Model predictions for object of class fRegress.
# S3 method for fRegress
predict(object, newdata=NULL, se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95, ...)Object of class inheriting from lmWinsor
Either NULL or a list matching object\$xfdlist.
If(is.null(newdata)) predictions <- object\$yhatfdobj
If newdata is a list, predictions = the sum of either newdata[i] *
betaestfdlist[i] if object\$yfdobj has class fd or inprod(
newdata[i], betaestfdlist[i]) if class(object\$yfdobj) =
numeric.
a switch indicating if standard errors of predictions are required
type of prediction (response or model term)
Tolerance/confidence level
additional arguments for other methods
The predictions produced by predict.fRegress are either a
vector or a functional parameter (class fdPar) object, matching
the class of object\$y.
If interval is not "none", the predictions will be
multivariate for object\$y and the requested lwr and
upr bounds. If object\$y is a scalar, these predictions
are returned as a matrix; otherwise, they are a multivariate
functional parameter object (class fdPar).
If se.fit is TRUE, predict.fRegress returns a
list with the following components:
vector or matrix or univariate or multivariate functional parameter
object depending on the value of interval and the class of
object\$y.
standard error of predicted means
1. Without newdata, fit <- object\$yhatfdobj.
2. With newdata, if(class(object\$y) == 'numeric'), fit <- sum
over i of inprod(betaestlist[i], newdata[i]). Otherwise, fit <- sum
over i of betaestlist[i] * newdata[i].
3. If(se.fit | (interval != 'none')) compute se.fit, then
return whatever is desired.