cDPA: cDPA
In PeakSegDP: Dynamic Programming Algorithm for Peak Detection in ChIP-Seq Data
cDPA R Documentation
cDPA
Description
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, ...
Usage
cDPA(count, weight = rep(1,
length(count)), maxSegments)
Arguments
count
Integer vector of count data to segment.
weight
Data weights (normally this is the number of base pairs).
maxSegments
Maximum number of segments to consider.
Author(s)
Toby Dylan Hocking, Guillem Rigaill
Examples
fit <- cDPA(c(0, 10, 11, 1), maxSegments=3)
stopifnot(fit$ends[3,4] == 3)
stopifnot(fit$ends[2,3] == 1)
PeakSegDP documentation built on May 29, 2024, 3:45 a.m.
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| cDPA | R Documentation |
cDPA
Description
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, ...
Usage
cDPA(count, weight = rep(1,
length(count)), maxSegments)Arguments
count |
Integer vector of |
weight |
Data weights (normally this is the number of base pairs). |
maxSegments |
Maximum number of segments to consider. |
Author(s)
Toby Dylan Hocking, Guillem Rigaill
Examples
fit <- cDPA(c(0, 10, 11, 1), maxSegments=3)
stopifnot(fit$ends[3,4] == 3)
stopifnot(fit$ends[2,3] == 1)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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