pruneByDP: Exact segmentation of a multivariate signal using dynamic...
In jointseg: Joint segmentation of multivariate (copy number) signals

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Exact segmentation of a multivariate signal using dynamic programming.

1 2	pruneByDP(Y, candCP = 1:(nrow(Y) - 1), K = length(candCP), allowNA = TRUE, verbose = FALSE)

`Y`	A `n*p` signal to be segmented
`candCP`	A vector of candidate change point positions (defaults to 1:(n-1))
`K`	The maximum number of change points to find
`allowNA`	A boolean value specifying whether missing values should be allowed or not.
`verbose`	A `logical` value: should extra information be output ? Defaults to `FALSE`.

This function retrieves the maximum likelihood solution of the gaussian homoscedastic change model into segments, for K \in {1 … length(candCP)}. The dynamic programming algorithm used is quadratic in time. For signals containing more than 1000 points, we recommend using a first pass segmentation (see segmentByRBS) to find a smaller number of candidates, and to run pruneByDP on these candidates only, as initially suggested by Gey and Lebarbier (2008). These two steps can be performed using jointSeg for generic multivariate signals, and using PSSeg for copy number signals from SNP array data.

if allowNA, the calulation of the cost of removing candidate breakpoints between i and j for i<j tolerates missing values. Method !allowNA is maintained in order to check consistency with the original dynamic programming in the absence of NA:s.

A list with elements:

`bkpList`	A list of vectors of change point positions for the best model with k change points, for k=1, 2, ... K
`rse`	A vector of K+1 residual squared errors
`V`	V[i,j] is the best RSE for segmenting intervals 1 to j

This implementation is derived from the MATLAB code by Vert and Bleakley: http://cbio.ensmp.fr/GFLseg.

Morgane Pierre-Jean and Pierre Neuvial

Bellman, R. (1961). On the approximation of curves by line segments using dynamic programming. Communications of the ACM, 4(6), 284.

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/

jointSeg, PSSeg

p <- 2
trueK <- 10
sim <- randomProfile(1e4, trueK, 1, p)
Y <- sim$profile
K <- 2*trueK
res <- segmentByRBS(Y, K)
resP <- pruneByDP(Y, res$bkp)

##   Note that all of the above can be dmethod=="other"one directly using 'jointSeg'
resJ <- jointSeg(sim$profile, method="RBS", K=K)
stopifnot(identical(resP$bkpList, resJ$dpBkp))

## check consistency when no NA
resP2 <- pruneByDP(Y, res$bkp, allowNA=FALSE)
max(abs(resP$rse-resP2$rse))

plotSeg(Y, list(resP$bkp[[trueK]], sim$bkp), col=1)