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