pROC (version 1.13.0)

roc: Build a ROC curve

Description

This is the main function of the pROC package. It builds a ROC curve and returns a “roc” object, a list of class “roc”. This object can be printed, plotted, or passed to the functions auc, ci, smooth.roc and coords. Additionally, two roc objects can be compared with roc.test.

Usage

roc(...)
# S3 method for formula
roc(formula, data, ...)
# S3 method for default
roc(response, predictor, controls, cases,
density.controls, density.cases,
levels=base::levels(as.factor(response)), percent=FALSE, na.rm=TRUE,
direction=c("auto", "<", "="">"), algorithm = 5, quiet = TRUE, 
smooth=FALSE, auc=TRUE, ci=FALSE, plot=FALSE, smooth.method="binormal",
ci.method=NULL, density=NULL, ...)

Arguments

response

a factor, numeric or character vector of responses, typically encoded with 0 (controls) and 1 (cases). Only two classes can be used in a ROC curve. If the vector contains more than two unique values, or if their order could be ambiguous, use levels to specify which values must be used as control and case value.

predictor

a numeric vector of the same length than response, containing the predicted value of each observation. An ordered factor is coerced to a numeric.

controls, cases

instead of response, predictor, the data can be supplied as two numeric or ordered vectors containing the predictor values for control and case observations.

density.controls, density.cases

a smoothed ROC curve can be built directly from two densities on identical x points, as in smooth.

formula, data

a formula of the type response~predictor. If mulitple predictors are passed, a named list of roc objects will be returned. Additional arguments data, subset and na.action are supported, see model.frame for more details.

levels

the value of the response for controls and cases respectively. By default, the first two values of levels(as.factor(response)) are taken, and the remaining levels are ignored. It usually captures two-class factor data correctly, but will frequently fail for other data types (response factor with more than 2 levels, or for example if your response is coded “controls” and “cases”, the levels will be inverted) and must then be specified here. If your data is coded as 0 and 1 with 0 being the controls, you can safely omit this argument.

percent

if the sensitivities, specificities and AUC must be given in percent (TRUE) or in fraction (FALSE, default).

na.rm

if TRUE, the NA values will be removed (ignored by roc.formula).

direction

in which direction to make the comparison? “auto” (default): automatically define in which group the median is higher and take the direction accordingly. “>”: if the predictor values for the control group are higher than the values of the case group (controls > t >= cases). “<”: if the predictor values for the control group are lower or equal than the values of the case group (controls < t <= cases).

algorithm

the method used to compute sensitivity and specificity, an integer of length 1 between 0 and 4. 1 (default): a safe, well-tested, pure-R code that is efficient when the number of thresholds is low. It goes with O(T*N). 2: an alternative pure-R algorithm that goes in O(N). Typically faster than 1 when the number of thresholds of the ROC curve is above 1000. Less tested than 1. 3: a C++ implementation of 1, about 3-5x faster. Typically the fastest with ROC curves with less than 1500 thresholds, but has a very bad worst-case when that number increases. 4 (debug only, slow): runs all 3 algorithms and makes sure they return the same values. 5: quickly select 2 or 3 based on the number of thresholds. 0: use microbenchmark to choose between 2 and 3.

quiet

set to FALSE to turn on messages when direction and levels are auto-detected. This default will be changed to TRUE in the future.

smooth

if TRUE, the ROC curve is passed to smooth to be smoothed.

auc

compute the area under the curve (AUC)? If TRUE (default), additional arguments can be passed to auc.

ci

compute the confidence interval (CI)? If set to TRUE, additional arguments can be passed to ci.

plot

plot the ROC curve? If TRUE, additional arguments can be passed to plot.roc.

smooth.method, ci.method

in roc.formula and roc.default, the method arguments to smooth (if smooth=TRUE) and of="auc") must be passed as smooth.method and ci.method to avoid confusions.

density

density argument passed to smooth.

…

further arguments passed to or from other methods, and especially:

• auc: partial.auc, partial.auc.focus, partial.auc.correct.

• ci: of, conf.level, boot.n, boot.stratified, progress

• ci.auc:, reuse.auc, method

• ci.thresholds: thresholds

• ci.sp: sensitivities

• ci.se: specificities

• plot.roc: add, col and most other arguments to the plot.roc function. See plot.roc directly for more details.

• smooth: method, n, and all other arguments. See smooth for more details.

Value

If the data contained any NA value and na.rm=FALSE, NA is returned. Otherwise, if smooth=FALSE, a list of class “roc” with the following fields:

auc

if called with auc=TRUE, a numeric of class “auc” as defined in auc.

ci

if called with ci=TRUE, a numeric of class “ci” as defined in ci.

response

the response vector. Patients whose response is not %in% levels are discarded. If NA values were removed, a na.action attribute similar to na.omit stores the row numbers.

predictor

the predictor vector converted to numeric as used to build the ROC curve. Patients whose response is not %in% levels are discarded. If NA values were removed, a na.action attribute similar to na.omit stores the row numbers.

original.predictor, original.response

the response and predictor vectors as passed in argument.

levels

the levels of the response as defined in argument.

controls

the predictor values for the control observations.

cases

the predictor values for the cases.

percent

if the sensitivities, specificities and AUC are reported in percent, as defined in argument.

direction

the direction of the comparison, as defined in argument.

fun.sesp

the function used to compute sensitivities and specificities. Will be re-used in bootstrap operations.

sensitivities

the sensitivities defining the ROC curve.

specificities

the specificities defining the ROC curve.

thresholds

the thresholds at which the sensitivities and specificities were computed.

call

how the function was called. See match.call for more details.

If smooth=TRUE a list of class smooth.roc as returned by smooth, with or without additional elements auc and ci (according to the call).

Errors

If no control or case observation exist for the given levels of response, no ROC curve can be built and an error is triggered with message “No control observation” or “No case observation”.

If the predictor is not a numeric or ordered, as defined by as.numeric or as.ordered, the message “Predictor must be numeric or ordered” is returned.

The message “No valid data provided” is issued when the data wasn't properly passed. Remember you need both response and predictor of the same (not null) length, or bot controls and cases. Combinations such as predictor and cases are not valid and will trigger this error.

Infinite values of the predictor cannot always be thresholded by infinity and can cause ROC curves to not reach 0 or 100% specificity or sensitivity. Since version 1.13.0, pROC returns NaN with a warning message “Infinite value(s) in predictor” if predictor contains any infinite values.

Details

This function's main job is to build a ROC object. See the “Value” section to this page for more details. Before returning, it will call (in this order) the smooth, auc, ci and plot.roc functions if smooth auc, ci and plot.roc (respectively) arguments are set to TRUE. By default, only auc is called.

Data can be provided as response, predictor, where the predictor is the numeric (or ordered) level of the evaluated signal, and the response encodes the observation class (control or case). The level argument specifies which response level must be taken as controls (first value of level) or cases (second). It can safely be ignored when the response is encoded as 0 and 1, but it will frequently fail otherwise. By default, the first two values of levels(as.factor(response)) are taken, and the remaining levels are ignored. This means that if your response is coded “control” and “case”, the levels will be inverted.

In some cases, it is more convenient to pass the data as controls, cases, but both arguments are ignored if response, predictor was specified to non-NULL values. It is also possible to pass density data with density.controls, density.cases, which will result in a smoothed ROC curve even if smooth=FALSE, but are ignored if response, predictor or controls, cases are provided.

Specifications for auc, ci and plot.roc are not kept if auc, ci or plot are set to FALSE. Especially, in the following case:

    myRoc <- roc(..., auc.polygon=TRUE, grid=TRUE, plot=FALSE)
    plot(myRoc)
  

the plot will not have the AUC polygon nor the grid. Similarly, when comparing “roc” objects, the following is not possible:

    roc1 <- roc(..., partial.auc=c(1, 0.8), auc=FALSE)
    roc2 <- roc(..., partial.auc=c(1, 0.8), auc=FALSE)
    roc.test(roc1, roc2)
  

This will produce a test on the full AUC, not the partial AUC. To make a comparison on the partial AUC, you must repeat the specifications when calling roc.test:

    roc.test(roc1, roc2, partial.auc=c(1, 0.8))
  

Note that if roc was called with auc=TRUE, the latter syntax will not allow redefining the AUC specifications. You must use reuse.auc=FALSE for that.

References

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.

See Also

auc, ci, plot.roc, print.roc, roc.test

Examples

# NOT RUN {
data(aSAH)

# Basic example
roc(aSAH$outcome, aSAH$s100b,
    levels=c("Good", "Poor"))
# As levels aSAH$outcome == c("Good", "Poor"),
# this is equivalent to:
roc(aSAH$outcome, aSAH$s100b)
# In some cases, ignoring levels could lead to unexpected results
# Equivalent syntaxes:
roc(outcome ~ s100b, aSAH)
roc(aSAH$outcome ~ aSAH$s100b)
with(aSAH, roc(outcome, s100b))
with(aSAH, roc(outcome ~ s100b))

# With a formula:
roc(outcome ~ s100b, data=aSAH)

# Using subset (only with formula)
roc(outcome ~ s100b, data=aSAH, subset=(gender == "Male"))
roc(outcome ~ s100b, data=aSAH, subset=(gender == "Female"))

# With numeric controls/cases
roc(controls=aSAH$s100b[aSAH$outcome=="Good"], cases=aSAH$s100b[aSAH$outcome=="Poor"])
# With ordered controls/cases
roc(controls=aSAH$wfns[aSAH$outcome=="Good"], cases=aSAH$wfns[aSAH$outcome=="Poor"])

# Inverted the levels: "Poor" are now controls and "Good" cases:
roc(aSAH$outcome, aSAH$s100b,
    levels=c("Poor", "Good"))

# The result was exactly the same because of direction="auto".
# The following will give an AUC < 0.5:
roc(aSAH$outcome, aSAH$s100b,
    levels=c("Poor", "Good"), direction="<")

# If we are sure about levels and direction auto-detection,
# we can turn off the messages:
roc(aSAH$outcome, aSAH$s100b, quiet = TRUE)

# If we prefer counting in percent:
roc(aSAH$outcome, aSAH$s100b, percent=TRUE)

# Test the different algorithms:
roc(aSAH$outcome, aSAH$s100b, algorithm = 1)
roc(aSAH$outcome, aSAH$s100b, algorithm = 2)
roc(aSAH$outcome, aSAH$s100b, algorithm = 3)
if (require(microbenchmark)) {
  roc(aSAH$outcome, aSAH$s100b, algorithm = 0)
}

# Plot and CI (see plot.roc and ci for more options):
roc(aSAH$outcome, aSAH$s100b,
    percent=TRUE, plot=TRUE, ci=TRUE)

# Smoothed ROC curve
roc(aSAH$outcome, aSAH$s100b, smooth=TRUE)
# this is not identical to
smooth(roc(aSAH$outcome, aSAH$s100b))
# because in the latter case, the returned object contains no AUC
# }