pred.fof.spike

the CV object obtained by <code><a rd-options="" href="/link/cv.fof.spike?package=FRegSigCom&version=0.3.0" data-mini-rdoc="FRegSigCom::cv.fof.spike">cv.fof.spike</a></code>.

fit.cv 

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.

X.test 

Make predition for functional response from the CV object obtained by <code><a rd-options="" href="/link/cv.fof.spike?package=FRegSigCom&version=0.3.0" data-mini-rdoc="FRegSigCom::cv.fof.spike">cv.fof.spike</a></code>.

Signal compression methods for functional regression. It includes various function-on-function (FOF) regression models such as the linear FOF model with functional response and both scalar and functional predictors for a small number of functional predictors, linear FOF models with a large number of functional predictors, linear FOF model for spiky data, stepwise selection for FOF models with two-way interactions, and nonlinear FOF models. It also includes scalar-on-function regression models with single (SOF) or multivariate (mSOF) scalar response variable, and SOF model for spiky data.

Ruiyan Luo

FRegSigCom

Functional Regression using Signal Compression Approach

pred.fof.spike function

the CV object obtained by <code><a rd-options='' href='cv.fof.spike'>cv.fof.spike</a></code>.

Make predition for functional response from the CV object obtained by <code><a rd-options='' href='cv.fof.spike'>cv.fof.spike</a></code>.

pred.fof.spike: Prediction for linear function-on-function regression on highly sensely observed spiky data

Description

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See Also

Examples