pROC (version 1.16.2)

coords: Coordinates of a ROC curve

Description

This function returns the coordinates of the ROC curve at the specified point.

Usage

coords(...)
# S3 method for roc
coords(roc, x, input=c("threshold", "specificity",
"sensitivity"), ret=c("threshold", "specificity", "sensitivity"),
as.list=FALSE, drop=TRUE, best.method=c("youden", "closest.topleft"),
best.weights=c(1, 0.5), transpose = FALSE, as.matrix=FALSE, ...)
# S3 method for smooth.roc
coords(smooth.roc, x, input=c("specificity",
"sensitivity"), ret=c("specificity", "sensitivity"), as.list=FALSE,
drop=TRUE, best.method=c("youden", "closest.topleft"), 
best.weights=c(1, 0.5), transpose = FALSE, as.matrix=FALSE, ...)

Arguments

roc, smooth.roc

a “roc” object from the roc function, or a “smooth.roc” object from the smooth function.

x

the coordinates to look for. Numeric (if so, their meaning is defined by the input argument) or one of “all” (all the points of the ROC curve), “local maximas” (the local maximas of the ROC curve) or “best” (see best.method argument). If missing or NULL, defaults to “all”.

input

If x is numeric, the kind of input coordinate (x). One of “threshold”, “specificity” or “sensitivity”. Can be shortenend (for example to “thr”, “sens” and “spec”, or even to “t”, “se” and “sp”). Note that “threshold” is not allowed in coords.smooth.roc, and that the argument is ignored when x is a character.

ret

The coordinates to return. See "Valid ret arguments" section below. Alternatively, the single value “all” can be used to return every coordinate available.

as.list

DEPRECATED. If the returned object must be a list. Will be removed in a future version.

drop

If TRUE the result is coerced to the lowest possible dimension, as per Extract. By default only drops if transpose = TRUE and either ret or x is of length 1.

best.method

if x="best", the method to determine the best threshold. See details in the ‘Best thresholds’ section.

best.weights

if x="best", the weights to determine the best threshold. See details in the ‘Best thresholds’ section.

transpose

whether to return the thresholds in columns (TRUE) or rows (FALSE). Since pROC 1.16 the default value is FALSE. See coords_transpose for more details the change.

as.matrix

if transpose is FALSE, whether to return a matrix (TRUE) or a data.frame (FALSE, the default). A data.frame is more convenient and flexible to use, but incurs a slight speed penalty. Consider setting this argument to TRUE if you are calling the function repeatedly.

…

further arguments passed from other methods. Ignored.

Value

Depending on the length of x and as.list argument.

	length(x) == 1 or length(ret) == 1	length(x) > 1 or length(ret) > 1 or drop == FALSE
as.list=TRUE	a list of the length of, in the order of, and named after, ret.	a list of the length of, and named after, x. Each element of this list is a list of the length of, in the order of, and named after, ret.
as.list=FALSE	a numeric vector of the length of, in the order of, and named after, ret (if length(x) == 1) or a numeric vector of the length of, in the order of, and named after, x (if length(ret) == 1.	a numeric matrix with one row for each ret and one column for each x

In all cases if input="specificity" or input="sensitivity" and interpolation was required, threshold is returned as NA.

Note that if giving a character as x (“all”, “local maximas” or “best”), you cannot predict the dimension of the return value unless drop=FALSE. Even “best” may return more than one value (for example if the ROC curve is below the identity line, both extreme points).

coords may also return NULL when there a partial area is defined but no point of the ROC curve falls within the region.

Details

This function takes a “roc” or “smooth.roc” object as first argument, on which the coordinates will be determined. The coordinates are defined by the x and input arguments. “threshold” coordinates cannot be determined in a smoothed ROC.

If input="threshold", the coordinates for the threshold are reported, even if the exact threshold do not define the ROC curve. The following convenience characters are allowed: “all”, “local maximas” and “best”. They will return all the thresholds, only the thresholds defining local maximas (upper angles of the ROC curve), or only the threshold(s) corresponding to the best sum of sensitivity + specificity respectively. Note that “best” can return more than one threshold. If x is a character, the coordinates are limited to the thresholds within the partial AUC if it has been defined, and not necessarily to the whole curve.

For input="specificity" and input="sensitivity", the function checks if the specificity or sensitivity is one of the points of the ROC curve (in roc$sensitivities or roc$specificities). More than one point may match (in step curves), then only the upper-left-most point coordinates are returned. Otherwise, the specificity and specificity of the point is interpolated and NA is returned as threshold.

The coords function in this package is a generic, but it might be superseded by functions in other packages such as colorspace or spatstat if they are loaded after pROC. In this case, call the pROC::coords explicitly.

Best thresholds

If x="best", the best.method argument controls how the optimal threshold is determined.

“youden”

Youden's J statistic (Youden, 1950) is employed. The optimal cut-off is the threshold that maximizes the distance to the identity (diagonal) line. Can be shortened to “y”.

The optimality criterion is: $$max(sensitivities + specificities)$$

“closest.topleft”

The optimal threshold is the point closest to the top-left part of the plot with perfect sensitivity or specificity. Can be shortened to “c” or “t”.

The optimality criterion is: $$min((1 - sensitivities)^2 + (1- specificities)^2)$$

In addition, weights can be supplied if false positive and false negative predictions are not equivalent: a numeric vector of length 2 to the best.weights argument. The elements define

1. the relative cost of of a false negative classification (as compared with a false positive classification)

2. the prevalence, or the proportion of cases in the population (\(\frac{n_{cases}}{n_{controls}+n_{cases}}\)).

The optimality criteria are modified as proposed by Perkins and Schisterman:

“youden”

$$max(sensitivities + r * specificities)$$

“closest.topleft”

$$min((1 - sensitivities)^2 + r * (1- specificities)^2)$$

with

$$r = \frac{1 - prevalence}{cost * prevalence}$$

By default, prevalence is 0.5 and cost is 1 so that no weight is applied in effect.

Note that several thresholds might be equally optimal.

Valid ret arguments

The following table lists valid ret arguments.

Value	Description	Formula	Synonyms
threshold	The threshold value	-	-
tn	True negative count	-	-
tp	True positive count	-	-
fn	False negative count	-	-
fp	False positive count	-	-
specificity	Specificity	tn / (tn + fp)	tnr
sensitivity	Sensitivity	tp / (tp + fn)	recall, tpr
accuracy	Accuracy	(tp + tn) / N	-
npv	Negative Predictive Value	tn / (tn + fn)	-
ppv	Positive Predictive Value	tp / (tp + fp)	precision
precision	Precision	tp / (tp + fp)	ppv
recall	Recall	tp / (tp + fn)	sensitivity, tpr
tpr	True Positive Rate	tp / (tp + fn)	sensitivity, recall
fpr	False Positive Rate	fp / (tn + fp)	1-specificity
tnr	True Negative Rate	tn / (tn + fp)	specificity
fnr	False Negative Rate	fn / (tp + fn)	1-sensitivity
fdr	False Discovery Rate	fp / (tp + fp)	1-ppv
youden	Youden Index	se + r * sp	-
closest.topleft	Distance to the top left corner of the ROC space	- ((1 - se)^2 + r * (1 - sp)^2)	-

The value “threshold” is not allowed in coords.smooth.roc.

Values can be shortenend (for example to “thr”, “sens” and “spec”, or even to “se”, “sp” or “1-np”). In addition, some values can be prefixed with 1- to get their complement: 1-specificity, 1-sensitivity, 1-accuracy, 1-npv, 1-ppv (but they cannot be shortened).

The values npe and ppe are automatically replaced with 1-npv and 1-ppv, respectively (and will therefore not appear as is in the output, but as 1-npv and 1-ppv instead). These must be used verbatim in ROC curves with percent=TRUE (ie. “100-ppv” is never accepted).

The “youden” and “closest.topleft” are weighted with r, according to the value of the best.weights argument. See the "Best thresholds" section above for more details.

The single value “all” can be used to return every coordinate available.

References

Neil J. Perkins, Enrique F. Schisterman (2006) ``The Inconsistency of "Optimal" Cutpoints Obtained using Two Criteria based on the Receiver Operating Characteristic Curve''. American Journal of Epidemiology 163(7), 670--675. DOI: 10.1093/aje/kwj063.

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.

W. J. Youden (1950) ``Index for rating diagnostic tests''. Cancer, 3, 32--35. DOI: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3.

See Also

roc, ci.coords

Examples

# NOT RUN {
# Create a ROC curve:
data(aSAH)
roc.s100b <- roc(aSAH$outcome, aSAH$s100b, percent = TRUE)

# Get the coordinates of S100B threshold 0.55
coords(roc.s100b, 0.55, transpose = FALSE)

# Get the coordinates at 50% sensitivity
coords(roc=roc.s100b, x=50, input="sensitivity", transpose = FALSE)
# Can be abbreviated:
coords(roc.s100b, 50, "se", transpose = FALSE)

# Works with smoothed ROC curves
coords(smooth(roc.s100b), 90, "specificity", transpose = FALSE)

# Get the sensitivities for all thresholds
cc <- coords(roc.s100b, "all", ret="sensitivity", transpose = FALSE)
print(cc$sensitivity)

# Get the best threshold
coords(roc.s100b, "best", ret="threshold", transpose = FALSE)

# Get the best threshold according to different methods
roc.ndka <- roc(aSAH$outcome, aSAH$ndka, percent=TRUE)
coords(roc.ndka, "best", ret="threshold", transpose = FALSE, 
       best.method="youden") # default
coords(roc.ndka, "best", ret="threshold", transpose = FALSE, 
       best.method="closest.topleft")

# and with different weights
coords(roc.ndka, "best", ret="threshold", transpose = FALSE, 
       best.method="youden", best.weights=c(50, 0.2))
coords(roc.ndka, "best", ret="threshold", transpose = FALSE, 
       best.method="closest.topleft", best.weights=c(5, 0.2))
       
# This is available with the plot.roc function too:
plot(roc.ndka, print.thres="best", print.thres.best.method="youden",
                                 print.thres.best.weights=c(50, 0.2)) 

# Return more values:
coords(roc.s100b, "best", ret=c("threshold", "specificity", "sensitivity", "accuracy",
                           "precision", "recall"), transpose = FALSE)

# Return all values
coords(roc.s100b, "best", ret = "all", transpose = FALSE)
                           
# You can use coords to plot for instance a sensitivity + specificity vs. cut-off diagram
plot(specificity + sensitivity ~ threshold, 
     coords(roc.ndka, "all", transpose = FALSE), 
     type = "l", log="x", 
     subset = is.finite(threshold))

# Plot the Precision-Recall curve
plot(precision ~ recall, 
     coords(roc.ndka, "all", ret = c("recall", "precision"), transpose = FALSE),
     type="l", ylim = c(0, 100))

# Alternatively plot the curve with TPR and FPR instead of SE/SP 
# (identical curve, only the axis change)
plot(tpr ~ fpr, 
     coords(roc.ndka, "all", ret = c("tpr", "fpr"), transpose = FALSE),
     type="l")
# }