ROCx

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), 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 (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

Kaspar Rufibach

logcondens

Estimate a Log-Concave Probability Density from Iid Observations

ROCx function

<dl> <dt>x</dt>
<dd>Vector of numbers in \([0, 1]\) where the ROC curve should be computed at.</dd> <dt>res0</dt>
<dd><code>dlc</code> object as a result of a call to <code>logConDens</code> for the data of the controls.</dd> <dt>res1</dt>
<dd><code>dlc</code> object as a result of a call to <code>logConDens</code> for the data of the cases.</dd> <dt>smooth</dt>
<dd>Logical. If <code>TRUE</code> kernel smoothed log-concave estimate is used.</dd></dl>

Arguments

Kaspar Rufibach, <a href="/link/kaspar.rufibach%40gmail.com?package=logcondens&version=2.1.8" data-mini-rdoc="logcondens::kaspar.rufibach@gmail.com">kaspar.rufibach@gmail.com</a>, <a href="http://www.kasparrufibach.ch">http://www.kasparrufibach.ch</a>

Author

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

<dl>

 <dt>x</dt>
<dd>Vector of numbers in \([0, 1]\) where the ROC curve should be computed at.</dd>

 <dt>res0</dt>
<dd><code>dlc</code> object as a result of a call to <code>logConDens</code> for the data of the controls.</dd>

 <dt>res1</dt>
<dd><code>dlc</code> object as a result of a call to <code>logConDens</code> for the data of the cases.</dd>

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

</dl>

Kaspar Rufibach, <a href='mailto:kaspar.rufibach@gmail.com'>kaspar.rufibach@gmail.com</a>, <a href='http://www.kasparrufibach.ch'>http://www.kasparrufibach.ch</a>

State of Data and AI Literacy Report 2025

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

Description

Usage

Value

Arguments

Author

References

See Also