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