The method identifies spikes with respect to a "reference" time-series, and
replaces these spikes with the reference value, or with
to the value of
action; see “Details”.
a vector of (time-series) values, a list of vectors, a data frame,
or an object that inherits from class
indication of the type of reference time series to be used in the detection of spikes; see ‘Details’.
an indication of the limit to differences between
length of running median used with
minimum non-spike value of
maximum non-spike value of
an indication of what to do with spike values, with
optional vector naming columns to be skipped. This is ignored if
Three modes of operation are permitted, depending on the value of
reference="median", the first step is to linearly interpolate
across any gaps (spots where
rule=2. The second step is to pass this through
runmed to get a running median spanning
elements. The result of these two steps is the "reference" time-series.
Then, the standard deviation of the difference between
and the reference is calculated. Any
x values that differ from
the reference by more than
n times this standard deviation are considered
to be spikes. If
replace="reference", the spike values are
replaced with the reference, and the resultant time series is
replace="NA", the spikes are replaced with
and that result is returned.
reference="smooth", the processing is the same as for
"median", except that
smooth is used to calculate the
reference time series.
reference="trim", the reference time series is constructed by
linear interpolation across any regions in which
x>max. (Again, this is done with
rule=2.) In this case, the value of
n is ignored, and the
return value is the same as
x, except that spikes are replaced
with the reference series (if
replace="reference" or with
A new vector in which spikes are replaced as described above.
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n <- 50 x <- 1:n y <- rnorm(n=n) y[n/2] <- 10 # 10 standard deviations plot(x, y, type='l') lines(x, despike(y), col='red') lines(x, despike(y, reference="smooth"), col='darkgreen') lines(x, despike(y, reference="trim", min=-3, max=3), col='blue') legend("topright", lwd=1, col=c("black", "red", "darkgreen", "blue"), legend=c("raw", "median", "smooth", "trim")) # add a spike to a CTD object data(ctd) plot(ctd) T <- ctd[["temperature"]] T <- T + 10 ctd[["temperature"]] <- T CTD <- despike(ctd) plot(CTD)
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