
u <- sweep(Xbar %*% v, 1, sqrt(rs), "/")
}
out <- u
}
else if (type == "species") {
if (!missing(newdata)) {
if (!object$ira && rank > 1)
stop("type = 'species' not available in detrended CA with 'newdata'")
if (object$iresc)
stop("type = 'species' not available in rescaled DCA with 'newdata'")
Xbar <- as.matrix(newdata)
cs <- colSums(Xbar)
Xbar <- (Xbar - outer(rs, cs))/sqrt(outer(rs, cs))
u <- sweep(u, 1, sqrt(rs), "*")
v <- sweep(t(Xbar) %*% u, 1, sqrt(cs), "/")
v <- sweep(v, 2, lam, "/")
}
out <- v
}
out
}
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