Package 'pROC ' - cranr - R Project


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PDF pROC: Display and Analyze ROC Curves

“pROC: an open-source package for R and S+ to analyze and compare ROC curves CRAN packages plyr MASS and logcondens employed in this package Examples

PDF reportROC: An Easy Way to Report ROC Analysis

pROC: an open-source package for R and S+ to analyze and compare ROC curves BMC Bioinformatics 7 77 doi: 10 1186/ · 147121051277 Examples

PDF reproducerpdf

18 oct 2023 · Description Includes data analysis and meta-analysis functions (e g to calculate effect sizes and 95 Confidence Intervals (CI) on 

PDF PRROC: Precision-Recall and ROC Curves for Weighted and

Description Computes the areas under the precision-recall (PR) and ROC curve for weighted (e g soft- labeled) and unweighted data In contrast to other 

PDF ROCpsychpdf

17 août 2020 · October 12 2022 Type Package Title Compute and Compare Diagnostic Test Statistics Across Groups Version 1 3 Date 2020-08-03

PDF Package interactions

Description A suite of functions for conducting and interpreting analysis of statistical interaction in regression models that was formerly part of the 'jtools 

PDF asbiopdf

20 août 2023 · Description Contains functions from: Aho K (2014) Foundational and Applied Statistics for Biolo- gists using R CRC/Taylor and Francis Boca 

PDF CRAN

This directory contains packages of functions which are themselves structured in directories The package named base is in a way the core of R and contains the 

  • Is pROC an open source package for R and s to analyze and compare ROC curves?

    Conclusions: 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.

  • What is the optimal threshold in pROC?

    The optimal threshold is the point closest to the top-left part of the plot with perfect sensitivity or specificity.

  • What package is pROC in R?

    (2011) “pROC: an open-source package for R and S+ to analyze and compare ROC curves”.

  • 4), the shape of the ROC curve became a sigmoidal-shaped (Fig. 5) and centering of this predictor by its mean also substantially improved the AUROC (0.495 vs 0.916).
    An inverted U-shaped relationship between an arbitrary created predictor and its outcome.

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