| pcaPACE {fda} | R Documentation |
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.