trackeR (version 1.5.2)

funPCA: Functional principal components analysis of distribution or concentration profiles.

Description

Functional principal components analysis of distribution or concentration profiles.

Generic function for functional principal components analysis

Usage

# S3 method for distrProfile
funPCA(object, what, nharm = 4, ...)
# S3 method for conProfile
funPCA(object, what, nharm = 4, ...)
funPCA(object, ...)

Value

An object of class trackeRfpca.

Arguments

object

The object to which a functional principal components analysis is applied.

what

The variable for which the profiles should be analysed.

nharm

The number of principal components estimated.

...

Arguments to be passed to methods.

Details

The ... argument is passed on to pca.fd.

References

Ramsay JO, Silverman BW (2005). Functional Data Analysis. Springer-Verlag New York.

Examples

if (FALSE) {
data('runs', package = 'trackeR')
dp <- distributionProfile(runs, what = 'speed')
dp.pca <- funPCA(dp, what = 'speed', nharm = 4)
## 1st harmonic  captures vast majority of the variation
plot(dp.pca, harm = 1)
## time spent above speed = 0 is the characteristic distinguishing the profiles
sumRuns <- summary(runs)
plot(sumRuns$durationMoving, dp.pca$scores[,1])
}