pred.msof

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

fit.obj

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.

X.test

Make predition for multivariate scalar response based on new observations of predictor curves from the CV object obtained by <code><a rd-options="" href="/link/cv.msof?package=FRegSigCom&version=0.3.0" data-mini-rdoc="FRegSigCom::cv.msof">cv.msof</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.msof function

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

Make predition for multivariate scalar response based on new observations of predictor curves from the CV object obtained by <code><a rd-options='' href='cv.msof'>cv.msof</a></code>.

pred.msof: Prediction for linear multivariate scalar-on-function regression

Description

Usage

Arguments

Value

See Also

Examples