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Open AccessJournal ArticleDOI

pROC: an open-source package for R and S+ to analyze and compare ROC curves

TLDR
pROC as mentioned in this paper is a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.
Abstract
Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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Journal ArticleDOI

Optimal high b-value for diffusion weighted MRI in diagnosing high risk prostate cancers in the peripheral zone.

TL;DR: To retrospectively determine the optimal b‐value(s) of diffusion‐weighted imaging (DWI) associated with intermediate–high risk cancer in the peripheral zone (PZ) of the prostate, diffusion‐ Weighted imaging is used as a surrogate for PZ lesion status.
Journal ArticleDOI

Improving multivariable prostate cancer risk assessment using the Prostate Health Index.

TL;DR: To analyse the clinical utility of a prediction model incorporating both clinical information and a novel biomarker, p2PSA, in order to inform the decision for prostate biopsy in an Irish cohort of men referred for prostate cancer assessment.
Journal ArticleDOI

Linking Environmental DNA and RNA for Improved Detection of the Marine Invasive Fanworm Sabella spallanzanii

TL;DR: In this article, DNA and RNA were co-extracted from settlement plates and water samples collected in the Auckland harbor, New Zealand, and analyzed using a Sabella spallanzanii specific droplet digital PCR assay combined with metazoan communities (Cytochrome c oxidase subunit I).
Journal ArticleDOI

Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario.

TL;DR: Simultaneously considering the whole complexity of bio-ecological adaptations within biotic assemblages subjected to human pressures provides a functional diagnostic tool both (i) ecologically relevant and (ii) efficient for ERA.
Journal ArticleDOI

Validated automatic speech biomarkers in primary progressive aphasia.

TL;DR: To automatically extract and quantify specific disease biomarkers of prosody from the acoustic properties of speech in patients with primary progressive aphasia.
References
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BookDOI

Modern Applied Statistics with S

TL;DR: A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.
Journal ArticleDOI

An introduction to ROC analysis

TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
Journal ArticleDOI

Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

TL;DR: A nonparametric approach to the analysis of areas under correlated ROC curves is presented, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
Journal ArticleDOI

A method of comparing the areas under receiver operating characteristic curves derived from the same cases.

James A. Hanley, +1 more
- 01 Sep 1983 - 
TL;DR: This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data.

Modern Applied Statistics With S

TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
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What is proc autoreg in sas?

The provided paper does not mention anything about PROC AUTOREG in SAS.