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FRegSigCom (version 0.3.0)

pred.nonlinear: Prediction for nonlinear function-on-function regression

Description

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

Usage

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

Arguments

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.

Value

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.

See Also

cv.nonlinear

Examples

Run this code
# NOT RUN {
#See the examples in cv.nonlinear().
# }

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