These are simulated data of tire responses to a rough road at the high-transient
event. The simulations have been made based on the fit of the so-called Slepian model
to a non-Gaussian rough road profile. Further details can be found in the reference. The
responses provided are measured at the wheel and thus describing the tire response.
There are 100 functional measurments, kept column-wise in the matrix.
Additionally, the time instants of the measurements are given as the first column in the matrix.
Since the package uses the so-called "lazy load", the matrix
is directly available without an explicit load of the data.
This means that data(tire) does not need to be invoked.
The data were saved using compress='xz' option, which requires 3.5 or higher version of R.
The data are uploaded as a dataframe, thus as.matrix(tire) is needed if the matrix form is required.
Usage
data(tire)References
Podg\(\mbox{\'o}\)rski, K, Rychlik, I. and Wallin, J. (2015) Slepian noise approach for gaussian and Laplace moving average processes. Extremes, 18(4):665–695, <doi:10.1007/s10687-015-0227-z>.
See also
truck for a related dataset;
Examples
#-----------------------------------------------------#
#----------- Plotting the trucktire data -------------#
#-----------------------------------------------------#
#Activating data:
data(tire)
data(truck)
matplot(tire[,1],tire[,2:11],type='l',lty=1) #ploting the first 10 tire responses
matplot(truck[,1],truck[,2:11],type='l',lty=1) #ploting the first 10 truck responses
#Projecting truck data into splinet bases
knots1=seq(0,50, by=2)
Subtruck= truck[2048:3080,] # selecting the truck data that in the interval[0,50]
TruckProj=project(as.matrix(Subtruck),knots1)
MeanTruck=matrix(colMeans(TruckProj$coeff),ncol=dim(TruckProj$coeff)[2])
MeanTruckSp=lincomb(TruckProj$basis,MeanTruck)
plot(MeanTruckSp) #the mean spline of the projections
plot(TruckProj$sp,sID=1:10) #the first ten projections of the functional data
Sigma=cov(TruckProj$coeff)
Spect=eigen(Sigma,symmetric = TRUE)
plot(Spect$values, type ='l',col='blue', lwd=4 ) #the eigenvalues
EigenTruckSp=lincomb(TruckProj$basis,t(Spect$vec))
plot(EigenTruckSp,sID=1:5) #the first five largest eigenfunctions
