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	Receiver operating characteristic - Wikipedia, the free encyclopedia
		The ROC can also be represented equivalently by plotting the fraction of true positives (TP) vs. the fraction of true negatives (TN).
		ROC curves are used to evaluate the results of a prediction and were first employed in the study of discriminator systems for the detection of radio signals in the presence of noise in the 1940s, following the attack on Pearl Harbor.
		In engineering, the area between the ROC curve and the no-discrimination line is often preferred, because of its useful mathematical properties as a non-parametric statistic.
	en.wikipedia.org /wiki/Receiver_operating_characteristic (657 words)

	ROC Creation
		A given ROC curve is produced by first over-sampling the minority class to a specified degree and then under-sampling the majority class at increasing degrees to generate the successive points on the curve.
		ROC curves were also generated by varying the loss ratio in Ripper from 0.9 to 0.001 and by varying the priors of the minority class from the original distribution to up to 50 times the majority class for a Naive Bayes Classifier.
		The ROC curve shown in Figure 17 is for the minority class over-sampled at 400%.
	www.cs.cmu.edu /afs/cs/project/jair/pub/volume16/chawla02a-html/node10.html (1071 words)

	The Receiver Operating Characteristic (ROC) Curve
		Receiver Operating Characteristic (ROC) curves plot the sensitivity of a test versus its false positive rate for various points (definitely present, probably present to definitely absent) and is especially applicable when test results are interpreted subjectively.
		ROC curves were developed in the 1950's as a by-product of research into making sense of radio signals contaminated by noise.
		The area under the curve is the percentage of randomly drawn pairs for which this is true (that is, the test correctly classifies the two patients in the random pair).
	www.mlahanas.de /MOEA/Med/ROC21.htm (1831 words)

	ROC Curve
		The ROC curve refers to the Receiver Operating Characteristic.
		As the response criterions are changed, the point on the individual curve changes, not the curve itself.
		As d' gets larger, the curve bows away from the diagonal until at extreme values it is along the outer walls of graph.
	www.csulb.edu /org/hfes/roc_curve.htm (166 words)

	Korean Journal of Radiology
		Important concepts involved in the correct use and interpretation of this analysis, such as smooth and empirical ROC curves, parametric and nonparametric methods, the area under the ROC curve and its 95% confidence interval, the sensitivity at a particular FPR, and the use of a partial area under the ROC curve are discussed.
		The nonparametric estimate of the area under the empirical ROC curve is the summation of the areas of the trapezoids formed by connecting the points on the ROC curve.
		It estimates the smooth ROC curve and its AUC, 95% CI of AUC, and the parameters a and b on the basis of a binormal distribution.
	www.kjronline.org /abstract/view_articletext.asp?year=2004&page=11 (3881 words)

	Delta Omega - Honorary Society in Public Health
		The Area Under the ROC Curve (AUC) is often used as an index of how well a scoring system is able to classify patients into one of the two medical alternatives (an area of 1 is perfect, 0.5 is non-informative) such as: death or survival, disease or non-disease.
		ROC methods have also been used to evaluated models selecting biomarkers in human genetics, the decision for choosing the best screening test to identify high risk people from epidemiological populations and the prediction of length of stay, mortality and cost in intensive care.
		When the standard deviation of the area under the ROC curve was estimated empirically, the values were consistently lower than the values obtained from applying the usual nonparametric method to estimate the standard error.
	www.deltaomega.org /00wang.htm (902 words)

	RECEIVER OPERATING CHARACTERISTIC (ROC) LITERATURE RESEARCH
		ROC curve regression analysis: the use of ordinal regression models for diagnostic test assessment.
		ROC curves and measures of discrimination accuracy: A reply to Swets.
		Estimating a ROC curve in the presence of verification bias.
	splweb.bwh.harvard.edu:8000 /pages/ppl/zou/roc.html (4429 words)

	Machine Learning (Theory) » ROC vs. Accuracy vs. AROC (Site not responding. Last check: 2007-11-03)
		The ROC vs. accuracy discussion is often conflated with “is the goal classification or ranking?” because ROC curve construction requires a ranking be produced.
		Although the area under the ROC curve (AROC) is not an intuitive quantity in itself, I find that its interpretation as a Wilcoxon-Mann-Whitney statistic, which effectively measures the fraction of positive-negative instance pairs that are ranked correctly (discussed, for example, in Corinna Cortes and Mehryar Mohri’s paper), makes the quantity easier to understand.
		One important method not yet mentioned in the present discussion is the elegant work by Provost and Fawcett on the ROC Convex Hull as an alternative to both “vanilla” ROC curves and the Area Under Curve summary.
	hunch.net /index.php?p=21 (1065 words)

	Stats: ROC curve
		An ROC curve is a graphical representation of the trade off between the false negative and false positive rates for every possible cut off.
		Equivalently, the ROC curve is the representation of the tradeoffs between sensitivity (Sn) and specificity (Sp).
		The ROC curve plots the false positive rate on the X axis and 1 - the false negative rate on the Y axis.
	www.childrensmercy.org /stats/ask/roc.asp (1623 words)

	[No title] (Site not responding. Last check: 2007-11-03)
		ROC curve of C4.5 for CRX The MATLAB functions for drawing the ROC curve and calculating the AUC value can be found in Appendix A and Appendix B. We got AUC=0.8949 for the ROC curve of Fig.
		ROC curve for MLP neural network (dataset: corral) The strategy to get the ROC curve here is simple: use different thresholds from -1 to 1, the output value greater than a threshold will reach the class 1, otherwise 0.
		The main idea of generating a ROC curve is to vary threshold on the output between its extremes, then calculate the resulting confusion matrix which contain the true positive ratio and false positive ratio to form a ROC point on the ROC space.
	www.cs.ualberta.ca /~xiaoyuan/auc_report.doc (7243 words)

	Specialty Laboratories ::: we help doctors help patients
		The ROC curve is generated by plotting the false-positive rate versus the true-positive rate of a test for different cut-off values.
		ROC curves are also valuable to choose the cutoff point of a test.
		However, ROC plots do not directly incorporate the effect of prevalence or the value of the possible test outcomes on test performance, which are two important factors in the practical utility of a diagnostic test.
	www.specialtylabs.com /books/display.asp?id=1103 (587 words)

	Department of Quantitative Health Sciences
		ROC methodology is appropriate in situations where there are 2 possible "truth states" (i.e., diseased/normal, event/non-event, or some other binary outcome), "truth" is known for each case, and "truth" is determined independently of the diagnostic tests / predictor variables / etc. under study.
		The area under the ROC curve (AUC) is commonly used as a summary measure of diagnostic accuracy.
		Nonparametric estimates of the area under the ROC curve (AUC) tend to underestimate the "smooth curve" area (i.e., parametric estimates), but this bias is negligible for continuous data.
	www.bio.ri.ccf.org /html/rocanalysis.html (2436 words)

	ROC curves (Site not responding. Last check: 2007-11-03)
		An ROC curve curve plots the cummulative proportion of old versus new items that are recognized at each level on the confidence scale.
		So to create an ROC curve the first step is to determine the proportion of old and new items where the level of confidence is at least -2, at least -1, at least +1, at least +2, and at least +3.
		The Y-intercept on the ROC curve thus represents the proportion of "old" items that would be accepted even if subjects were being as careful as they could possibly be.
	www.uark.edu /misc/lampinen/tutorials/roc.htm (469 words)

	ROC Curves Documentation (Site not responding. Last check: 2007-11-03)
		A ROC curve provides a graphical representation of the relationship between the true-positive and false-positive prediction rate of a model.
		The red line is the ROC curve for the input data, the gray line is the reference line.
		The ROC curve module uses an empirical technique to calculate the ROC curve, which does not use an assumed likelihood estimator to fit a smooth curve between the sample points.
	pops.csse.monash.edu.au /roccurves_doc.html (943 words)

	Receiver Operating Characteristic Curves
		ROC curves should be particularly valuable if we can use them to compare the performance of two tests.
		The one big catch with ROC curves is where the test and gold standard are not independent.
		Earlier literature on ROC curves often seems to have made the unfortunate assumption that the underlying distributions are normal curves.
	www.anaesthetist.com /mnm/stats/roc (8738 words)

	ROC Study
		Simulation studies indicated that both algorithms produced reliable estimates of the binormal ROC curve parameters a and b, the ROC-area index Az, and the standard errors of those estimates.
		The area under an ROC curve that has been fit by the conventional binormal model (Az) is widely used as an index of diagnostic performance.
		ML estimates of the proper and conventional binormal ROC curves are virtually identical when the conventional ROC shows no hook, but the proper binormal curves have monotonic slope for all datasets, including those for which the conventional binormal model produces degenerate fits.
	www-radiology.uchicago.edu /krl/rocstudy.htm (1184 words)

	ROC_Soft
		This program fits a binormal ROC curve to continuously distributed data (e.g., probability assessment on a 0-100 scale or numerical output from a laboratory experiment such as a blood count) by maximum-likelihood estimation.
		This program calculates the maximum-likelihood estimates of a "bivariate binormal" model for potentially correlated ordinal category data from two diagnostic tests, and thus, the binormal ROC curves implied by those data and their correlations.
		This program calculates the maximum-likelihood estimates of a "bivariate binorml" model for continuously-distributed data from two potentially correlated diagnostic tests, and thus, to estimate the binormal ROC curves implied by those data and their correlations.
	home.uchicago.edu /~junji/KRL_HP/rocold.htm (405 words)

	[No title] (Site not responding. Last check: 2007-11-03)
		Bootstrap standard errors for the binormal ROC parameters and corresponding area under the curve (AUC) estimates are obtained.
		^partial(^fp%^)^ specifies that estimates of the ROC curve parameters are to be based on a restricted range of the false positive rate, FP <= fp%.
		An interpretation for the ROC curve and inference using GLM procedures.
	www.fhcrc.org /science/labs/pepe/book/prg/dfroc.hlp (474 words)

	GraphROC: Generation of Receiver operating characteristics curves (Site not responding. Last check: 2007-11-03)
		The Receiver operating characteristics (ROC) curves can be generated simply by selecting Graph ROC curve when the two distributions display window is active.
		The non-parametric ROC curve is generated using all possible cutoff limits.
		In addition to that the ROC curves for MCH (erythrocyte mean corpuscular hemoglobin) and Eryt (erythrocyte count) are shown as obtained from the same set of samples.
	members.tripod.com /refstat/kuva6.htm (113 words)

	Signal Detection Theory (Site not responding. Last check: 2007-11-03)
		The Receiver Operating Characteristic (ROC) curve in the middle of the applet shows the relationship between a Hit rate (H) and a False Alarm rate (FA) for all possible Criterion values, for a respondent with one specific value of d'.
		The location of the point on the ROC curve is associated with the Criterion value.
		The ROC curve will be a straight line going from the bottom left to the top right corner.
	wise.cgu.edu /sdt/sdt.html (679 words)

	ROC Curve Code
		We are releasing our source code for generating ROC curves and variants for recommender system applications.
		ROC curves are similar to the precision/recall curves used in information retrieval as well as lift curves used in marketing communities.
		An empirical ROC curve is a curve plotted using the empirical distribution of the data as opposed to using a smoothing function.
	www.cis.upenn.edu /group/datamining/software_dist/ROCtools (492 words)

	SAS Sample 521 : Plot ROC curve with labelled points for a binary-response model
		The receiver operating characteristic (ROC) curve is a diagnostic tool for assessing the ability of a logistic model to discriminate between events and nonevents.
		Each point on the ROC curve provides the sensitivity and specificity measures associated with a cutpoint in the probability scale which allows classification of each observation as either a predicted event or a predicted nonevent.
		The %ROC macro performs nonparametric comparison of areas under correlated ROC curves.
	support.sas.com /ctx/samples/index.jsp?sid=521 (1048 words)

	ROC curves
		An ROC curve is simply a graph of sensitivity vs (1-specificity).
		The area under the ROC curve (AUROCC) is a reflection of how good the test is at distinguishing (or "discriminating") between patients with and without iron deficiency anemia.
		There are no hard and fast rules, but in general an ROC of 0.5 to 0.7 is associated with marginally useful tests, an area of 0.7 to 0.9 with a good test, and an area greater than 0.9 with an excellent test.
	www.poems.msu.edu /EBM/Diagnosis/ROC.htm (880 words)

	ACOMED - statistics, ROC curve / ROC curves / DAC method
		The ROC curve (receiver operating characteristics) is one of the main approaches.
		However, because of some disadvantages of the ROC analysis we have developed a new method called discordance analysis characteristics (DAC method) which is submitted for publishing.
		A german version of a page is available which provides links to some statistical software packages calculating ROC curves.
	www.acomed-statistik.de /roc-curve.html (120 words)

	[No title] (Site not responding. Last check: 2007-11-03)
		Although theories about ROC curves have been established and computation methods and computer software are available for cross-sectional design, limited research for estimating ROC curves and their summary statistics has been done for repeated measure designs, which are useful in many applications, such as biological, medical and health services research.
		The area under a ROC curve is calculated through the Wilcoxon non-parametric approach by comparing the predicted probability of all discordant pairs of observations.
		The ROC curve is constructed by plotting a series of pairs of true positive rate (sensitivity) and false positive rate (1-specificity) calculated from varying cuts of positivity escalated by increments of 0.005 in predicted probability.
	www.jstatsoft.org /v08/i12/Abstract.txt (263 words)

	Stata help for roccomp
		Plot 1, Plot 2, Plot 3 plot#opts(plot_options) affect rendition of the #th ROC curve; plot 1 is the gold standard
		hanley specifies that the standard error for the area under the ROC curve be calculated using the method suggested by Hanley and McNeil.
		binormal specifies that the areas under the ROC curves to be compared should be estimated using the binormal distribution assumption.
	www.stata.com /help.cgi?roccomp (1156 words)

	ACOMED - statistik, ROC-Kurve / ROC-Kurven
		Although commercial statistical software is used in my company usually, it is advantegeous to use MS Excel-tools in some cases, for instance if free layout of the figures is required.
		Free demo versions with restricted numbers of cases are available (single ROC curve, comparison of two ROC curves).
		However, 3 MS Excel tools related to ROC curves and DAC-method have been developed.
	www.acomed-statistik.de /roc-tools_engl.html (258 words)

	Example 42.7: ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits
		Since the experimental ODS GRAPHICS statement is specified, a graphical display of the ROC curve is produced as shown in Output 42.7.3.
		In this example, the area under the ROC curve is 0.953.
		The ROC curve may also be displayed with the GPLOT procedure by using the following code.
	support.sas.com /91doc/getDoc/statug.hlp/logistic_sect60.htm (486 words)

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