| Title: | Three Dimensional Functional Component Analysis |
|---|---|
| Description: | Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>. |
| Authors: | Nan Lin, Momiao Xiong |
| Maintainer: | Nan Lin <[email protected]> |
| License: | GPL-2 | GPL-3 |
| Version: | 1.0 |
| Built: | 2025-02-08 03:05:49 UTC |
| Source: | https://github.com/cran/FPCA3D |
Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.
The DESCRIPTION file:
| Package: | FPCA3D |
| Type: | Package |
| Title: | Three Dimensional Functional Component Analysis |
| Version: | 1.0 |
| Date: | 2018-07-09 |
| Author: | Nan Lin, Momiao Xiong |
| Maintainer: | Nan Lin <[email protected]> |
| Description: | Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>. |
| License: | GPL-2 | GPL-3 |
| Depends: | graphics, grDevices, stats, utils |
| NeedsCompilation: | no |
| Packaged: | 2018-07-09 22:49:27 UTC; nan |
| Date/Publication: | 2018-07-10 15:20:09 UTC |
| Repository: | https://edmondlinnan.r-universe.dev |
| RemoteUrl: | https://github.com/cran/FPCA3D |
| RemoteRef: | HEAD |
| RemoteSha: | ec740cf7237ec19b5fda370772e15d5801d39042 |
Index of help topics:
FFT2FS_3D Three dimensional Fourier Series FPCA3D-package Three Dimensional Functional Component Analysis FPCA_3D_score Three Dimensional Functional Component Analysis
data_in = array(runif(4000,0,1),dim=c(10,10,10,4)) test = FPCA_3D_score(data_in,0.8)
Nan Lin, Momiao Xiong
Maintainer: Nan Lin <[email protected]>
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.
data_in = array(runif(4000,0,1),dim=c(10,10,10,4)) test = FPCA_3D_score(data_in,0.8)data_in = array(runif(4000,0,1),dim=c(10,10,10,4)) test = FPCA_3D_score(data_in,0.8)
Calculate the three dimensional Fourier series coeffiients of the input three dimensional array.
FFT2FS_3D(A)FFT2FS_3D(A)
A |
A three dimensional numerical data array. For example, A can be the data array of an three dimensional image. |
Calcualte the three dimensional numerical data array. The input A array can be any three dimensional data array. For image input data, the input should be data array only without any header information.
A three dimensional Fourier series coefficients array of the input A data array.
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.
test_data = array(runif(1000,0,1),dim = c(10,10,10)) rlt = FFT2FS_3D(test_data)test_data = array(runif(1000,0,1),dim = c(10,10,10)) rlt = FFT2FS_3D(test_data)
Calculation of three dimensional functional principal component scores for a series of three dimensional array data.
FPCA_3D_score(X, prop)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.
data_in = array(runif(4000,0,1),dim=c(10,10,10,4)) test = FPCA_3D_score(data_in,0.8)data_in = array(runif(4000,0,1),dim=c(10,10,10,4)) test = FPCA_3D_score(data_in,0.8)