https://github.com/cran/cutpointr
Raw File
Tip revision: 7e56c827a694247d212e9a0167a119f917e1f31b authored by Christian Thiele on 31 August 2018, 15:50:10 UTC
version 0.7.4
Tip revision: 7e56c82
roc01.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/metrics.R
\name{roc01}
\alias{roc01}
\title{Calculate the distance between points on the ROC curve and (0,1)}
\usage{
roc01(tp, fp, tn, fn, ...)
}
\arguments{
\item{tp}{(numeric) number of true positives.}

\item{fp}{(numeric) number of false positives.}

\item{tn}{(numeric) number of true negatives.}

\item{fn}{(numeric) number of false negatives.}

\item{...}{for capturing additional arguments passed by method.}
}
\description{
Calculate the distance on the ROC space between points on the ROC curve
and the point of perfect discrimination
from true positives, false positives, true negatives and false negatives.
The inputs must be vectors of equal length. To be used with
\code{method = minimize_metric}. \cr
\cr
sensitivity = tp / (tp + fn) \cr
specificity = tn / (tn + fp) \cr
roc01 = sqrt((1 - sensitivity)^2 + (1 - specificity)^2) \cr
}
\examples{
roc01(10, 5, 20, 10)
roc01(c(10, 8), c(5, 7), c(20, 12), c(10, 18))
oc <- cutpointr(suicide, dsi, suicide,
  method = minimize_metric, metric = roc01)
plot_roc(oc)
}
\seealso{
Other metric functions: \code{\link{F1_score}},
  \code{\link{abs_d_ppv_npv}},
  \code{\link{abs_d_sens_spec}}, \code{\link{accuracy}},
  \code{\link{cohens_kappa}}, \code{\link{cutpoint}},
  \code{\link{false_omission_rate}},
  \code{\link{misclassification_cost}}, \code{\link{npv}},
  \code{\link{odds_ratio}}, \code{\link{p_chisquared}},
  \code{\link{plr}}, \code{\link{ppv}},
  \code{\link{precision}}, \code{\link{prod_ppv_npv}},
  \code{\link{prod_sens_spec}}, \code{\link{recall}},
  \code{\link{risk_ratio}}, \code{\link{sensitivity}},
  \code{\link{specificity}}, \code{\link{sum_ppv_npv}},
  \code{\link{sum_sens_spec}}, \code{\link{total_utility}},
  \code{\link{tpr}}, \code{\link{tp}}, \code{\link{youden}}
}
\concept{metric functions}
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