pred.nonlinear: Prediction for nonlinear function-on-function regression
In FRegSigCom: Functional Regression using Signal Compression Approach

Description Usage Arguments Value Author(s) See Also Examples

Make predition for response based on new observations of predictor curves from the CV object obtained by cv.nonlinear.

1	pred.nonlinear(fit.cv, X.test, t.y.test=NULL)

`fit.cv`	the CV object obtained by `cv.nonlinear`.
`X.test`	new observations of functional predictors. It is a list of length p, the number of functional predcitors. Each element is the observed matrix from a functional predictor, with rows repsenting observation vectors and columns corresponding to the observation time points.
`t.y.test`	a vector of observation time points where values of predicted response curves are to be calculated. If `t.y.test`=NULL (default), `t.y` in `cv.nonlinear` will be used.

A matrix which contains the predicted values of response curves. The number of rows equal to the sample size of the new data set, and the number of columns is equal to the length of t.y.test or t.y when t.y.test=NULL.

Ruiyan Luo and Xin Qi

cv.nonlinear