kz {kza} | R Documentation |
Kolmogorov-Zurbenko low-pass linear filter.
kz(x, m, k = 3)
x |
The raw data that will be smoothed. The data can have as many as 3 dimensions. KZ will also handle a time series. |
m |
Window size for the filter. This can be up to 3 dimensions, but not more than the dimensionality of the input data x. |
k |
Number of iterations. |
KZ is an iterated moving average. The filter can be used with missing values. One iteration is equivalent to a simple moving average. Three iterations is an approximately Gaussian shaped filter.
Zurbenko, I. G., 1986: The spectral Analysis of Time Series. North-Holland, 248 pp.
## 2 dimensions set.seed(2) a <- matrix(rep(0,100*100),nrow=100) a[35:70,35:70]<-1 a <- a + matrix(rnorm(100*100,0,1),nrow=100) z<-kz(a,m=c(20,5),k=3) x <- seq(1,100) y <- x op <- par(bg = "white") c="lightblue" m="Unsmoothed" persp(x, y, a, zlab="a", ticktype="detailed", theta = 60, phi = 45, col = c, main=m) m="KZ(a,m=c(20,5),k=3)" persp(x, y, z, zlab="z", ticktype="detailed", theta = 60, phi = 45, col = c, main=m) #example t <- seq(0,20,length=20*365) set.seed(6); e <- rnorm(n = length(t), sd = 2.0) y <- sin(3*pi*t) + e z <- kz(y,30) par(mfrow=c(2,1)) plot(y,ylim=c(-5,5),type="l",main="y = sin(3*pi*t) + noise") plot(z,ylim=c(-5,5), type="l",main="KZ filter") lines(sin(3*pi*t), col="blue") par(mfrow=c(1,1))