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Last data update: 2014.03.03

R: Receiver Operating Characteristic and Precision-Recall Curves

roc	R Documentation

Receiver Operating Characteristic and Precision-Recall Curves

Description

Computes the Receiver Operating Characteristic (ROC) or precision-recall curves from contingencies tables (confusion matrices) and their associated probabilities, and optionally plots them.

Usage

roc( contabs, plotit = TRUE)
prc( contabs, plotit = TRUE, ymax = max(y))

Arguments

`contabs`	A data frame representing a contingency tables (confusion matrices) for a binary classification experiment. The column names should include `TP` (for true positive), `FN` (for false negative), `FP` (for false positive), `TN` (for true negative); these can be given in any order.
`plotit`	A logical variable indicating whether or not the ROC curve should be plotted.
`ymax`	Upper vertical axis value for plotting precision-recall curves with `prc`. The default is `max(y)`, (the maximum value for precision derived from the contingency table). `ymax` should be no larger than 1.

Details

The estimated area may be inaccurate when the sector covered by the contingency tables is small, and should be used with caution in such cases.

Value

A vector with the following named components:

`area`	The area under the extended curve (covered by the black solid and dotted lines).
`sector`	The estimated area under the sector (below the curve and within the red lines) covered by the contingency table.
`width`	The width of the sector (within the red vertical lines) covered by the contingency table.

As a side-effect, the ROC (roc) or precision-recall curve (prc) is plotted if plotit = TRUE. The red vertical lines highlight the sector covered by the contingency table. The black lines approximate the curve. They are dotted beyond the endpoints of the sector, where there is no information from the contingency table. The diagonal blue line in the ROC curve indicates the line between (0,0) and (1,1).

References

J. Davis and M. Goadrich, The relationship between Precision-Recall and ROC curves, manuscript, Department of Computer Science, University of Wisconsin.

Examples

data(dream4)

network <- 1

nTimePoints <- length(unique(dream4ts10[[network]]$time))

edges1ts10 <- networkBMA( data = dream4ts10[[network]][,-(1:2)],
                      nTimePoints = nTimePoints, prior.prob = 0.1)

# check for self loops in estimated network
selfN <- any(as.character(edges1ts10[,1]) == as.character(edges1ts10[,2]))
selfN

reference <- dream4gold10[[network]]

# check for self loops in reference (there are none)
selfR <- any(as.character(reference[,1]) == as.character(reference[,2]))
selfR

# restrict reference to edges; first two columns (gene) only
reference <- reference[reference[,3] == 1,1:2]

contingencyTables <- contabs(network = edges1ts10, reference = reference,
                             size = 100)

roc(contingencyTables)
prc(contingencyTables)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(networkBMA)
Loading required package: BMA
Loading required package: survival
Loading required package: leaps
Loading required package: robustbase

Attaching package: 'robustbase'

The following object is masked from 'package:survival':

    heart

Loading required package: inline
Loading required package: rrcov
Scalable Robust Estimators with High Breakdown Point (version 1.3-11)

Loading required package: Rcpp

Attaching package: 'Rcpp'

The following object is masked from 'package:inline':

    registerPlugin

Loading required package: RcppArmadillo
Loading required package: RcppEigen

Attaching package: 'RcppEigen'

The following objects are masked from 'package:RcppArmadillo':

    fastLm, fastLmPure

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/networkBMA/roc.Rd_%03d_medium.png", width=480, height=480)
> ### Name: roc
> ### Title: Receiver Operating Characteristic and Precision-Recall Curves
> ### Aliases: roc prc
> 
> ### ** Examples
> 
> data(dream4)
> 
> network <- 1
> 
> nTimePoints <- length(unique(dream4ts10[[network]]$time))
> 
> edges1ts10 <- networkBMA( data = dream4ts10[[network]][,-(1:2)],
+                       nTimePoints = nTimePoints, prior.prob = 0.1)
> 
> # check for self loops in estimated network
> selfN <- any(as.character(edges1ts10[,1]) == as.character(edges1ts10[,2]))
> selfN
[1] TRUE
> 
> reference <- dream4gold10[[network]]
> 
> # check for self loops in reference (there are none)
> selfR <- any(as.character(reference[,1]) == as.character(reference[,2]))
> selfR
[1] FALSE
> 
> # restrict reference to edges; first two columns (gene) only
> reference <- reference[reference[,3] == 1,1:2]
> 
> contingencyTables <- contabs(network = edges1ts10, reference = reference,
+                              size = 100)
> 
> roc(contingencyTables)
     area    sector     width 
0.7003922 0.7003922 0.9529412 
> prc(contingencyTables)
    area   sector    width 
0.228439 0.214489 1.000000 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>