Description Usage Arguments Details Value Author(s) References Examples
View source: R/FPCA_covsmooth_fpcasc.R
Inputs two data sets and returns the score estimates of covariance function that can be used for 2-sample testing with the 'testAD_fun.R' function.
1 | FPCA_covsmooth_fpcasc(Y.1, Y.2, threshold = 0.99)
|
Y.1 |
Y1 needs to be read in as n x d where n is the number of curves and d is the number of observations along the curve; Each row is a separate curve |
Y.2 |
Y2 needs to be read in as n x d where n is the number of curves and d is the number of observations along the curve; Each row is a separate curve |
threshold |
The proporton of variance explained by the eigenvectors that are used to explain the data and create the smoothed covaraince matrix |
This code smooths the raw covariances first and then pools them. It uses the smoothing methodology from the refund package.
The output is a list of following elements
cov.dat.1 |
smoothed covariance of data set 1 |
cov.dat.2 |
smoothed covariance of data set 2 |
L |
L_U : The number of eigenfunctions that will be used to explain the data (this depends on the threshold) |
eigenfns |
(Phi.U_eigenfn): The eigenfunctions of the smoothed pooled covariance |
eigenvals1 |
(lambda_U1_eigenval) : The estimated eigenvalues from projecting onto the space of pooled eigenfunctions |
eigenvals2 |
(lambda_U2_eigenval) : The estimated eigenvalues from projecting onto the space of pooled eigenfunctions |
sigma.noise2.1 |
estimated error variance of data set 1 |
sigma.noise2.2 |
estimated error variance of data set 2 |
Author of the function: Gina-Maria Pomann
Author of this package: Subhrangshu Nandi (snandi@wisc.edu or nands31@gmail.com)
Pomann, G.M., Staicu, A.M., and Ghosh,S. (2016). A two-sample distribution-free test for functional data with application to a diffusion tensor imaging study of multiple sclerosis. Journal of the Royal Statistical Society: Series C (Applied Statistics).
1 2 3 4 5 6 7 8 9 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.