pred.sigcom: Prediction for linear function-on-function regression using signal compression

Description

Make predition for functional response from the CV object obtained by cv.sigcom.

Usage

pred.sigcom(fit.obj, X.test, t.y.test=NULL, Z.test = NULL)

Arguments

fit.obj

the CV object obtained by cv.sigcom.

X.test

new observations for the 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.

Z.test

new observations for the scalar predictors. It is a matrix with rows representing observation vectors and columns respresenting scalar variables. Default is NULL, indicating no scalar predictors.

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.sigcom will be used.

Value

A matrix containing the predicted response for the new observations. The number of rows is 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.

References

Ruiyan Luo and Xin Qi, (2017) Function-on-Function Linear Regression by Signal Compression, Journal of the American Statistical Association. 112(518), 690-705. http://www.tandfonline.com/doi/abs/10.1080/01621459.2016.1164053

Examples

Run this code

# NOT RUN {
#See the examples in cv.sigcom().
# }