fpca (version 0.2-1)

prac: An example with M=10 (basis functions) and r=5 (non-zero eigenvalues)

Description

A simulated dataset as an example which corresponds to the "practical" case in the paper

Usage

data(prac)

Format

prac is a list with six components (in the given order):

data

data matrix with three columns: column 1--ID, column 2--measurement, column 3--time.

eigenfunctions

true eigenfunctions: generated from cubic Bsplines with M=10 equally spaced knots.

eigenvalues

true eigenvalues: first--1, second--0.66, third--0.52, fourth--0.44, fifth--0.38, others--zero.

number_of_basis

true number of basis functions: M=10.

dimension

true dimension of the process: r=5.

error_sd

true error standard deviation: 0.25.

Details

mean curve of the process is zero; principal component scores and errors are all i.i.d N(0,1); there are 500 subjects, and each has 2~10 measurements uniformly distributed on [0,1]; in total there are 3018 measurements