pROC-package {pROC} | R Documentation |
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves. Sample size / power computation for one or two ROC curves are available.
The basic unit of the pROC package is the roc
function. It
will build a ROC curve, smooth it if requested (if smooth=TRUE
),
compute the AUC (if auc=TRUE
), the confidence interval (CI) if
requested (if ci=TRUE
) and plot the curve if requested (if
plot=TRUE
).
The roc
function will call smooth
,
auc
,
ci
and plot
as necessary. See these
individual functions for the arguments that can be passed to them
through roc
. These function can be called separately.
Two paired (that is roc
objects with the same
response
) or unpaired (with different response
) ROC
curves can be compared with the roc.test
function.
If you use pROC in published research, please cite the following paper:
Xavier Robin, Natacha Turck, Alexandre Hainard, Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez and Markus Müller (2011). “pROC: an open-source package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 12, p. 77. DOI: 10.1186/1471-2105-12-77
Type citation("pROC")
for a BibTeX entry.
The authors would be glad to hear how pROC is employed. You are kindly encouraged to notify Xavier Robin <Xavier.Robin@unige.ch> about any work you publish.
The following abbreviations are employed extensively in this package:
ROC: receiver operating characteristic
AUC: area under the ROC curve
pAUC: partial area under the ROC curve
CI: confidence interval
SP: specificity
SE: sensitivity
roc | Build a ROC curve |
are.paired | Dertermine if two ROC curves are paired |
auc | Compute the area under the ROC curve |
ci | Compute confidence intervals of a ROC curve |
ci.auc | Compute the CI of the AUC |
ci.coords | Compute the CI of arbitrary coordinates |
ci.se | Compute the CI of sensitivities at given specificities |
ci.sp | Compute the CI of specificities at given sensitivities |
ci.thresholds | Compute the CI of specificity and sensitivity of thresholds |
ci.coords | Compute the CI of arbitrary coordinates |
coords | Coordinates of the ROC curve |
cov | Covariance between two AUCs |
ggroc | Plot a ROC curve with ggplot2 (Experimental) |
has.partial.auc | Determine if the ROC curve have a partial AUC |
lines.roc | Add a ROC line to a ROC plot |
plot.ci | Plot CIs |
plot | Plot a ROC curve |
print | Print a ROC curve object |
roc.test | Compare the AUC of two ROC curves |
smooth | Smooth a ROC curve |
var | Variance of the AUC |
This package comes with a dataset of 141 patients with aneurysmal
subarachnoid hemorrhage: aSAH
.
To install this package, make sure you are connected to the internet and issue the following command in the R prompt:
install.packages("pROC")
To load the package in R:
library(pROC)
All the bootstrap operations for significance testing, confidence interval, variance and covariance computation are performed with non-parametric stratified or non-stratified resampling (according to the stratified
argument) and with the percentile method, as described in Carpenter and Bithell (2000) sections 2.1 and 3.3.
Stratification of bootstrap can be controlled
with boot.stratified
. In stratified bootstrap (the default), each replicate
contains the same number of cases and controls than the original
sample. Stratification is especially useful if one group has only
little observations, or if groups are not balanced.
The number of bootstrap replicates is controlled by boot.n
. Higher numbers will give a more precise estimate of the significance tests and confidence intervals
but take more time to compute. 2000 is recommanded by Carpenter and Bithell (2000) for confidence intervals. In our experience this is sufficient for a good estimation of the
first significant digit only, so we recommend the use of 10000 bootstrap replicates to obtain a good estimate of the second significant digit whenever possible.
A progressbar shows the progress of bootstrap operations. It is handled by the plyr package (Wickham, 2011),
and is created by the progress_*
family of functions.
Sensible defaults are guessed during the package loading:
In non-interactive mode, no progressbar is displayed.
In embedded GNU Emacs “ESS”, a txtProgressBar
In Windows, a winProgressBar
bar.
In other systems with a graphical display, a tkProgressBar
.
In systems without a graphical display, a txtProgressBar
.
The default can be changed with the option “pROCProgress”. The option must be a list with
a name
item setting the type of progress bar (“none”, “win”, “tk”
or “text”). Optional items of the list are “width”, “char” and “style”,
corresponding to the arguments to the underlying progressbar functions.
For example, to force a text progress bar:
options(pROCProgress = list(name = "text", width = NA, char = "=", style = 3)
To inhibit the progress bars completely:
options(pROCProgress = list(name = "none"))
Versions 1.6 and 1.7 of pROC focused on execution speed to handle large datasets. Let's say we have the following dataset with 100 thousands observations:
response <- round(runif(1E5)) predictor <- rnorm(1E5) system.time(rocobj <- roc(response, predictor)) # Very slow!
roc
By default, pROC uses an algorithm that linearly depends on the number of thresholds. An naive optimization is to reduce the precision of the predictor by generating ties in the data
system.time(rocobj <- roc(response, round(predictor))) # Faster - but losing precision
Since version 1.6, pROC contains an alternative algorithm with an overhead growing linearly as a function of the number of observations. Use the algorithm=2
arguments when calling roc
.
system.time(rocobj <- roc(response, predictor, algorithm = 2)) # Better
When unsure about the fastest algorithm, use algorithm=0
to choose between 2 and 3. Make sure microbenchmark is installed. Beware, this is very slow as it will repeat the computation 10 times to obtain a decent estimate of each algorithm speed.
if (!require(microbenchmark)) install.packages("microbenchmark") rocobj <- roc(response, round(predictor), algorithm = 0) rocobj <- roc(response, predictor, algorithm = 0) # Very slow!
Bootstrap is typically slow because it involves repeatedly computing the ROC curve (or a part of it).
Some bootstrap functions are faster than others. Typically, ci.thresholds
is the fastest, and ci.coords
the slowest. Use ci.coords
only if the CI you need cannot be computed by the specialized CI functions ci.thresholds
, ci.se
and ci.sp
. Note that ci.auc
cannot be replaced anyway.
A naive way to speed-up the boostrap is by removing the progress bar:
rocobj <- roc(response, round(predictor)) system.time(ci(rocobj)) system.time(ci(rocobj, progress = "none"))
It is of course possible to reduce the number of boostrap iterations. See the boot.n
argument to ci
. This will reduce the precision of the bootstrap estimate.
Bootstrap operations can be performed in parallel. The backend provided by the plyr package is used, which in turn relies on the foreach package.
To enable parallell processing, you first need to load an adaptor for the foreach package (doMC, doMPI, doParallel, doRedis, doRNG or doSNOW)), register the backend, and set parallel=TRUE
.
library(doParallel) registerDoParallel(cl <- makeCluster(getOption("mc.cores", 2))) ci(rocobj, method="bootstrap", parallel=TRUE) stopCluster(cl)
Progress bars are not available when parallel processing is enabled.
DeLong is an asymptotically exact method to evaluate the uncertainty of an AUC (DeLong et al. (1988)). Since version 1.9, pROC uses the algorithm proposed by Sun and Xu (2014) which has an O(N log N) complexity and is always faster than bootstrapping. By default, pROC will choose the DeLong method whenever possible.
rocobj <- roc(response, round(predictor), algorithm=3) system.time(ci(rocobj, method="delong")) system.time(ci(rocobj, method="bootstrap", parallel = TRUE))
Xavier Robin, Natacha Turck, Jean-Charles Sanchez and Markus Müller
Maintainer: Xavier Robin <Xavier.Robin@unige.ch>
James Carpenter and John Bithell (2000) “Bootstrap condence intervals: when, which, what? A practical guide for medical statisticians”. Statistics in Medicine 19, 1141–1164. DOI: 10.1002/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F.
Elisabeth R. DeLong, David M. DeLong and Daniel L. Clarke-Pearson (1988) “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach”. Biometrics 44, 837–845.
Tom Fawcett (2006) “An introduction to ROC analysis”. Pattern Recognition Letters 27, 861–874. DOI: 10.1016/j.patrec.2005.10.010.
Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an open-source package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. DOI: 10.1186/1471-2105-12-77.
Xu Sun and Weichao Xu (2014) “Fast Implementation of DeLongs Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves”. IEEE Signal Processing Letters, 21, 1389–1393. DOI: 10.1109/LSP.2014.2337313.
Hadley Wickham (2011) “The Split-Apply-Combine Strategy for Data Analysis”. Journal of Statistical Software, 40, 1–29. URL: www.jstatsoft.org/v40/i01.
CRAN packages ROCR, verification or Bioconductor's roc for ROC curves.
CRAN packages plyr, MASS and logcondens employed in this package.
data(aSAH) # Build a ROC object and compute the AUC roc(aSAH$outcome, aSAH$s100b) roc(outcome ~ s100b, aSAH) # Smooth ROC curve roc(outcome ~ s100b, aSAH, smooth=TRUE) # more options, CI and plotting roc1 <- roc(aSAH$outcome, aSAH$s100b, percent=TRUE, # arguments for auc partial.auc=c(100, 90), partial.auc.correct=TRUE, partial.auc.focus="sens", # arguments for ci ci=TRUE, boot.n=100, ci.alpha=0.9, stratified=FALSE, # arguments for plot plot=TRUE, auc.polygon=TRUE, max.auc.polygon=TRUE, grid=TRUE, print.auc=TRUE, show.thres=TRUE) # Add to an existing plot. Beware of 'percent' specification! roc2 <- roc(aSAH$outcome, aSAH$wfns, plot=TRUE, add=TRUE, percent=roc1$percent) ## Coordinates of the curve ## coords(roc1, "best", ret=c("threshold", "specificity", "1-npv")) coords(roc2, "local maximas", ret=c("threshold", "sens", "spec", "ppv", "npv")) ## Confidence intervals ## # CI of the AUC ci(roc2) ## Not run: # CI of the curve sens.ci <- ci.se(roc1, specificities=seq(0, 100, 5)) plot(sens.ci, type="shape", col="lightblue") plot(sens.ci, type="bars") ## End(Not run) # need to re-add roc2 over the shape plot(roc2, add=TRUE) ## Not run: # CI of thresholds plot(ci.thresholds(roc2)) ## End(Not run) # In parallel if (require(doParallel)) { registerDoParallel(cl <- makeCluster(getOption("mc.cores", 2L))) ## Not run: ci(roc2, method="bootstrap", parallel=TRUE) stopCluster(cl) } ## Comparisons ## # Test on the whole AUC roc.test(roc1, roc2, reuse.auc=FALSE) ## Not run: # Test on a portion of the whole AUC roc.test(roc1, roc2, reuse.auc=FALSE, partial.auc=c(100, 90), partial.auc.focus="se", partial.auc.correct=TRUE) # With modified bootstrap parameters roc.test(roc1, roc2, reuse.auc=FALSE, partial.auc=c(100, 90), partial.auc.correct=TRUE, boot.n=1000, boot.stratified=FALSE) ## End(Not run)