if (FALSE) {
library('fda')
data('runs', package = 'trackeR')
dp <- distributionProfile(runs, what = 'speed')
dpFun <- profile2fd(dp, what = 'speed',
fdnames = list('speed', 'sessions', 'time above threshold'))
dp.pca <- pca.fd(dpFun, nharm = 4)
## 1st harmonic captures vast majority of the variation
dp.pca$varprop
## time spent above speed = 0 is the characteristic distinguishing the profiles
plot(dp.pca, harm = 1)
sumRuns <- summary(runs)
plot(sumRuns$durationMoving, dp.pca$scores[,1])
}
Run the code above in your browser using DataLab