Description Usage Arguments Details Value References Examples
Calculation of three dimensional functional principal component scores for a series of three dimensional array data.
1 | FPCA_3D_score(X, prop)
|
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. |
Calculate the three dimensional functional principal component scores for a series of three dimensional data.
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.
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.
1 2 | data_in = array(runif(4000,0,1),dim=c(10,10,10,4))
test = FPCA_3D_score(data_in,0.8)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.