getPCA.test {robflreg}R Documentation

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×pn \times p-dimensional data matrix for functional data X(s)X(s) (test sample), where nn denotes the sample size and pp denotes the number of grid points for X(s)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)

[Package robflreg version 1.2 Index]