Functional principal component analysis

Description

Performs a functional principal component analysis.

Usage

fpca(data=data, dimBase=4, fpcCtrl=NULL)

Arguments

Details

A functional principal component analysis is executed. Therefore, a smoothed mean curve and a smoothed covariance matrix are built and upon these, eigenvalues and eigenvectors are calculated. If average=TRUE, data is stored in a sparse matrix of dimension (nr_curves x nr_union_time_points) and matrix operations speed up the calculation. However, if the curves do not have many common time points, average should be set to FALSE. If the smoothing parameter (either for the mean or for the covariance) specified in fpcCtrl led to NA-values in the smoothing process, it is automatically increased by 10% of its actual value and a warning is printed. This process is repeated until smoothing is possible.

Value

Author(s)

Christina Yassouridis

See Also

fpcCtrl

Examples

##Generate a regular dataset
set.seed(2804)
ds <- sampleFuncy(obsNr=40, timeNr=15, reg=TRUE)

##Execute functional principal component analysis
res <- fpca(Data(ds))
matplot(res$base,type='l')

##Generate an irregular dataset
set.seed(2804)
ds <- sampleFuncy(obsNr=30, k=5, timeNrMin=5, timeNrMax=7, reg=FALSE)

##Execute functional principal component analysis
res <- fpca(Data(ds))
matplot(res$base, type='l', main="First 4 basis functions.")

Results