plot.trackeRfpca: Plot function for functional principal components analysis of...

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

Description

Plot function for functional principal components analysis of distribution and concentration profiles.

Usage

## S3 method for class 'trackeRfpca'
plot(x, harm = NULL, expand = NULL,
  pointplot = TRUE, ...)

Arguments

x	An object of class trackeRfpca as returned by funPCA.
harm	A numerical vector of the harmonics to be plotted. Defaults to all harmonics.
expand	The factor used to generate suitable multiples of the harmonics. If NULL, the effect of +/- 2 standard deviations of each harmonic is plotted.
pointplot	Should the harmonics be plotted with + and - point characters? Otherwise, lines are used.
...	Currently not used.

References

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

See Also

plot.pca.fd

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)
plot(dp.pca, harm = 1, pointplot = FALSE)

## End(Not run)

trackeR documentation built on May 15, 2019, 5:04 p.m.