cDPA | R Documentation |
A constrained dynamic programming algorithm (cDPA) can be used to compute the best segmentation with respect to the Poisson likelihood, subject to a constraint on the number of segments, and the changes which must alternate: up, down, up, down, ...
cDPA(count, weight = rep(1,
length(count)), maxSegments)
count |
Integer vector of |
weight |
Data weights (normally this is the number of base pairs). |
maxSegments |
Maximum number of segments to consider. |
Toby Dylan Hocking, Guillem Rigaill
fit <- cDPA(c(0, 10, 11, 1), maxSegments=3)
stopifnot(fit$ends[3,4] == 3)
stopifnot(fit$ends[2,3] == 1)
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