Computes running covariance between time-series x
and short-time pattern y
.
RunningCov(x, y, circular = FALSE)
x | A numeric vector. |
---|---|
y | A numeric vector, of equal or shorter length than |
circular | Logical; whether running variance is computed assuming
circular nature of |
A numeric vector.
Computes running covariance between time-series x
and short-time pattern y
.
The length of output vector equals the length of x
.
Parameter circular
determines whether x
time-series is assumed to have a circular nature.
Assume \(l_x\) is the length of time-series x
, \(l_y\) is the length of short-time pattern y
.
If circular
equals TRUE
then
first element of the output vector corresponds to sample covariance between x[1:l_y]
and y
,
last element of the output vector corresponds to sample covariance between c(x[l_x], x[1:(l_y - 1)])
and y
.
If circular
equals FALSE
then
first element of the output vector corresponds to sample covariance between x[1:l_y]
and y
,
the \(l_x - W + 1\)-th last element of the output vector corresponds to sample covariance between x[(l_x - l_y + 1):l_x]
,
last W-1
elements of the output vector are filled with NA
.
See runstats.demo(func.name = "RunningCov")
for a detailed presentation.
x <- sin(seq(0, 1, length.out = 1000) * 2 * pi * 6) y <- x[1:100] out1 <- RunningCov(x, y, circular = TRUE) out2 <- RunningCov(x, y, circular = FALSE) plot(out1, type = "l"); points(out2, col = "red")