getPCA.test: Get the functional principal component scores for a given...

In robflreg: Robust Functional Linear Regression

Get the functional principal component scores for a given test sample

Description

This function is used to compute the functional principal component scores of a test sample based on outputs obtained from getPCA.

Usage

getPCA.test(object, data)

Arguments

object	An output object of getPCA.
data	An n \times p-dimensional data matrix for functional data X(s) (test sample), where n denotes the sample size and p denotes the number of grid points for X(s).

Details

See getPCA for details.

Value

A matrix of principal component scores for the functional data.

Author(s)

Ufuk Beyaztas and Han Lin Shang

Examples

sim.data <- generate.ff.data(n.pred = 5, n.curve = 200, n.gp = 101)
Y <- sim.data$Y
Y.train <- Y[1:100,]
Y.test <- Y[101:200,]
gpY = seq(0, 1, length.out = 101) # grid points
rob.fpca <- getPCA(data = Y.train, nbasis = 20, ncomp = 4,
gp = gpY, emodel = "robust")
rob.fpca.test <- getPCA.test(object = rob.fpca, data = Y.test)

robflreg documentation built on May 29, 2024, 3:55 a.m.