Nothing
###################
# calculates the acf based on median correlation
# input
# x: time series without NAs as vector
# lag.max: maximal lag of interest
# output: calculated acf
###################
acfrob.median <- function(x, lag.max, biascorr = TRUE) {
n <- length(x)
lags <- 1:lag.max
# calculating the acf (biased!):
x_centered <- x - median(x)
mediancor <- function(x, y) median(x*y)/median(x^2) #median correlation
acfvalues_biased <- numeric(length(lags))
for (i in lags) {
acfvalues_biased[i] <- mediancor(x_centered[1:(n-i)], x_centered[(i+1):n])
}
if(!biascorr) return(acfvalues_biased)
# transformation for unbiasedness:
load(system.file("extdata", "acfbiascorr_median", package = "robts")) # loading the simulated expected values for the median correlation
acfvalues <- sapply(acfvalues_biased, linearinterpol, a=get("expectations"), b=get("values"))
res <- list(
acfvalues = acfvalues,
are = NA
)
return(res)
}
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