slide: Apply a function in a sliding window along a vector

In jiho/castr: Process CTD casts

Description

Allows to compute a moving average, moving median, or even moving standard deviation, etc. in a generic way.

Usage

slide(x, k, fun, n = 1, ...)

Arguments

x	input numeric vector.
k	order of the window; the window size is 2k+1.
fun	function to apply in the moving window.
n	number of times to pass the function over the data.
...	arguments passed to fun. A usual one is na.rm=TRUE to avoid getting NAs at the extremities of x.

Details

A window of size 2k+1 is centred on element i of x. All elements from index i-k to index i+k are sent to function fun. The returned value is associated with index i in the result. The window is moved to element i+1 and so on.

For such sliding window computation to make sense, the data must be recorded on a regular coordinate (i.e. at regular intervals). Otherwise, data points that are far from each other may end up in the same window.

The extremeties of the input vector are padded with NA to be able to center the sliding window from the first to the last elements. This means that, to avoid getting k missing values at the beginning and at the end of the result, na.rm=TRUE should be passed to fun.

Value

The data passed through fun, n times.

See Also

cweights() to compute weights centered in the middle of the window.

Examples

# create some data and add random noise
xs <- sin(seq(0, 4*pi, length=100))
x <- xs + rnorm(length(xs), sd=0.25)
plot(x)
lines(xs)
# filter the data in various ways
# moving average
mav   <- slide(x, 3, mean, na.rm=TRUE)
# running moving average
rmav  <- slide(x, 3, mean, na.rm=TRUE, n=4)
# weighted moving average
wmav  <- slide(x, 3, weighted.mean, na.rm=TRUE, w=cweights(3))
# weighted running moving average
wrmav <- slide(x, 3, weighted.mean, na.rm=TRUE, w=cweights(3), n=4)
# moving median
mmed  <- slide(x, 3, median, na.rm=TRUE)
lines(mav, col="red")
lines(rmav, col="red", lty="dashed")
lines(wmav, col="orange")
lines(wrmav, col="orange", lty="dashed")
lines(mmed, col="blue")
# inspect variability around filtered data
plot(slide(x-rmav, 7, sd))
plot(slide(x-mmed, 7, mad))

jiho/castr documentation built on April 5, 2020, 2:12 p.m.