jointSeg: Joint segmentation of multivariate signals

Description

Joint segmentation of multivariate signals in two steps:

first-pass segmentation. By default, a fast, greedy approach is used (see method).
pruning of the candidate change points obtained by dynamic programming

Usage

jointSeg(Y, method = "RBS", stat = NULL, dpStat = stat,
  segFUN = NULL, jitter = NULL,
  modelSelectionMethod = ifelse(match(method, c("DynamicProgramming",
  "RBS", "GFLars"), nomatch = 0) > 0, "Lebarbier", "none"),
  modelSelectionOnDP = (match(method, c("DynamicProgramming", "RBS",
  "GFLars"), nomatch = 0) > 0), ..., profile = FALSE, verbose = FALSE)

Arguments

The signal to be segmented (a matrix or a numeric vector)

method

A character value, the type of segmentation method used. May be one of:

"RBS": Recursive Binary Segmentation (the default), see segmentByRBS as described in Gey and Lebarbier (2005)
"GFLars": Group fused LARS as described in Bleakley and Vert (2011).
"DP": Dynamic Programming (Bellman, 1956). For univariate signals the pruned DP of Rigaill et al (2010) is used.
"other": The segmentation method is passed as a function using argument segFUN (see examples in directory otherMethods of the jointseg package).

stat

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

dpStat

A vector containing the names or indices of the columns of Y to be segmented at the second round of segmentation. Defaults to the value of argument stat.

segFUN

The segmentation function to be used when method is set to other. Not used otherwise.

jitter

Uncertainty on breakpoint position after initial segmentation. Defaults to NULL. See Details.

modelSelectionMethod

A character value, the name of the method used to perform model selection.

modelSelectionOnDP

A logical value. If TRUE (the default), model selection is performed on segmentation after dynamic programming; else model selection is performed on initial segmentation. Only applies to methods "DP", "RBS" and "GFLars".

...

Further arguments to be passed to the lower-level segmentation method determined by argument method.

profile

A logical value: trace time and memory usage ?

verbose

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

Details

If modelSelectionOnDP is set to FALSE, then model selection is run on the sets of the form bkp[1:k] for \(1 \leq k \leq length(bkp)\), where bkp is the set of breakpoints identified by the initial segmentation. In particular, this implies that the candidate breakpoints in bkp are sorted by order of appearance and not by position.

If jitter is not NULL, it should be a vector of integer indices. The set of candidate breakpoints passed on to dynamic programming is augmented by all indices distant from an element of jitter from one of the candidates. For example, if jitter==c(-1, 0, 1) and the initial set of breakpoints is c(1,5) then dynamic programming is run on c(1,2,4,5,6).

If the return value of the initial segmentation has an element named dpseg, then initial segmentation results are not pruned by dynamic programming.

References

Bleakley, K., & Vert, J. P. (2011). The group fused lasso for multiple change-point detection. arXiv preprint arXiv:1106.4199.

Vert, J. P., & Bleakley, K. (2010). Fast detection of multiple change-points shared by many signals using group LARS. Advances in Neural Information Processing Systems, 23, 2343-2351.

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/

Rigaill, G. (2010). Pruned dynamic programming for optimal multiple change-point detection. arXiv preprint arXiv:1004.0887.

Examples

Run this code

# NOT RUN {
## A two-dimensional signal
p <- 2
trueK <- 10
len <- 1e4
sim <- randomProfile(len, trueK, 1, p)
Y <- sim$profile
K <- 2*trueK
res <- jointSeg(Y, method="RBS", K=K)
bkp <- res$bestBkp
getTpFp(bkp, sim$bkp, tol=5, relax = -1)   ## true and false positives
plotSeg(Y, list(sim$bkp, res$bestBkp), col=1)

## Now we add some NA:s in one dimension
jj <- sim$bkp[1]
Y[jj-seq(-10,10), p] <- NA
res2 <- jointSeg(Y, method="RBS", K=K, verbose=TRUE)
bkp <- res2$bestBkp
getTpFp(res2$bestBkp, sim$bkp, tol=5, relax = -1)   ## true and false positives
# }