problem.train: problem train
In tdhock/PeakSegPipeline: Genome-Wide Peak Prediction Using Supervised Learning and Optimal Segmentation
Description
Usage
Arguments
Author(s)
Description
Train a penalty function that predicts the number of peaks for
each separate problem. Run this step after computing target
intervals for each labeled separate problem (problem.target), and
before peak prediction (problem.predict).
Usage
1
2
3
problem.train(data.dir.str,
verbose = getOption("PeakSegPipeline.verbose",
1))
Arguments
data.dir.str
verbose
Author(s)
Toby Dylan Hocking
tdhock/PeakSegPipeline documentation built on March 3, 2020, 1:35 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Author(s)
Description
Train a penalty function that predicts the number of peaks for
each separate problem. Run this step after computing target
intervals for each labeled separate problem (problem.target), and
before peak prediction (problem.predict).
Usage
1 2 3 | problem.train(data.dir.str,
verbose = getOption("PeakSegPipeline.verbose",
1))
|
Arguments
data.dir.str |
|
verbose |
Author(s)
Toby Dylan Hocking
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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