A new version of pROC, 1.15.3, has been released and is now available on CRAN. It is a minor bugfix release. Versions 1.15.1 and 1.15.2 were rejected from CRAN.

Here is the full changelog:

• Fix -Inf threshold in coords for curves with direction = ">" (issue 60).
• Keep list order in ggroc (issue 58).
• Fix erroneous error in ci.coords with ret="threshold" (issue 57).
• Restore lazy loading of the data and fix an R CMD check warning "Variables with usage in documentation object 'aSAH' not in code".
• Fix vdiffr unit tests with ggplot2 3.2.0 (issue 53).

The latest version of pROC, 1.15.0 has just been released. It features significant speed improvements, many bug fixes, new methods for use in dplyr pipelines, increased verbosity, and prepares the way for some backwards-incompatible changes upcoming in pROC 1.16.0.

Verbosity

Since its initial release, pROC has been detecting the levels of the positive and negative classes (cases and controls), as well as the direction of the comparison, that is whether values are higher in case or in control observations. Until now it has been doing so silently, but this has lead to several issues and misunderstandings in the past. In particular, because of the detection of direction, ROC curves in pROC will nearly always have an AUC higher than 0.5, which can at times hide problems with certain classifiers, or cause bias in resampling operations such as bootstrapping or cross-validation.

In order to increase transparency, pROC 1.15.0 now prints a message on the command line when it auto-detects one of these two arguments.

	> roc(aSAH$outcome, aSAH$ndka)
	Setting levels: control = Good, case = Poor
	Setting direction: controls < cases

	Call:
	roc.default(response = aSAH$outcome, predictor = aSAH$ndka)

	Data: aSAH$ndka in 72 controls (aSAH$outcome Good) < 41 cases (aSAH$outcome Poor).
	Area under the curve: 0.612

If you run pROC repeatedly in loops, you may want to turn off these diagnostic messsages. The recommended way is to explicitly specify them explicitly:

	roc(aSAH$outcome, aSAH$ndka, levels = c("Good", "Poor"), direction = "<")

Alternatively you can pass quiet = TRUE to the ROC function to silenty ignore them.

	roc(aSAH$outcome, aSAH$ndka, quiet = TRUE)

As mentioned earlier this last option should be avoided when you are resampling, such as in bootstrap or cross-validation, as this could silently hide some biases due to changing directions.

Speed

Several bottlenecks have been removed, yielding significant speedups in the roc function with algorithm = 2 (see issue 44), as well as in the coords function which is now vectorized much more efficiently (see issue 52) and scales much better with the number of coordinates to calculate. With these improvements pROC is now as fast as other ROC R packages such as ROCR.

With Big Data becoming more and more prevalent, every speed up matters and making pROC faster has very high priority. If you think that a particular computation is abnormally slow, for instance with a particular combination of arguments, feel free to submit a bug report.

As a consequence, algorithm = 2 is now used by default for numeric predictors, and is automatically selected by the new algorithm = 6 meta algorithm. algorithm = 3 remains slightly faster with very low numbers of thresholds (below 50) and is still the default with ordered factor predictors.

Pipelines

The roc function can be used in pipelines, for instance with dplyr or magrittr. This is still a highly experimental feature and will change significantly in future versions (see issue 54 for instance). Here is an example of usage:

library(dplyr)
aSAH %>% 
    filter(gender == "Female") %>% 
    roc(outcome, s100b)

The roc.data.frame method supports both standard and non-standard evaluation (NSE), and the roc_ function supports standard evaluation only. By default it returns the roc object, which can then be piped to the coords function to extract coordinates that can be used in further pipelines

aSAH %>%
    filter(gender == "Female") %>%
    roc(outcome, s100b) %>%
	coords(transpose=FALSE) %>%
    filter(sensitivity > 0.6,
           specificity > 0.6)

More details and use cases are available in the ?roc help page.

Transposing coordinates

Since the initial release of pROC, the coords function has been returning a matrix with thresholds in columns, and the coordinate variables in rows.

data(aSAH)
rocobj <- roc(aSAH$outcome, aSAH$s100b)
coords(rocobj, c(0.05, 0.2, 0.5))
#                   0.05       0.2       0.5
# threshold   0.05000000 0.2000000 0.5000000
# specificity 0.06944444 0.8055556 0.9722222
# sensitivity 0.97560976 0.6341463 0.2926829

This format doesn't conform to the grammar of the tidyverse, outlined by Hadley Wickham in his Tidy Data 2014 paper, which has become prevalent in modern R language. In addition, the dropping of dimensions by default makes it difficult to guess what type of data coords is going to return.

	coords(rocobj, "best")
	#   threshold specificity sensitivity 
	#   0.2050000   0.8055556   0.6341463 
	# A numeric vector

Although it is possible to pass drop = FALSE, the fact that it is not the default makes the behaviour unintuitive. In an upcoming version of pROC, this will be changed and coords will return a data.frame with the thresholds in rows and measurement in colums by default.

Changes in 1.15

• Addition of the transpose argument.
• Display a warning if transpose is missing. Pass transpose explicitly to silence the warning.
• Deprecation of as.list.

With transpose = FALSE, the output is a tidy data.frame suitable for use in pipelines:

 coords(rocobj, c(0.05, 0.2, 0.5), transpose = FALSE)
#      threshold specificity sensitivity
# 0.05      0.05  0.06944444   0.9756098
# 0.2       0.20  0.80555556   0.6341463
# 0.5       0.50  0.97222222   0.2926829

It is recommended that new developments set transpose = FALSE explicitly. Currently these changes are neutral to the API and do not affect functionality outside of a warning.

Upcoming backwards incompatible changes in future version (1.16)

The next version of pROC will change the default transpose to FALSE. This is a backward incompatible change that will break any script that did not previously set transpose and will initially come with a warning to make debugging easier. Scripts that set transpose explicitly will be unaffected.

Recommendations

If you are writing a script calling the coords function, set transpose = FALSE to silence the warning and make sure your script keeps running smoothly once the default transpose is changed to FALSE. It is also possible to set transpose = TRUE to keep the current behavior, however is likely to be deprecated in the long term, and ultimately dropped.

New coords return values

The coords function can now return two new values, "youden" and "closest.topleft". They can be returned regardless of whether input = "best" and of the value of the best.method argument, although they will not be re-calculated if possible. They follow the best.weights argument as expected. See issue 48 for more information.

Bug fixes

Several small bugs have been fixed in this version of pROC. Most of them were identified thanks to an increased unit test coverage. 65% of the code is now unit tested, up from 46% a year ago. The main weak points remain the testing of all bootstrapping and resampling operations. If you notice any unexpected or wrong behavior in those, or in any other function, feel free to submit a bug report.

Getting the update

The update his available on CRAN now. You can update your installation by simply typing:

install.packages("pROC")

Here is the full changelog:

• roc now prints messages when autodetecting levels and direction by default. Turn off with quiet = TRUE or set these values explicitly.
• Speedup with algorithm = 2 (issue 44) and in coords (issue 52).
• New algorithm = 6 (used by default) uses algorithm = 2 for numeric data, and algorithm = 3 for ordered vectors.
• New roc.data.frame method and roc_ function for use in pipelines.
• coords can now returns "youden" and "closest.topleft" values (issue 48).
• New transpose argument for coords, TRUE by default (issue 54).
• Use text instead of Tcl/Tk progress bar by default (issue 51).
• Fix method = "density" smoothing when called directly from roc (issue 49).
• Renamed roc argument n to smooth.n.
• Fixed 'are.paired' ignoring smoothing arguments of roc2 with return.paired.rocs.
• New ret option "all" in coords (issue 47)
• drop in coords now drops the dimension of ret too (issue 43)

pROC 1.14.0 was released with many bug fixes and some new features.

Multiclass ROC

The multiclass.roc function can now take a multivariate input with columns corresponding to scores of the different classes. The columns must be named with the corresponding class labels. Thanks Matthias Döring for the contribution.

Let's see how to use it in practice with the iris dataset. Let's first split the dataset into a training and test sets:

data(iris)
iris.sample <- sample(1:150)
iris.train <- iris[iris.sample[1:75],]
iris.test <- iris[iris.sample[76:150],]

We'll use the nnet package to generate some predictions. We use the type="prob" to the predict function to get class probabilities.

library("nnet")
mn.net <- nnet::multinom(Species ~ ., iris.train)

iris.predictions <- predict(mn.net, newdata=iris.test, type="prob")
head(iris.predictions)

	          setosa   versicolor    virginica
	63  2.877502e-21 1.000000e+00 6.647660e-19
	134 1.726936e-27 9.999346e-01 6.543642e-05
	150 1.074627e-28 7.914019e-03 9.920860e-01
	120 6.687744e-34 9.986586e-01 1.341419e-03
	6   1.000000e+00 1.845491e-24 6.590050e-72
	129 4.094873e-45 1.779882e-15 1.000000e+00

Notice the column names, identical to the class labels. Now we can use the multiclass.roc function directly:

multiclass.roc(iris.test$Species, iris.predictions)

Many modelling functions have similar interfaces, where the output of predict can be changed with an extra argument. Check their documentation to find out how to get the required data.

Multiple aesthetics for ggroc

It is now possible to pass several aesthetics to ggroc. So for instance you can map a curve to both colour and linetype:

roc.list <- roc(outcome ~ s100b + ndka + wfns, data = aSAH)
ggroc(roc.list, aes=c("linetype", "color"))

Mapping 3 ROC curves to 2 aesthetics with ggroc.

Getting the update

The update his available on CRAN now. You can update your installation by simply typing:

install.packages("pROC")

Here is the full changelog:

• The multiclass.roc function now accepts multivariate decision values (code contributed by Matthias Döring).
• ggroc supports multiple aesthetics.
• Make ggplot2 dependency optional.
• Suggested packages can be installed interactively when required.
• Passing both cases and controls or response and predictor arguments is now an error.
• Many small bug fixes.

pROC 1.13.0 was just released with bug fixes and a new feature.

Infinite values in predictor

Following the release of pROC 1.12, it quickly became clear with issue #30 that infinite values were handled differently by the different algorithms of pROC. The problem with these values is that they cannot be thresholded. An Inf will always be greater than any value. This means that in some cases, it may not be possible to reach 0 or 100% specificity or sensitivity. This also revealed that threshold-agnostic algorithms such as algorithm="2" or the DeLong theta calculations would happily reach 0 or 100% specificity or sensitivity in those case, although those values are unattainable.

Starting with 1.13.0, when pROC's roc function finds any infinite value in the predictor argument, or in controls or cases, it will return NaN (not a number).

Numerical accuracy

The handling of near ties close to + or - Infinity or 0 has been improved by calculating the threshold (which is the mean between two consecutive values) differently depending on the mean value itself. This allows preserving as much precision close to 0 without maxing out large absolute values.

New argument for ggroc

ggroc can now take a new value for the aes argument, aes="group". Consistent with ggplot2, it allows to curves with identical aesthetics to be split in different groups. This is especially useful for instance in facetted plots.

library(pROC)
data(aSAH)
roc.list <- roc(outcome ~ s100b + ndka + wfns, data = aSAH)
g.list <- ggroc(roc.list)
g.group <- ggroc(roc.list, aes="group")
g.group + facet_grid(.~name)

Facetting of 3 ROC curves with ggroc.

Getting the update

The update has just been accepted on CRAN and should be online soon. Once it is out, update your installation by simply typing:

install.packages("pROC")

The full changelog is:

• roc now returns NaN when predictor contains infinite values ( issue #30).
• Better handling of near-ties near +-Infinity and 0.
• ggroc supports aes="group" to allow curves with identical aesthetics.

A major regression slipped into yesterday's release 1.12.0 of pROC. The fix of issue #25 allocated a matrix which could quickly become very large for bigger datasets, and cause significant slow downs as the machine allocated the memory, or even crash pROC with an error message such as cannot allocate vector of size ... for larger datasets. This bug (issue #29) is fixed (and now automatically tested for) in pROC 1.12.1.

Please update your installation by simply typing:

install.packages("pROC")

I just released pROC 1.12.0, which fixes several bugs and should significantly improve the performance on large datasets by selecting the best algorithm automatically.

Issue 25

GitHub issue #25 identified two distinct bugs causing ci.auc, var and other functions to fail when calculating DeLong placements with the following error message, with different variation of numbers:

Error in delongPlacements(roc) :
 A problem occured while calculating DeLong's theta: got 0.50057161522129678399 instead of 0.50032663726931247972. This is a bug in pROC, please report it to the maintainer.

pROC calculates the AUC with the trapezoidal method. This is the AUC obtained when calling auc(roc). When using the DeLong method (for roc.test, var etc.), the AUC is also calculated with an other method (similar to the Wilcoxon/Mann-Whitney statistic). These two values should be identical, at least down to something close to the floating point precision of the hardware, typically below 10^-8. To be on the safe side, pROC checks this assumption after calculating the DeLong AUC.

The first sub-issue caused by a broken conversion of the roc curve from percent to fractions for internal calculations, followed by a broken check that the DeLong code produced the correct AUC. In combination, these two bugs caused pROC to stop with an error message in the specific case where percent=TRUE and direction=">". The check was introduced in pROC version 1.9.1. The bug in the conversion from percent to fraction was present in earlier versions, however it never affected calculations, which is why it was left unnoticed until the check was added. Both bugs are now fixed.

The second sub-issue was impacting the calculation of the thresholds. When two predictor values were too close, their mean could not be represented exactly in the IEEE 754 arithmetic and the result would rounded back to one or the other value, pretty much arbitrarily depending on the implementation.

 a <- c(0.65354946699793847742, 0.65354946699793858844)
> print(mean(a), digits = 20)
[1] 0.65354946699793847742
> mean(a) == a
[1]  TRUE FALSE

Because pROC calculates the ROC thresholds as the mean between consecutive predictor observations, this would cause some comparisons to be incorrect when calculating sensitivity and specificity. As a consequence, erroneous sensitivities, sensitivities and AUCs may have been reported in the past. The issue was fixed by carefully selecting the correct value for threshold in case the mean was identical to a predictor value.

Other bug fixes

• GitHub issue #27 caused ci.auc to return NaN when cases or controls contained only a single observation. The function has been fixed and now returns NA as expected.
• power.roc.curve failed with ROC curves having percent=TRUE. This issue was identified when adding testthat unit tests for the function.
• ci(..., of="coords") returned the ci function instead of calculating the CI.
• C++ code now check for user interrupts regularly with Rcpp::checkUserInterrupt() so that very long runs can be aborted from R.
• A better error message (instead of a useless internal garbage message error) is now displayed when attempting to return threshold with ci.coords. An empirical ROC curve, like one produced by pROC, is is made of discrete points that concentrate all the possible thresholds. Lines are added to join the points visually on the plot, however they do not contain any actual threshold. Returning thresholds at an arbitrary sensitivity or specificity requires either to be very lucky to have a point at the exact desired value, or to interpolate thresholds between the points. Interpolating is more tricky than it sounds and is very sensitive to the method of calculation of the threshold (very different results will be returned by pROC that uses the mean between consecutive predictor, than by some other packages which use the values directly).

New algorithm

A new "meta" algorithm is introduced in the roc function. algorithm = 5 causes pROC to automatically select algorithm 2 or 3, based on the number of threshold of the ROC curve. Algorithm 3 has a time complexity of O(N^2). It behaves very well when the number of thresholds remains low. However its square term can cause a catastrophic slowdown with very large datasets where the predictor takes nearly continuous values, and the ROC curve contains many thresholds (typically this will become very obvious above 10000 thresholds). Algorithm 2 has an algorithmic complexity of O(N), and shows a much better performance with large number of data points. However it comes with a rather large pre-factor which makes it very inefficient in most small- to normal-sized datasets. The decisive factor is the number of thresholds, and pROC will select algorithm 2 in curves with more than about 1500 thresholds, 3 otherwise. Algorithm 5 is now the default algorithm in pROC which should significantly improve the performance with large datasets, without impacting the speed in most cases.

Getting the update

The update has just been submitted to the CRAN and should be online soon. Once it is out, update your installation by simply typing:

install.packages("pROC")

The full changelog is:

• Fix bug that crashed DeLong calculations when predictor had near-ties close to the floating point precision limit that were rounded back to a predictor value (issue #25).
• Fix bug that crashed ci.auc and var if direction was ">" and percent=TRUE (issue #25).
• Fix bug causing ci to return NaN values with method="delong" when cases or controls had a single observation (issue #27).
• Fix power.roc.curve failed with curves having percent=TRUE.
• Fix ci(..., of="coords") returned the ci function instead of the CI.
• C++ code now check for user interrupts regularly with Rcpp::checkUserInterrupt().
• Better error message for ci.coords attempting to return threshold.
• New algorithm = 5 (used by default) chooses the algorithm based on the number of thresholds to avoid worst case with algorithm = 3.

pROC 1.11.0 is now on CRAN! This is a minor update that mostly fixes notes in CRAN checks. It also adds support for the legacy.axes argument to change the axis labeling in ggroc.

The full changelog is:

• Added argument legacy.axes to ggroc
• Fix NOTE about "apparent S3 methods exported but not registered" in R CMD check

A new update of pROC is now available on CRAN: version 1.10.0.

ggplot2 support (Experimental)

A new function was introduced: ggroc. Given a roc object, or a (optionally named) list of roc objects, it returns a ggplot object, that can then be printed, with optional aesthetics, themes etc. Here is a basic example:

library(pROC)
# Create a basic roc object
data(aSAH)
rocobj <- roc(aSAH$outcome, aSAH$s100b)
rocobj2 <- roc(aSAH$outcome, aSAH$wfns)

library(ggplot2)
# Multiple curves:
gg2 <- ggroc(list(s100b=rocobj, wfns=rocobj2))
gg2

Basic ggplot with two ROC curves.

The usual ggplot syntax applies, so you can add themes, labels, etc. Note the aes argument, which control the aesthetics for geom_line to map to the different ROC curves supplied. Here we use "linetype" instead of the default color:

# with additional aesthetics:
gg2b <- ggroc(list(s100b=rocobj, wfns=rocobj2), aes="linetype", color="red")
# You can then your own theme, etc.
gg2b + theme_minimal() + ggtitle("My ROC curve")

Basic ggplot with two ROC curves.

This functionality is currently experimental and subject to change. Please report bugs and feedback on pROC's GitHub issue tracker.

Precision and recall in coords

The coords function supports two new ret values: "precision" and "recall":

library(pROC)
# Create a basic roc object
data(aSAH)
rocobj <- roc(aSAH$outcome, aSAH$s100b)
coords(rocobj, "best", ret = c("threshold", "sensitivity", "specificity", "precision", "recall"))
  threshold sensitivity specificity   precision      recall 
  0.2050000   0.6341463   0.8055556   0.6500000   0.6341463

It makes it very easy to get a Precision-Recall (PR) plot:

plot(precision ~ recall, t(coords(rocobj, "all", ret = c("recall", "precision"))), type="l", main = "PR plot of S100B")

A simple PR plot.

Automated testing

Several functions are now covered with tests (powered by the testthat package) to ensure correct behavior. This allowed me to find and fix a few glitches. It will also make it easier to refactor code in the future.

The tests are automatically run by R CMD check. Additional tests that are too slow to be enabled by defauld can be activated with the RUN_SLOW_TESTS environment variable.

export RUN_SLOW_TESTS=true
R CMD check pROC

Test results can be seen on Travis CI, and the coverage of the tests can be seen on Codecov. Currently 30% of the code is tested. This includes most functionality, with the exception of bootstrapping and smoothing which I plan to implement in the future.

Obtaining the update

To update your installation, simply type:

install.packages("pROC")

Here is the full changelog:

• Basic ggplot2 support (one and multiple ROC curves)
• Implement precision and recall for coords
• Fix: properly handle NAs in cases when passing cases/controls to roc (thanks Thomas König for the report)
• Fix various minor bugs detected with new unit tests

Can php's htmlspecialchars delete your data? The answer, unfortunately, is yes.

I just updated a database server with a web interface from PHP 5.3 (in Ubuntu 12.04) to PHP 7 (Ubuntu 16.04.2). It went pretty smoothly, but after a couple of weeks, users started reporting missing data in some fields where they were expecting some. After some investigation, it turns out the curlpit is the htmlspecialchars function, which changed behaviour with the update. Given the following script:

<?php 
$string = "An e acute character: \xE9\n";
echo htmlspecialchars($string);
?>

In PHP 5.3, it would output:

An e acute character: �

Now with PHP >= 5.4, here's the output:

Yep, that's correct: the output is empty. PHP just discarded the whole string. Without even a warning!

While this is documented in the manual, this is the most stupid and destructive design I have seen in a long while. Data loss guaranteed when the user saves the page without realizing some fields are accidentally empty! How can anyone be so brain dead and design and implement such a behaviour? Without even a warning!

It turns out one has to define the encoding for the function to work with non-UTF8 characters:

htmlspecialchars($string, ENT_COMPAT,'ISO-8859-1', true);

As this is a legacy application dating back more than 15 years, I fully expect some strings to be broken beyond repair. Thus I wrote the following function to replace all the calls to htmlspecialchars:

function safe_htmlspecialchars($string) {
	$htmlstring = htmlspecialchars($string, ENT_COMPAT,'ISO-8859-1', true);
  
        if (strlen($string) > 0 && strlen($htmlstring) == 0) {
                trigger_error ("htmlspecialchars failed to convert data", E_USER_ERROR);
        }
}

Displaying an error in case of doubt is the only sensible behaviour here, and should be the default.

Moral of the story: I'm never using PHP in a new project again. And neither should you, if you value your data more than the PHP developers who clearly don't.

After nearly two years since the previous release, pROC 1.9.1 is finally available on CRAN. Here is a list of the main changes:

• subset and na.action arguments now handled properly in roc.formula. This means you can now do something like this:
  

  data(aSAH)
  roc(outcome ~ s100b, data=aSAH, subset=(gender == "Male"))
  roc(outcome ~ s100b, data=aSAH, subset=(gender == "Female"))

  Thanks Terry Therneau for the report.
• Added policies to handle the case where a ROC curve has multiple "best" threshold in ci.coords. The following policies are available:
  • "stop" will abort the processing and throw an error (with stop). This is the default.
  • "omit" will ignore the sample (as in NULL). This can lead to a reduced effective number of usable sample in the final statistic.
  • "random" will select one of the threshold randomly.
  Here is an example of usage:
  

  data(aSAH)
  ci.coords(aSAH$outcome, aSAH$s100b, x="best", input = "threshold", 
            ret=c("specificity", "ppv", "tp"),
            best.policy = "random")

  Thanks Nicola Toschi for the report.
• Support xlim and ylim gracefully in plot.roc.
• Improved validation of input class levels and direction; A message can be printed when auto-detecting, use the quiet argument to turn on.
• Removed extraneous name attribute on the p.value (thanks Paweł Kleka for the report).
• Faster DeLong algorithm (code contributed by Stefan Siegert). The code is based on the algorithm by Xu Sun and Weichao Xu (2014) that has an O(N log N) complexity instead of O(N²).
  The DeLong algorithm is now always faster than bootstrapping, even in the previous edge case of ROC curve with large number of samples and few thresholds where bootstrapping used to be faster. Here is a quick example with 200000 data points:

  library(pROC)
  n <- 200000
  a <- as.numeric(cut(rnorm(n), c(-Inf, -1, 0, 1, Inf)))
  b <- round(runif(n))
  r <- roc(b, a, algorithm = 3)
  
  # With Bootstrap
  > system.time(var(r, method = "b", progress = "none"))
  utilisateur     système      écoulé 
       25.896       0.136      26.027 
  
  # With old DeLong algorithm
  > system.time(var(r, method = "d"))
  utilisateur     système      écoulé 
       47.352       0.008      47.353 
  
  # With new DeLong algorithm
  > system.time(var(r, method = "d"))
  utilisateur     système      écoulé 
        0.016       0.008       0.023 

Obtaining the update

To update your installation, simply type:

install.packages("pROC")

References

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.