| Type: | Package |
| Title: | Three Dimensional Functional Component Analysis |
| Version: | 1.0 |
| Date: | 2018-07-09 |
| Author: | Nan Lin, Momiao Xiong |
| Maintainer: | Nan Lin <edmondlinnan@gmail.com> |
| 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 |
| Repository: | CRAN |
| Date/Publication: | 2018-07-10 15:20:09 UTC |
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>.
Details
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 <edmondlinnan@gmail.com> |
| 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 |
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)
Author(s)
Nan Lin, Momiao Xiong
Maintainer: Nan Lin <edmondlinnan@gmail.com>
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.
See Also
Examples
data_in = array(runif(4000,0,1),dim=c(10,10,10,4))
test = FPCA_3D_score(data_in,0.8)
Three dimensional Fourier Series
Description
Calculate the three dimensional Fourier series coeffiients of the input three dimensional array.
Usage
FFT2FS_3D(A)
Arguments
A |
A three dimensional numerical data array. For example, A can be the data array of an three dimensional image. |
Details
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.
Value
A three dimensional Fourier series coefficients array of the input A data array.
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
test_data = array(runif(1000,0,1),dim = c(10,10,10))
rlt = FFT2FS_3D(test_data)
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)