R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("ROCR-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('ROCR')
Loading required package: gplots
Loading required package: gdata
Loading required package: gtools
Loading required package: gtools

Attaching package: 'gplots'


	The following object(s) are masked from package:stats :

	 lowess 

> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "ROCR.hiv"
> 
> ### * ROCR.hiv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ROCR.hiv
> ### Title: Data set: Support vector machines and neural networks applied to
> ###   the prediction of HIV-1 coreceptor usage
> ### Aliases: ROCR.hiv
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ROCR.hiv)
> attach(ROCR.hiv)
> pred.svm <- prediction(hiv.svm$predictions, hiv.svm$labels)
> perf.svm <- performance(pred.svm, 'tpr', 'fpr')
> pred.nn <- prediction(hiv.nn$predictions, hiv.svm$labels)
> perf.nn <- performance(pred.nn, 'tpr', 'fpr')
> plot(perf.svm, lty=3, col="red",main="SVMs and NNs for prediction of
+ HIV-1 coreceptor usage")
> plot(perf.nn, lty=3, col="blue",add=TRUE)
> plot(perf.svm, avg="vertical", lwd=3, col="red",
+      spread.estimate="stderror",plotCI.lwd=2,add=TRUE)
> plot(perf.nn, avg="vertical", lwd=3, col="blue",
+ spread.estimate="stderror",plotCI.lwd=2,add=TRUE)
> legend(0.6,0.6,c('SVM','NN'),col=c('red','blue'),lwd=3)
> 
> 
> 
> cleanEx(); ..nameEx <- "ROCR.simple"
> 
> ### * ROCR.simple
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ROCR.simple
> ### Title: Data set: Simple artificial prediction data for use with ROCR
> ### Aliases: ROCR.simple
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # plot a ROC curve for a single prediction run
> # and color the curve according to cutoff.
> data(ROCR.simple)
> pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)
> perf <- performance(pred,"tpr","fpr")
> plot(perf,colorize=TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "ROCR.xval"
> 
> ### * ROCR.xval
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ROCR.xval
> ### Title: Data set: Artificial cross-validation data for use with ROCR
> ### Aliases: ROCR.xval
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # plot ROC curves for several cross-validation runs (dotted
> # in grey), overlaid by the vertical average curve and boxplots
> # showing the vertical spread around the average.
> data(ROCR.xval)
> pred <- prediction(ROCR.xval$predictions, ROCR.xval$labels)
> perf <- performance(pred,"tpr","fpr")
> plot(perf,col="grey82",lty=3)
> plot(perf,lwd=3,avg="vertical",spread.estimate="boxplot",add=TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "performance"
> 
> ### * performance
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: performance
> ### Title: Function to create performance objects
> ### Aliases: performance
> ### Keywords: classif
> 
> ### ** Examples
> 
> ## computing a simple ROC curve (x-axis: fpr, y-axis: tpr)
> library(ROCR)
> data(ROCR.simple)
> pred <- prediction( ROCR.simple$predictions, ROCR.simple$labels)
> perf <- performance(pred,"tpr","fpr")
> plot(perf)
> 
> ## precision/recall curve (x-axis: recall, y-axis: precision)
> perf1 <- performance(pred, "prec", "rec")
> plot(perf1)
> 
> ## sensitivity/specificity curve (x-axis: specificity,
> ## y-axis: sensitivity)
> perf1 <- performance(pred, "sens", "spec")
> plot(perf1)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot-methods"
> 
> ### * plot-methods
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot-methods
> ### Title: Plot method for performance objects
> ### Aliases: plot.performance plot-methods plot,performance-method
> ###   plot,performance,missing-method
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # plotting a ROC curve:
> library(ROCR)
> data(ROCR.simple)
> pred <- prediction( ROCR.simple$predictions, ROCR.simple$labels )
> perf <- performance( pred, "tpr", "fpr" )
> plot( perf )
> 
> # To entertain your children, make your plots nicer
> # using ROCR's flexible parameter passing mechanisms
> # (much cheaper than a finger painting set)
> par(bg="lightblue", mai=c(1.2,1.5,1,1))
> plot(perf, main="ROCR fingerpainting toolkit", colorize=TRUE,
+   xlab="Mary's axis", ylab="", box.lty=7, box.lwd=5,
+   box.col="gold", lwd=17, colorkey.relwidth=0.5, xaxis.cex.axis=2,
+   xaxis.col='blue', xaxis.col.axis="blue", yaxis.col='green', yaxis.cex.axis=2,
+   yaxis.at=c(0,0.5,0.8,0.85,0.9,1), yaxis.las=1, xaxis.lwd=2, yaxis.lwd=3,
+   yaxis.col.axis="orange", cex.lab=2, cex.main=2)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "prediction"
> 
> ### * prediction
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: prediction
> ### Title: Function to create prediction objects
> ### Aliases: prediction
> ### Keywords: classif
> 
> ### ** Examples
> 
> # create a simple prediction object
> library(ROCR)
> data(ROCR.simple)
> pred <- prediction(ROCR.simple$predictions,ROCR.simple$labels)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  3.06 0.11 3.18 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')