problem.joint.predict: problem joint predict

In tdhock/PeakSegPipeline: Genome-Wide Peak Prediction Using Supervised Learning and Optimal Segmentation

Description

Compute peak predictions for a joint problem.

Usage

problem.joint.predict(jointProblem.dir, 
    verbose = getOption("PeakSegPipeline.verbose", 
        1))

Arguments

jointProblem.dir	project/problems/problemID/jointProblems/jointProbID
verbose

Value

data.table with one row, describing predicted peaks for both group and sample models. Columns are chrom, peakStart, peakEnd, peak.mean.vec, background.mean.vec (for computing normalized peak height relative to background), sample.loss.diff.vec (for ranking peaks in a given sample from most to least likely), sample.peaks, group.peaks, sample.loss.diff, group.loss.diff (for ranking likelihood of peak regions in joint model).

Author(s)

Toby Dylan Hocking

tdhock/PeakSegPipeline documentation built on March 3, 2020, 1:35 a.m.