FPCA_3D_score: Three Dimensional Functional Component Analysis

In FPCA3D: Three Dimensional Functional Component Analysis

Description

Calculation of three dimensional functional principal component scores for a series of three dimensional array data.

Usage

FPCA_3D_score(X, prop)

Arguments

X	The input data array. X is a four dimensional data array. The first three dimensional data represents the three dimensional data array for each observation. The fourth dimention represents the observations.
prop	The prespecified proportion of variance the calcuatled functional principal component scores can explain in the functional domain.

Details

Calculate the three dimensional functional principal component scores for a series of three dimensional data.

Value

A two dimensional score matrix. The row of the score matrix represents each individual and the column of the score matrix represent each component score.

References

Lin N, Jiang J, Guo S, Xiong M. Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis. PLOS ONE. 2015;10(7):e0132945.

Examples

data_in = array(runif(4000,0,1),dim=c(10,10,10,4))
test = FPCA_3D_score(data_in,0.8)

FPCA3D documentation built on May 2, 2019, 4:17 a.m.