2005
DOI: 10.1093/bioinformatics/bti623
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ROCR: visualizing classifier performance in R

Abstract: tobias.sing@mpi-sb.mpg.de.

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Cited by 2,877 publications
(2,227 citation statements)
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“…Third, diseases were grouped into two categories of relatively even size (see above). Models were built using the R packages caret (Kuhn, 2008) and randomForest, and ROC curves were generated with ROCR (Sing et al , 2005). …”
Section: Methodsmentioning
confidence: 99%
“…Third, diseases were grouped into two categories of relatively even size (see above). Models were built using the R packages caret (Kuhn, 2008) and randomForest, and ROC curves were generated with ROCR (Sing et al , 2005). …”
Section: Methodsmentioning
confidence: 99%
“…The minimal variant frequency was set to 1/10,000 (VarScan) and the poolsize was set to 10,000 (CRISP, vipR). ROC curves and the area under the ROC curve were computed for each variant frequency with the R package ROCR 32 . See Supplementary Methods for a detailed description of the chosen options.…”
Section: Methodsmentioning
confidence: 99%
“…ArcGIS 10.3 was used to generate validation datasets, and Python was used for image processing (ESRI, 2014). Modelling and statistical analysis were conducted in R using the following packages: ggplot2 (Wickham, 2009), dplyr (Wickham and Francois, 2015), rasterVis (Perpinan and Hijmans, 2016), randomForest (Liaw and Wiener, 2002), reshape2 (Wickham, 2007), ROCR (Sing et al, 2005), SDMTools (VanDerWal et al, 2014), and spatial.tools (Greenberg, 2014).…”
Section: Analysis Toolsmentioning
confidence: 99%