truck: Data on truck responses to a rough road profile

In Splinets: Functional Data Analysis using Splines and Orthogonal Spline Bases

Data on truck responses to a rough road profile

Description

These are simulated data of truck 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. Details can be found in the reference. The responses provided are at the driver seat. 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, thus data(truck) does not need to be invoked. 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(truck)

Format

numerical 4095 x 101 dataframe: truck

References

Podgorski, 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

tire 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
 
 

Splinets documentation built on March 7, 2023, 8:24 p.m.