jointseg (version 1.0.2)

doRBS: Run RBS segmentation

Description

High-level function for multivariate recursive binary (RBS) segmentation

Usage

doRBS(Y, K, stat = NULL, ..., verbose = FALSE)

Arguments

Y

A n*p signal to be segmented

K

The number of change points to find

stat

A vector containing the names or indices of the columns of Y to be segmented

…

Further arguments to be passed to 'segmentByRBS'

verbose

A logical value: should extra information be output ? Defaults to FALSE.

Value

An object of the same structure as the output of segmentByRBS

Details

This function is a wrapper around the lower-level segmentation function segmentByRBS. It can be run on p-dimensional, piecewise-constant data in order to defined a set of candidate change points. It is recommended to prune this list of candidates using dynamic programming (pruneByDP), combined with a selection of the best number of change points. The jointSeg function provides a convenient wrapper for performing segmentation, pruning and model selection.

References

Gey, S., & Lebarbier, E. (2008). Using CART to Detect Multiple Change Points in the Mean for Large Sample. http://hal.archives-ouvertes.fr/hal-00327146/

See Also

PSSeg, pruneByDP

Examples

# NOT RUN {
p <- 2
trueK <- 10
len <- 5e4
sim <- randomProfile(len, trueK, 1, p)
Y <- sim$profile
K <- 2*trueK
res <- doRBS(Y, K)
getTpFp(res$bkp, sim$bkp, tol=10, relax = -1)   ## true and false positives

cols <- rep(2, K)
cols[1:trueK] <- 3
par(mfrow=c(p,1))
for (ii in 1:p) {
    plot(Y[, ii], pch=19, cex=0.2)
    abline(v=res$bkp[1:trueK], col= cols)
    abline(v=sim$bkp, col=8, lty=2)
}

## NA:s in one dimension at a true breakpoint
jj <- sim$bkp[1]
Y[jj-seq(-10, 10), p] <- NA
res2 <- doRBS(Y, K)
getTpFp(res2$bkp, sim$bkp, tol=10, relax = -1)   ## true and false positives

## NA:s in both dimensions at a true breakpoint
Y[jj-seq(-10, 10), ] <- NA
res3 <- doRBS(Y, K)
getTpFp(res3$bkp, sim$bkp, tol=10, relax = -1)   ## true and false positives

# }