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

1 |

`Y` |
A |

`K` |
The number of change points to find |

`stat` |
A vector containing the names or indices of the columns of |

`...` |
Further arguments to be passed to 'segmentByGFLars' |

`verbose` |
A |

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 *√{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.

An object of the same structure as the output of `segmentByGFLars`

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

Morgane Pierre-Jean and Pierre Neuvial

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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)
``` |

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