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###################
# calculates the acf using the GK approach
# input
# x: time series (without NA) as vector
# lag.max: the maximal lag of interest
# scalefn: which variance estimator function to use (Qn, mad, scaleTau2, reweightedQn, ...)
# ...: passed to function scalefn
# output: autocorrelation function
###################
acfrob.GK <- function(x, lag.max, scalefn = Qn, ...) {
n <- length(x)
lags <- 1:lag.max
# calculating the acf:
acfvalues <- numeric(length(lags))
for (i in lags) {
acfvalues[i] <- corGK(x[1:(n-i)], x[(i+1):n], scalefn=scalefn, ...)
}
are <- NA #factor is NA unless the scale function matches one of the following alternatives:
if(identical(scalefn, Qn)) are <- sqrt(1/0.8227)
if(identical(scalefn, scaleTau2)) are <- sqrt(1/0.8)
if(identical(scalefn, mad)) are <- sqrt(1/0.3674)
if(identical(scalefn, sd)) are <- 1
res <- list(
acfvalues = acfvalues,
are = are
)
return(res)
}
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