Nothing
`predict.timetrack` <- function(object, newdata, ...) {
namNew <- deparse(substitute(newdata))
## Apply a transformation - let tran deal with arg matching
if(!isTRUE(all.equal(transform, "none"))) {
newdata <- tran(newdata, method = object$transform, ...)
}
## merge X and passive
dat <- join(object$X, newdata, type = "left")
X <- dat[[1]]
newdata <- dat[[2]]
## common set of species
tmp <- colSums(X > 0) > 0
X <- X[, tmp]
newdata <- newdata[, tmp]
## fitted values for newdata
pred <- predict(object$ordination, newdata = newdata, type = "wa",
scaling = object$scaling, model = "CCA",
rank = object$rank)
pred2 <- predict(object$ordination, newdata = newdata, type = "wa",
scaling = object$scaling, model = "CA",
rank = object$rank)
pred <- cbind(pred, pred2)
## return object
nams <- object$labels
nams[["passive"]] <- namNew
## update object with the new passive data predictions
object$fitted.values <- pred
object$labels <- nams
object
}
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