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

In trackeR: Infrastructure for Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices

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 class 'distrProfile'
funPCA(object, what, nharm = 4, ...)

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

funPCA(object, ...)

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.

Value

An object of class trackeRfpca.

References

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

Examples

## Not run: 
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])

## End(Not run)

trackeR documentation built on May 29, 2024, 5:04 a.m.