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Sample variance of a Kolmogorov-Zurbenko adaptive filter. You want a sigma of at least 3.
kzsv(object) # S3 method for kzsv summary(object, digits=getOption("digits"), …) # S3 method for kzsv plot(x, …)
The resultant object from kza function.
The results of the kza function.
Precision of output.
Other parameters.
# NOT RUN { x <- c(rep(0,4000),rep(0.5,2000),rep(0,4000)) noise <- rnorm(n = 10000, sd = 1.0) # normally-distributed random variates v <- x + noise a<-kza(v, m=1000, k=3) sv<-kzsv(a) # }
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