roc(pROC)Spotfire S+ Documentation

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(x, ...)
## S3 method for class 'formula':
roc(formula, data, ...)
## Default S3 method:
roc(response, predictor,
levels=getFunction("levels")(as.factor(response)), percent=FALSE, na.rm=TRUE,
direction=c("auto", "<", ">"), smooth=FALSE, auc=TRUE, ci=FALSE,
plot=FALSE, smooth.method="binormal", ci.method=NULL, density=NULL, ...)

Arguments

x a formula (for roc.formula) or a response vector (for roc.default).
response a factor, numeric or character vector of responses, typically encoded with 0 (controls) and 1 (cases). The object. 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, containing the value of each observation. An ordered factor is coerced to a numeric.
formula a formula of the type response~predictor.
data a matrix or data.frame containing the variables in the formula. 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 precised 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.
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. “<”: if the predictor values for the control group are higher or equal than the values of the case group.
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 TRUE (default), 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.roc (if smooth=TRUE) and of="auc") must be passed as smooth.method and ci.method to avoid confusions.
density density argument passed to smooth.roc.
... 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
  • 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.

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.roc, 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.

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.

Value

If the data contained any NA value, 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 as passed in argument. 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. If NA values were removed, a na.action attribute similar to na.omit stores the row numbers.
original.predictor the predictor vector 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.
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.

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

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)

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

# 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

[Package pROC version 1.4.9 Index]