doGFLars {jointseg}

Group fused Lars segmentation

Description

High-level function for multivariate fused Lars (GFLars) segmentation

Usage

doGFLars(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 'segmentByGFLars'

verbose

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

Details

This function is a wrapper around the lower-level segmentation function segmentByGFLars. 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.

For the specific case of DNA copy number data segmentation, see the dedicated wrapper PSSeg.

The default weights \sqrt{n/(i*(n-i))} are calibrated as suggested by Bleakley and Vert (2011). Using this calibration, the first breakpoint maximizes the likelihood ratio test (LRT) statistic.

Value

An object of the same structure as the output of segmentByGFLars

Note

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

Author(s)

Morgane Pierre-Jean and Pierre Neuvial

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.

Examples

p <- 2
trueK <- 10
sim <- randomProfile(1e4, trueK, 1, p)
Y <- sim$profile
K <- 2*trueK
res <- doGFLars(Y, K)
print(res$bkp)
print(sim$bkp)
plotSeg(Y, res$bkp)

## a toy example with missing values
sim <- randomProfile(1e2, 1, 0.1, 2)
Y <- sim$profile
Y[3:50, 2] <- NA

res <- doGFLars(Y, 10, 2, verbose=TRUE)
print(res$bkp)
print(sim$bkp)
plotSeg(Y, res$bkp)