## ----Rle-rollmean, eval=FALSE------------------------------------------------- # rollmeanRle <- function (x, k) # { # n <- length(x) # cumsum(c(Rle(sum(window(x, 1, k))), window(x, k + 1, n) - window(x, 1, n - k))) / k # } ## ----Rle-rollvar, eval=FALSE-------------------------------------------------- # rollvarRle <- function(x, k) # { # n <- length(x) # means <- rollmeanRle(x, k) # nextMean <- window(means, 2, n - k + 1) # cumsum(c(Rle(sum((window(x, 1, k) - means[1])^2)), # k * diff(means)^2 - (window(x, 1, n - k) - nextMean)^2 + (window(x, k + 1, n) - nextMean)^2)) / (k - 1) # } ## ----Rle-rollcov, eval=FALSE-------------------------------------------------- # rollcovRle <- function(x, y, k) # { # n <- length(x) # meanX <- rollmeanRle(x, k) # meanY <- rollmeanRle(y, k) # nextMeanX <- window(meanX, 2, n - k + 1) # nextMeanY <- window(meanY, 2, n - k + 1) # cumsum(c(Rle(sum((window(x, 1, k) - meanX[1]) * (window(y, 1, k) - meanY[1]))), # k * diff(meanX) * diff(meanY) - (window(x, 1, n - k) - nextMeanX) * (window(y, 1, n - k) - nextMeanY) + (window(x, k + 1, n) - nextMeanX) * (window(y, k + 1, n) - nextMeanY))) / (k - 1) # } ## ----Rle-rollsd, eval=FALSE--------------------------------------------------- # rollsdRle <- function(x, k) # { # sqrt(rollvarRle(x, k)) # } ## ----Rle-rollcor,eval=FALSE--------------------------------------------------- # rollcorRle <- function(x, y, k) # { # rollcovRle(x, y, k) / (rollsdRle(x, k) * rollsdRle(y, k)) # }