coords  R Documentation 
This function returns the coordinates of the ROC curve at one or several specified point(s).
coords(...)
## S3 method for class 'roc'
coords(roc, x, input="threshold", 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 class 'smooth.roc'
coords(smooth.roc, x, input, 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, ...)
roc, smooth.roc 
a “roc” object from the

x 
the coordinates to look for. Numeric (if so, their meaning is
defined by the 
input 
If 
ret 
The coordinates to return. See “Available coordinates” 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 
best.method 
if 
best.weights 
if 
transpose 
whether
to return the thresholds in columns ( 
as.matrix 
if 
... 
further arguments passed from other methods. Ignored. 
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 upperleftmost 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.
If x="best"
, the best.method
argument controls how the
optimal threshold is determined.
Youden's J statistic (Youden, 1950) is employed (default). The optimal cutoff is the threshold that maximizes the distance to the identity (diagonal) line. Can be shortened to “y”.
The optimality criterion is:
max(sensitivities + specificities)
The optimal threshold is the point closest to the topleft 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
the relative cost of of a false negative classification (as compared with a false positive classification)
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:
max(sensitivities + r * specificities)
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.
The following table lists the coordinates that are available in the ret
and input
arguments.
Value  Description  Formula  Synonyms  Valid input 
threshold  The threshold value      Yes 
tn  True negative count      Yes 
tp  True positive count      Yes 
fn  False negative count      Yes 
fp  False positive count      Yes 
specificity  Specificity  tn / (tn + fp)  tnr  Yes 
sensitivity  Sensitivity  tp / (tp + fn)  recall, tpr  Yes 
accuracy  Accuracy  (tp + tn) / N    No 
npv  Negative Predictive Value  tn / (tn + fn)    No 
ppv  Positive Predictive Value  tp / (tp + fp)  precision  No 
precision  Precision  tp / (tp + fp)  ppv  No 
recall  Recall  tp / (tp + fn)  sensitivity, tpr  Yes 
tpr  True Positive Rate  tp / (tp + fn)  sensitivity, recall  Yes 
fpr  False Positive Rate  fp / (tn + fp)  1specificity  Yes 
tnr  True Negative Rate  tn / (tn + fp)  specificity  Yes 
fnr  False Negative Rate  fn / (tp + fn)  1sensitivity  Yes 
fdr  False Discovery Rate  fp / (tp + fp)  1ppv  No 
youden  Youden Index  se + r * sp    No 
closest.topleft  Distance to the top left corner of the ROC space   ((1  se)^2 + r * (1  sp)^2)    No 
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 “1np”). In addition, some values can be prefixed with
1
to get their complement:
1specificity
, 1sensitivity
, 1accuracy
, 1npv
, 1ppv
.
The values npe
and ppe
are automatically replaced with
1npv
and 1ppv
, respectively (and will therefore not appear
as is in the output, but as 1npv
and 1ppv
instead).
These must be used verbatim in ROC curves with percent=TRUE
(ie. “100ppv” 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.
For ret
, the single value “all” can be used to return
every coordinate available.
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.
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: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/aje/kwj063")}.
Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an opensource package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/147121051277")}.
W. J. Youden (1950) “Index for rating diagnostic tests”. Cancer, 3, 32–35. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/10970142(1950)3:1<32::AIDCNCR2820030106>3.0.CO;23")}.
roc
, ci.coords
# 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. cutoff diagram
plot(specificity + sensitivity ~ threshold,
coords(roc.ndka, "all", transpose = FALSE),
type = "l", log="x",
subset = is.finite(threshold))
# Plot the PrecisionRecall 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")
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