Description Usage Arguments Details References See Also Examples
Kolmogorov-Zurbenko periodogram and smoothing using DiRienzo-Zurbenko (DZ).
1 2 3 4 5 6 7 8 9 10 11 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## 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)
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