pcaPACE: Estimate the functional principal components

In fda: Functional Data Analysis

Estimate the functional principal components

Description

Carries out a functional PCA with regularization from the estimate of the covariance surface

Usage

  pcaPACE(covestimate, nharm, harmfdPar, cross)

Arguments

covestimate	a list with the two named entries "cov.estimate" and "meanfd"
nharm	the number of harmonics or principal components to compute.
harmfdPar	a functional parameter object that defines the harmonic or principal component functions to be estimated.
cross	a logical value: if TRUE, take into account the cross covariance for estimating the eigen functions.

Value

an object of class "pca.fd" with these named entries:

harmonics	a functional data object for the harmonics or eigenfunctions
values	the complete set of eigenvalues
scores	NULL. Use "scoresPACE" for estimating the pca scores
varprop	a vector giving the proportion of variance explained by each eigenfunction
meanfd	a functional data object giving the mean function

References

Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

Yao, F., Mueller, H.G., Wang, J.L. (2005), Functional data analysis for sparse longitudinal data, J. American Statistical Association, 100, 577-590.

fda documentation built on Sept. 30, 2024, 9:19 a.m.