problem.models: problem models
In tdhock/PeakSegPipeline: Genome-Wide Peak Prediction Using Supervised Learning and Optimal Segmentation
Description
Usage
Arguments
Value
Author(s)
Description
Read models from filesystem. Reads both CSV and RDS files, then
saves them all in RDS, then deletes all CSV files.
Usage
1
2
3
problem.models(problem.dir,
verbose = getOption("PeakSegPipeline.verbose",
1))
Arguments
problem.dir
character string path: samples/groupID/sampleID/problems/probID
verbose
print messages?
Value
Data table with all info from model files. One row per model, with
list columns segments.dt and errors.dt with elements that are data
tables representing the segmentation model and label errors.
Author(s)
Toby Dylan Hocking
tdhock/PeakSegPipeline documentation built on March 3, 2020, 1:35 a.m.
R Package Documentation
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Description Usage Arguments Value Author(s)
Description
Read models from filesystem. Reads both CSV and RDS files, then saves them all in RDS, then deletes all CSV files.
Usage
1 2 3 | problem.models(problem.dir,
verbose = getOption("PeakSegPipeline.verbose",
1))
|
Arguments
problem.dir |
character string path: samples/groupID/sampleID/problems/probID |
verbose |
print messages? |
Value
Data table with all info from model files. One row per model, with list columns segments.dt and errors.dt with elements that are data tables representing the segmentation model and label errors.
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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