kzp {kza} | R Documentation |
Kolmogorov-Zurbenko periodogram and smoothing using DiRienzo-Zurbenko (DZ).
kzp(y, m=length(y), k=1) ## S3 method for class 'kzp' smooth(object, log=TRUE, smooth_level=0.05, method = "DZ") ## S3 method for class 'kzp' nonlinearity(x) ## S3 method for class 'kzp' variation(x) ## S3 method for class 'kzp' summary(object, digits=getOption("digits"), top=1, ...) ## S3 method for class 'kzp' plot(x, ...)
y |
The raw data. |
m |
The width of filtering window |
k |
The number of iterations for the KZFT |
object |
Output from kzp function. |
log |
Use logarithm values for smoothing. |
smooth_level |
Percentage of smoothness to apply. |
method |
Method used for smoothing; choices are "DZ" or "NZ". |
digits |
precision of output. |
top |
list top values |
... |
Other parameters. |
x |
periodogram |
The Kolmogorov-Zurbenko Periodogram is an estimate of the spectral density using the Kolmogorov-Zurbenko Fourier Transform (KZFT).
I. G. Zurbenko, 1986: The spectral Analysis of Time Series. North-Holland, 248 pp.
I. G. Zurbenko, P. S. Porter, Construction of high-resolution wavelets, Signal Processing 65: 315-327, 1998.
A. G. DiRienzo, I. G. Zurbenko, Semi-adaptive nonparametric spectral estimation, Journal of Computational and Graphical Statistics 8(1): 41-59, 1998.
R. Neagu, I. G. Zurbenko, Algorithm for adaptively smoothing the log-periodgram, Journal of the Franklin Institute 340: 103-123, 2003.
Wei Yang and Igor Zurbenko, kzft: Kolmogorov-Zurbenko Fourier Transform and Applications, R-Project 2007.
## Not run: t<-1:6000 f1<-0.03 f2<-0.04 noise<-15*rnorm(length(t)) amp=1.5 s<-amp*sin(2*pi*f1*t)+amp*sin(2*pi*f2*t) system.time(a<-kzp(s+noise,m=500,k=3)) b<-smooth.kzp(a, smooth_level=0.01) par(mfrow=c(3,1)) plot(periodogram(s+noise),type='l') plot(a) plot(b) par(mfrow=c(1,1)) # signal/noise signal<-kzft(s+noise,m=500,k=3) print(paste("signal-to-noise ratio = ", round(sqrt(var(2*Re(signal))/var(s+noise)),4) )) summary(a, digits=2, top=2) ## End(Not run)