Package ROC. R topics documented: November 13, 2018
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1 Package ROC November 13, 2018 Version Title utilities for ROC, with uarray focus Author Vince Carey Henning Redestig for C++ language enhancements Maintainer Vince Carey utilities for ROC, with uarray focus Depends R (>= 1.9.0), utils, methods Suggests Biobase License Artistic-2.0 URL LazyLoad Yes biocviews DifferentialExpression git_url git_branch RELEASE_3_8 git_last_commit da15bc4 git_last_commit_date Date/Publication R topics documented: AUC plot-methods rocc-class rocdemo.sca trapezint Index 7 1
2 2 AUC AUC functionals of ROC curve various functionals of ROC (Receiver Operating Characteristic) curves Usage AUC(rocobj) AUCi(rocobj) pauc(rocobj,t0) pauci(rocobj,t0) Arguments rocobj t0 element of class rocc FPR point at which TPR is evaluated or limit in (0,1) to integrate to Details AUC, pauc, AUCi and pauci compute the Area Under the Curve. AUC and pauc employ the trapezoidal rule. AUCi and pauci use integrate(). AUC and AUCi compute the area under the curve from 0 to 1 on the x-axis (i.e., the 1 - specificity axis). pauc and pauci compute the are under the curve from 0 to argument t0 on the x-axis (i.e., the 1 - specificity axis). Elements of class rocc can be created by rocdemo.sca() or other constructors you might make using the code of rocdemo.sca() as a template. Author(s) Vince Carey (stvjc@channing.harvard.edu) References Rosner, B., 2000, Fundamentals of Biostatistics, 5th Ed., pp Duda, R. O., Hart, P. E., Stork, D. G., 2001 Pattern Classification, 2nd Ed., p. 49 See Also rocdemo.sca R1 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), dxrule.sca, print(auc(r1)) print(pauc(r1,.3)) print(pauci(r1,.3)) print(roc(r1,.3))
3 plot-methods 3 plot-methods plot method for ROC curves plot method for ROC curves Methods x = rocc plots an ROC curve object, with additional parameters available: show.thresh (logical): should marker threshold values be plotted? jit (logical): should plotted points be jittered? add (logical): increment to current plot? line (logical): plot points or lines? threshcex (numeric): if showing threshold values, set character expansion in text call to this value threshysh (numeric): if showing threshold values, add this quantity to y coordinate of curve to plot the threshold value (should be negative for printing below point) threshdig (numeric): if showing threshold values, use this as the digits parameter to round to display the threshold... extra parameters passed to base plot, lines or points as needed R1 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), dxrule.sca, plot(r1, line=true, show.thresh=true, lwd=2, threshdig=2) R2 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), dxrule.sca, plot(r2, line=true, add=true, col="green", lwd=2 ) R3 <- rocdemo.sca( rbinom(40,1,.4), rnorm(40), dxrule.sca, points(r3, col="red", pch=19) rocc-class Class rocc, ROC curve representation object representing ROC curve, typically created using rocdemo.sca
4 4 rocdemo.sca Creating Objects new('rocc', sens =..., # Object of class numeric spec =..., # Object of class numeric rule =..., # Object of class function cuts =..., # Object of class numeric markerlabel =..., # Object of class character caselabel =..., # Object of class character ) Slots sens: Object of class "numeric" sensitivity values spec: Object of class "numeric" specificity values rule: Object of class "function" rule to classify objects cuts: Object of class "numeric" thresholds defining curve markerlabel: Object of class "character" name of measured marker caselabel: Object of class "character" name of condition Methods plot (rocc, missing): a plotting function with some additional parameters R1 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), dxrule.sca, plot( R1, show.thresh=true ) rocdemo.sca function to build objects of class rocc rocdemo.sca demonstrate rocc class construction using a scalar marker and simple functional rule Usage rocdemo.sca(truth, data, rule=null, cutpts=na, markerlabel="unnamed marker", caselabel="unnamed diagnosis")
5 rocdemo.sca 5 Arguments truth true classification of objects. Must take values 0 or 1. data rule Details Value cutpts markerlabel caselabel quantitative markers used to classify rule: a function with arguments (x, thresh) returning 0 or 1. If no rule is provided or the standard rule dxrule.sca is passed, a faster C-based implementation is used to compute sensitivity and specificity. values of thresholds; no NA allowed, or they will be recomputed using smallest gap between data points with distinct values textual label describing marker textual label describing classification dxrule.sca is function (x, thresh) ifelse(x > thresh, 1, 0) The default value of argument cutpts is a point less than min(data), points separating the unique values of data and a point greater than max(data). an object of S4 class rocc Author(s) See Also Vince Carey (stvjc@channing.harvard.edu) AUC R1 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), plot(r1, line=true, show.thresh=true) truth <- c(0, 1, 0, 1, 1, 0, 1, 1) data <- c(2, 3, 4, 4, 5, 6, 7, 8) R2 <- rocdemo.sca(truth, data, dxrule.sca) plot(r2, line=true, show.thresh=true) R3 <- rocdemo.sca(truth, data, function(x, thresh) 1 - dxrule.sca(x, thresh)) if (AUC(R2) + AUC(R3)!= 1) stop('sum of AUCs should be 1.') # # more involved # set.seed(1234) x = runif(1000) w = runif(1000) z = rbinom(1000, 1, plogis( *x +.3*w)) m1 = glm(z~x, fam=binomial) demorule.glm.clo = function(model) function(w,thresh) ifelse(predict(model, newdata=list(x=w), type="response")>thresh, 1, 0) demorule.glm = demorule.glm.clo(m1) R4 = rocdemo.sca(z, x, demorule.glm )
6 6 trapezint plot(r4) trapezint trapezoidal rule for AUC trapezoidal rule for AUC Usage trapezint(x, y, a, b) Arguments x y a b x - abscissae y - ordinates a - lower limit of integration b - upper limit of integration Details Value uses approx estimated AUC x <- sort(runif(30)) y <- sin(x) print(trapezint(x,y,0,1))
7 Index Topic methods plot-methods, 3 rocc-class, 3 Topic models AUC, 2 rocdemo.sca, 4 trapezint, 6.initClasses (rocc-class), 3 AUC, 2 AUCi (AUC), 2 dxrule.sca (rocdemo.sca), 4 lines,rocc-method (plot-methods), 3 pauc (AUC), 2 pauci (AUC), 2 plot,rocc-method (plot-methods), 3 plot-methods, 3 points,rocc-method (plot-methods), 3 ROC (AUC), 2 rocc-class, 3 rocdemo.sca, 4 trapezint, 6 7
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