ROCx

Vector of numbers in $[0, 1]$ where the ROC curve should be computed at.

<code>dlc</code> object as a result of a call to <code><a href="/link/logConDens?package=logcondens&version=2.0.6" rd-options="" data-mini-rdoc="logcondens::logConDens">logConDens</a></code> for the data of the controls.

res0

<code>dlc</code> object as a result of a call to <code><a href="/link/logConDens?package=logcondens&version=2.0.6" rd-options="" data-mini-rdoc="logcondens::logConDens">logConDens</a></code> for the data of the cases.

res1

Logical. If <code>TRUE</code> kernel smoothed log-concave estimate is used.

smooth

Computes the value of the ROC curve at $x$ (which may be a vector) based on log-concave density estimates of the constituent distributions.

nonparametric

Given independent and identically distributed observations X(1), ..., X(n), this package allows to compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

ROCx: Compute ROC curve at a given x based on log-concave estimates for the constituent distributions

Description

Usage

Arguments

Value

References

See Also