Description Usage Arguments Details Value References See Also Examples
PRG  generate a Precision Recall Gain object to show accuracy of states
1 
states 
A GRangesList produced by 
comparison 
A GRanges object that indicates TRUE or FALSE for a
specific genomic intervals. The names of the the 
state.select 
A character vector that will filter the states object if
the states object has a "state" column. For example if the output of

comparison.select 
a list of numeric vectors that can be used to select
a subset of 
This code incorprates PrecisionRecallGain analysis from
https://github.com/meeliskull/prg/tree/master/R_package.
From the
abstract of the referenced paper: "PrecisionRecall analysis abounds in
applications of binary classification where true negatives do not add value
and hence should not affect assessment of the classifier's performance.
Perhaps inspired by the many advantages of receiver operating
characteristic (ROC) curves and the area under such curves for
accuracybased performance assessment, many researchers have taken to
report PrecisionRecall (PR) curves and associated areas as performance
metric. We demonstrate in this paper that this practice is fraught with
difficulties, mainly because of incoherent scale assumptions – e.g., the
area under a PR curve takes the arithmetic mean of precision values whereas
the F_beta score applies the harmonic mean. We show how to fix this by plotting
PR curves in a different coordinate system, and demonstrate that the new
PrecisionRecallGain curves inherit all key advantages of ROC curves. In
particular, the area under PrecisionRecallGain curves conveys an expected
F1 score on a harmonic scale, and the convex hull of a
PrecisionRecallGain curve allows us to calibrate the classifier's scores
so as to determine, for each operating point on the convex hull, the
interval of beta values for which the point optimises F_beta. We demonstrate
experimentally that the area under traditional PR curves can easily favour
models with lower expected F1 score than others, and so the use of
PrecisionRecallGain curves will result in better model selection."
a list that contains the precision gain and recall gain, the convex hull, and the area under the precision recall gain curve (auprg).
Flach, P., & Kull, M. (2015). PrecisionRecallGain Curves: PR Analysis Done Right. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28 (pp. 838846). Curran Associates, Inc.
http://www.cs.bris.ac.uk/~flach/PRGcurves/
1 2 3 4 5  load(system.file("extdata", "heart.states.rda", package = "StatePaintR"))
load(system.file("extdata", "vista.heart.enhancers.rda", package = "StatePaintR"))
PRG(states = heart.states,
comparison = heart.enhancers,
state.select = c("EAR", "EARC", "AR", "ARC"))

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