* http://www.stata.com/support/statalist/faq The best cut-off has the highest true positive rate together with the lowest false positive rate. > rocfit x3 y3 > rocplot, confband name(rocfit1) nodraw 1B Manresa Road twoway (connect line1 line2, mcolor(black) xlabel(0(0.1)1) ylabel(0(0.1)1 , angle(horizontal))) /* gen line2 = line1 > lines. Email: r.newson@imperial.ac.uk Re: st: Putting multiple ROC curves on a single graph An alternative possibility to -roccomp- is to use the -senspec- package, downloadable from SSC, to calcutate sensitivities and specificities, which can then be graphed using the -graph twoway- command. Plotting multiple measures ROC is available in many softwares SPSS, Stata Medcalc. 1 Answer. As long as the classifier outputs posterior probability estimates you can compute a ROC-curve by . * http://www.ats.ucla.edu/stat/stata/, http://www.stata.com/support/statalist/faq, st: SV: Combining ROC curves in one graph. Step 6 - Model Diagnostics. > graph combine. . You can use -rocreg- to fit several models, create the new variables Dear list I have plotted three ROC curves using stata . Correlated data Example 1 Harrells c or Somers D. The Stata Journal 2010; 10(3): 339358. > . In Stata, there are variety of commands available: roccomp Tests of equality of ROC areas rocgold Tests of equality of ROC areas against a standard ROC curve rocfit Parametric ROC. Step 1 - Load the necessary libraries. execution of rocreg creates. I wanted the ROC curves from each of these models in one graph and did the following: We have no bibliographic references for this item. We illustrate this using the auto data distributed with Stata 7.0. To do this we simply modify the line generating the probability vector pr to. Fri, 19 Aug 2011 16:25:39 -0500 * It is possible to do this using the logistic linear predictors and the roccomp command.Here is an example: You can use the add = TRUE argument the plot function to plot multiple ROC curves. Besides logistic and probit models, several other types of two-class classifiers can be evaluated using a ROC-curve. single status variable, with one or more classification variables. Kim mlogitroc generates multiclass ROC curves for classification accuracy based on multinomial logistic regression using mlogit. Web page: http://www.imperial.ac.uk/nhli/r.newson/ disease-status variable, -ndisease-, which zeroes the true status estimates of the false-positive rate and ROC value. * http://www.stata.com/help.cgi?search Use this tab to perform ROC curve analysis. You can browse but not post. together with -lincom-, which calculates confidence intervals (as well On 28/02/2011 15:36, Barth Riley wrote: > Plot multiple different coloured ROC curves using ROCR. Step 4: Create the ROC Curve. How to plot a ROC curve using ROCR package in r, *with only a classification contingency table* 8 R get AUC and plot multiple ROC curves together at the same time > P.S. base package which works. List of 15 $ percent : logi FALSE $ sensitivities : num [1:26455] 1 1 1 1 1 1 . * http://www.stata.com/support/statalist/faq The sensitivity and specificity methods test if the sensitivity (respectively specificity) of the ROC curves are different at the given level . Re: st: Combining ROC curves in one graph . You can do your three regressions and then do out-of-sample predictions saving those results in new variables. Apr 1, 2015 #4. ROC curve where the predictor is independent of outcome * http://www.stata.com/support/statalist/faq multiple logistic regression models) on a singlegraph? Next, B=100 mlogit runs are made again, but this time after shuffling class labels of all records prior to modeling, which results in B=100 realizations of null accuracy. The disease status need not be coded as 0/1, but if it is not, stat_roc assumes (with a warning) that the lowest value in sort order signifies disease-free status. Smoothed probability distributions are obtained for the B=100 alternative and null accuracy values using kernel density estimation (KDE, Gaussian kernel) to obtain 100 smoothed realizations for alternative and null accuracy. Dear Stata-users, Step 3- Create train and test dataset. Imperial College London To create the ROC curve, we'll highlight every value in the range F3:G14. From Kim Peters also wrote: @adibender " ROCR ROC " ?plot.performance . Use the following command to fit the logistic regression model: logit low age smoke Step 3: Create the ROC curve. empirical estimate of the ROC curve, We use the -connect(J)- option Date I hope this helps. http://fmwww.bc.edu/repec/bocode/m/mlogitroc.ado, http://fmwww.bc.edu/repec/bocode/m/mlogitroc.sthlp, http://fmwww.bc.edu/repec/bocode/m/mlogitroc.png, MLOGITROC: Stata module to calculate multiclass ROC Curves and AUC from Multinomial Logistic Regression, https://edirc.repec.org/data/debocus.html. Kim Oren Gradel In gure 1, observe that when the proportion of cases varies across centers (scenario 1), the pooled ROC curve for Y * For searches and help try: Now we use -rocreg- to estimate a nonparametric ROC curve of -disease- Tel: +44 (0)20 7352 8121 ext 3381 lsens, gensens(sens3) genspec(spec3) replace nodraw If you want to compute the AUC for the combination of two variables, you can include both as predictors in a logistic regression model and compute the AUC using the predictions from the model. The plot of the curves offers an excellent visual comparison of the models' performances, and the area under the curve table gives you the numbers to back up your conclusions from the plot. lsens, gensens(sens4) genspec(spec4) replace nodraw Date > rocfit x2 y2 Now we run -rocreg- on the -ndisease- status variable. Model 1 has the outcome variable + X variables, Model 2 = Model 1 + var1, Model 3 = Model 1 + var2, and Model 4 = Model 1 + var1 + var2. > roctab x1 y1, graph name(rocgraph1) nodraw Centre for National Clinical Databases, South : */ * http://www.ats.ucla.edu/stat/stata/, mailto:owner-statalist@hsphsun2.harvard.edu, http://www.stata.com/support/statalist/faq, Re: st: Combining ROC curves in one graph, st: Gen Randomized geo coordinates within a range. variable, -disease-, with probability 2/10. ROC analysis is used in clinical epidemiology to quantify how accurately medical diagnostic tests (or systems) can discriminate between two patient states, typically referred to as "diseased" and "nondiseased" ( 16, 17, 21, 22 ). However, these methods are usually . */ (scatter sens4 spec_4 , msymbol(p) mcolor(blue)) Good luck Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org. National Heart and Lung Institute Thanks in advance We can create the ROC curve for the model using the following command: lroc First, let's establish that in binary classification, there are four possible outcomes for a test prediction: true . quietly rocreg disease rating, nobootstrap Dear R Studio Community, I am trying to plot 2 ROC curves in one graph to nicely compare them. I have had the same problem, or at least a similar one, and I suggest a solution here: The receiver operating characteristic (ROC) curve is frequently used for evaluating the performance of binary classification algorithms. rocplot, confband name(rocfit3) nodraw easyROC supports both parametric and nonparametric approximations for ROC curve analysis. If you're not familiar with ROC curves, they can take some effort to understand. multiple fitted models are discussed in Newson (2010). Moreover, I would like to do the same thing after rocfit and rocplot: generate ndisease = disease For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). rocplot, confband name(rocfit2) nodraw roctab x1 y1, graph name(rocgraph1) nodraw roctab x2 y2, graph name(rocgraph2) nodraw roctab x3 y3, graph name(rocgraph3) nodraw Barth Tel: +45 65 41 17 03 * http://www.stata.com/support/statalist/faq Sendt: 19. august 2011 09:59 Apr 1, 2015 #4. It is the pooling of ROC curves that is making me scratch my head! An example of an ROC curve from logistic regression is shown below. and classification variable -rating-. Both parametric and nonparametric methods have been developed to estimate and compare the AUCs. . -----Oprindelig meddelelse----- from SSC to calculate the ROC areas (using the option -transf(c)-), [Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index] You can automatically draw ROC curves for different classifiers using the same status variable. /* Model 3: */ From [Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index] Step 4 -Create a model for logistics using the training dataset. This allows to link your profile to this item. * http://www.stata.com/help.cgi?search http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/ [Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index] If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. Thank you for your advice. rename _roc_rating roc_ndisease It is a model of time to events. Newson RB. 1. neurological disease status is classified on a single 5-scale rating. set seed 12345671 Room 33, Emmanuel Kaye Building /* Model 2: */ curves for different status variables in the same graph. In the code below, we load the data and create a fictional * http://www.ats.ucla.edu/stat/stata/, http://www.stata-journal.com/article.html?article=st0198, http://www.imperial.ac.uk/nhli/r.newson/papers.htm, http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/, http://www.stata.com/support/statalist/faq, st: Putting multiple ROC curves on a single graph, Re: st: Fama-Macbech regression procedure, Re: st: technical question on the removal of outliers. Twoway scatter plots of the smoothed pdfs are constructed, followed by plotting the ROC curve. Go to the middle tab. Dr Gonzalo, you can use option of comparison of two or more ROC curves in Stata. P.S. * http://www.stata.com/support/statalist/faq plot (roc_1, col = 1, lty = 2, main = "ROC") plot (roc_2, col = 4, lty = 3, add = TRUE) The dput is too big to post on stackoverflow so here is the structure of one of the ROC calculations. Here, the coordinates of the curve are not as . Comparing areas under the ROC curve The area under multiple ROC curves can be compared by using roccomp. All the receiver operating characteristic (ROC) commands in Stata use a Tue, 23 Aug 2011 12:30:20 +0200 Use Comparison of ROC curves to test the statistical significance of the difference between the areas under 2 to 6 dependent ROC curves (derived from the same cases) with the method of DeLong et al. xi: logistic i.dod30 age i.kon i.charlindex i.bak i.afdtype_unik i.sepsis First select marker(s), where all names of the variables, except the status variable, will be imported automatically by the tool. You will load. > rocplot, confband name(rocfit3) nodraw Multiple imputation and ROC curve anlaysis. logistics refvar classvar The following step-by-step example shows how plot multiple ROC curves in Python. rocfit x3 y3 I don't know what you mean by that. gen line1=0 E-mail: kim.gradel@ouh.regionsyddanmark.dk xi: logistic i.dod30 age i.kon i.charlindex i.bak i.afdtype_unik i.sepsis crp10 * For searches and help try: /* Model 4: */ ROC curves in logistic regression are used for determining the best cutoff value for predicting whether a new observation is a "failure" (0) or a "success" (1). * For searches and help try: Since the word "plot" is ambiguous, it is unclear which of these results the original poster had in mind. When requesting a correction, please mention this item's handle: RePEc:boc:bocode:s457181. To put several on one graph: Go to the first ROC graph. Subject Roger Newson StataCorp ; In the Positive event drop-down list, select the state that indicates the presence of the condition/event of interest. */ (scatter sens1 spec_1 , msymbol(p) mcolor(black) legend(off)) /* Purchase the article from Fax: +44 (0)20 7351 8322 General contact details of provider: https://edirc.repec.org/data/debocus.html . Fax: +45 65 91 72 64 The code in #2 will produce a graph with a single panel showing all three ROC curves. Although ROC curve analysis can be used for a variety of applications across a number of research fields, we will examine ROC curves through the lens of diagnostic testing. rocfit x1 y1 xi: logistic i.dod30 age i.kon i.charlindex i.bak i.afdtype_unik crp10 > ROC, AUC, Stata, Sensitivity, Specificity, Diagnostic device. Seattle, WA. We begin by tting a logistic model with foreign as (1988) or Hanley & McNeil, 1983.. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. "statalist@hsphsun2.harvard.edu" * http://www.ats.ucla.edu/stat/stata/ If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. fit1=glm (a~b+c, family='binomial') fit2=glm (a~c, family='binomial') Predict on the same data you trained the model with (or hold some out to test on if you want) rocplot, confband name(rocfit1) nodraw > || or () do not seem to work. Re: st: Putting multiple ROC curves on a single graph We rename the generated ROC and false-positive rate variables that this > Royal Brompton Campus Roger B Newson BSc MSc DPhil st: SV: Combining ROC curves in one graph Opinions expressed are those of the author, not of the institution. General contact details of provider: https://edirc.repec.org/data/debocus.html . Dear Stata-users, UNITED KINGDOM Abstract. Subject Step 7 - Create AUC and ROC for test data (pROC lib) statalist@hsphsun2.harvard.edu Charles Lindsey The ROC Curve. > roctab x3 y3, graph name(rocgraph3) nodraw To Kim Peters wrote: rename _fpr_rating fpr_ndisease You can do your three regressions and then do out-of-sample predictions saving those results in new variables. Thread starter CE479; Start date Mar 31, 2015; C. CE479 New Member. Double click to bring up the Format Graph dialog. Before describing the procedure for comparing areas under two or more ROC curves, let's examine the similarity between Stata's lroc command, usedto produceROC curves after logistic regression, and the roctab command. roc r rocr roc A multivariable ROC AS SUCH is a nonsense, given it is related to the change in sensitivity and specificity (the axes of the ROC plot, to be more precise they are 1-specificity (FDR) and . > rocplot, confband name(rocfit2) nodraw * http://www.stata.com/help.cgi?search When a predictor is categorical, the ROC curve has one less than number of categories as . Variables: select the variables of interest (at least 2, maximum 6). calculation of standard errors because we are only interested in the Emne: st: Combining ROC curves in one graph The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. * http://www.ats.ucla.edu/stat/stata/ Respiratory Epidemiology and Public Health Group For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . Overview Software Description Websites Readings Courses Overview This page briefly describes methods to evaluate risk prediction models using ROC curves. Step 2 - Read a csv dataset. A new command, -rocreg- can be used to draw ROC curves for different status variables in the same graph. Re: st: Combining ROC curves in one graph Nonparametric, semiparametric, and parametric . How can I combine these three roclines into one graph. All material on this site has been provided by the respective publishers and authors. Fra: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] P vegne af Kim Peeters * All the receiver operating characteristic (ROC) commands in Stata use a single status variable, with one or more classification variables. Roger And the ROC */ (scatter sens2 spec_2 , msymbol(p) mcolor(red)) /* There are 11 different variables in the dataset, but the only three that we care about are low, age, and smoke. Date Your models seem to be univariate, but instead of It also accepts -graph twoway- options so you can customize the appearance of the graph to your taste. http://www.imperial.ac.uk/nhli/r.newson/papers.htm Once the markers are selected, the direction should be defined. We use the -quietly- prefix to It provides a graphical representation of a classifier's performance, rather than a single value like most other metrics. */ (scatter sens3 spec_3 , msymbol(p) mcolor(green)) /* /* This is admittedly primitive, but it works!!!! The Harrell C statistic (which is also the area under an ROC curve) for a Cox regression model relates to the accuracy of its predictions about whose "death" precedes whose. This video shows you how to create ROC curves for diagnostic devices using Stata. Then -roccurve- can simultaneously calculate and plot ROC curves of the death_30d outcome against each of those predictions. "'statalist@hsphsun2.harvard.edu'" The ROC curve is informative about the performance over a series of thresholds and can be summarized by the area under the curve (AUC), a single number. you must be able to use Thank you. It also allows you to accept potential citations to this item that we are uncertain about. The geom_roc function requires the aesthetics d for disease status, and m for marker. /* Model 1: */ y <- data[ , c( " pnf " , " lac " )] roc lsens, gensens(sens1) genspec(spec1) replace nodraw Description When evaluating the performance of a screening test, an algorithm or a statistical model - such as a logistic regression - for which the outcome is dichotomous (e.g. Web: www.KCSinfo.dk Then we'll click the Insert tab along the top ribbon and then click Insert Scatter(X, Y) to create the following plot: Step 5: Calculate the AUC. gen spec_3 = 1-spec3 The algorithm begins by running mlogit B=100 times using bootstrapped records for each run while the original class labels are intact. I don't want to array the separately drawn graphs into one using I used the "cutpointr" package and I don't know how to merge the 2 results. Machine learning adapted ROC-curves to characterize the discriminative performance of classifiers. In this video I describe how ROC curves are constructed and how to interpret them to get combined ROC curves for the parametric case. In a typical diagnostic test, each unit (e.g., individual or patient) is measured on some scale or given a score with the intent that the measurement or score will be useful rocfit x2 y2 Departmental Web page: Delong and bootstrap methods test for a difference in the (partial) AUC of the ROC curves. Please note that corrections may take a couple of weeks to filter through > roctab x2 y2, graph name(rocgraph2) nodraw -twoway You can help correct errors and omissions. I don't want to array the separately drawn graphs into one using graph combine. Then -roccurve- can simultaneously calculate and plot ROC curves of the death_30d outcome against each of those predictions. In the True state drop-down list, select the true condition variable. -_fpr_rating- variables before fitting the next ROC model, because we Here is a more detailed answer to Kim's question. st: SV: Combining ROC curves in one graph ; If comparing 2 or more independent ROC curves, in the Y drop-down list, select the diagnostic test variable, and then in the Factor drop-down list, select the grouping variable. ROC curve from a model with a very strong predictor. Charles Lindsey, The Venkatraman method tests if the two curves are perfectly superposed. In Stata it is very easy to get the area under the ROC curve following either logit or logistic by using the lroc command. st: SV: Combining ROC curves in one graph Dear Stata-users, I have had the same problem, or at least a similar one, and I suggest a solution here: I have four multivariable logistic regression models. Boulevard 29, Entrance 101, 4th floor, DK-5000 Odense C Model 1 has the outcome variable + X variables, Model 2 = Model 1 + var1, Model 3 = Model 1 + var2, and Model 4 = Model 1 + var1 + var2. Stata's suite for ROC analysis consists of: roctab , roccomp, rocfit, rocgold, rocreg, and rocregplot . Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org. gen spec_1 = 1-spec1 Run -findit roccurve- and then install -roccurve- from ST00154. Consider the pooled ROC curve for Y; this curve combines all case observations together and all con-trol observations together, regardless of study center. containing the false-positive rate and ROC values, and then plot the ROC gen spec_4 = 1-spec4 The code in #3 will produce a graph with three panels, with one ROC curve in each. The ROC and false-positive rate values are saved as variables The Danish Clinical Quality Improvement Programme rename _fpr_rating fpr_disease * > Moreover, I would like to do the same thing after rocfit and rocplot: Til: Statalist To Often you may want to fit several classification models to one dataset and create a ROC curve for each model to visualize which model performs best on the data. It also accepts -graph twoway- options so you can customize the appearance of the graph to your taste. http://www.stata-journal.com/article.html?article=st0198 diseased vs. non-diseased), we typically * http://www.stata.com/help.cgi?search Multiple regression (an extension of simple linear regression) is used to predict the value of a dependent variable (also known as an outcome variable) based on the value of two or more independent variables (also known as predictor variables).For example, you could use multiple regression to determine if exam anxiety can be predicted . Class prediction is then performed for records not sampled during bootstrapping, and accuracy for the left out records is determined as the fraction of correct class membership predictions. Let's consider an example using the Hanley data, where an individual's The ROC curve is plotted with False Positive Rate in the x-axis against the True Positive Rate in the y-axis. I am trying to plot two ROC curves similar to below. legend(label(1 disease) label(2 ndisease)) In analysis of binary outcomes, the receiver operator characteristic (ROC) curve is heavily used to show the performance of a model or algorithm. can automatically draw ROC curves for different classifiers using the Our 28-story hotel overlooks the Han River, next to the iconic Dragon Bridge and five minutes from Da Nang beaches. The full area under a given ROC curve, or AUC, formulates an important . References rather than a simple line connector. || or () do not seem to work. Click Add to add a data set to the graph, and pick the appropriate data set (the "ROC Curve" page of the appropriate ROC analysis. As the area under an ROC curve is a measure of the usefulness of a test in general, where a greater area means a more useful test, the areas under ROC . . M. mostater New Member. Best wishes Kim Oren Gradel replace line1=1 if age>=60 From A new command, -rocreg- can be used to draw ROC Login or. This function compares two correlated (or paired) or uncorrelated (unpaired) ROC curves. Description. This chapter described different metrics for evaluating the performance of classification models. curves using -twoway-. I have four multivariable logistic regression models. . Stata's roctab provides nonparametric estimation of the ROC curve, and produces Bamber and Hanley confidence intervals for the area under the ROC curve. But to compare which is best . CE479 said: Missing data for two variables is 12.2% and 11.7%; the rest (13 variables) are less than 3.5% . Enjoy all-day dining in our restaurant and caf, and cocktails with panoramic views from The Sail lounge. In this article I will show how to adapt ROC Curve and ROC AUC metrics for multiclass classification. 09 Jan 2017, 15:08. > How can I combine these three roclines into one graph. The command syntax is slightly different if the ROC curves are correlated (that is, different diagnostic tests are applied to the same sample) or independent (that is, diagnostic tests are applied to different samples). An ROC curve is based on the notion of a "separator" scale, on which results for the diseased and nondiseased form a . -_roc_rating- and -_fpr_rating-. Repeat as necessary. . webuse hanley, clear The receiver operating characteristic (ROC) curve can be utilized to evaluate the performance of diagnostic tests. Kim In summary they show us the separability of the classes by all possible thresholds, or in other words, how well the model is classifying each class. > I have plotted three ROC curves using will need them for the graph. replace ndisease = 0 if runiform() > .8 I just need one graph that contains all three ROC lines. Public profiles for Economics researchers, Curated articles & papers on economics topics, Upload your paper to be listed on RePEc and IDEAS, Pretend you are at the helm of an economics department, Data, research, apps & more from the St. Louis Fed, Initiative for open bibliographies in Economics, Have your institution's/publisher's output listed on RePEc. To scatter roc_ndisease fpr_ndisease, connect(J) sort /// The ROC Curve and the ROC AUC score are important tools to evaluate binary classification models. Step 5- Make predictions on the model using the test dataset. Re: st: Putting multiple ROC curves on a single graph We rename the -_roc_rating- and In the dialog box you need to enter: Data. the various RePEc services. The idea here is that a single Prism graph can contain data sets . downloadable from SSC, to calcutate sensitivities and specificities, which can then be graphed using the -graph twoway- command. I just need one graph that contains all three ROC The false positive rate (FPR), true positive rate (TPR), and area under the curve (AUC) are determined from the smooth pdfs derived from KDE. Department of Quality and Research / HTA To evaluate the performance of regression models, read the Chapter @ref (regression-model-accuracy-metrics). hjanes@fhcrc.org. Now let's run the simulation one more time but where the variable x is in fact independent of y. See some sample code: library (pROC) data (infert) auc (case ~ age, data=infert) #Compute AUC for predicting case with the variable age mod1<-glm . Subject You may face such situations when you run multiple models and try to plot the ROC-Curve for each model in a single figure. roctab refvar classvar . Now we will draw the ROC curves with -rating- as a classifier for . or download a pre-publication draft from Mon, 28 Feb 2011 17:19:12 +0000 as P-values) for differences between 2 ROC areas. Best regards, Step 1: Import Necessary Packages First, we'll import several necessary packages in Python: Kim can use a similar method with -rocreg- and the -probit ml- options pr - exp(0*x)/(1+exp(0*x)) which gives the following ROC curve. * S dng phn mm Stata v ng cong ROC v chn ngng chn on The more that the curve hugs the top left corner of the plot, the better the model does at . rename _roc_rating roc_disease quietly rocreg ndisease rating, nobootstrap . Create ROC curves easily using MedCalc. Receiver operating characteristic (ROC) Analysis is a useful way to assess the accuracy of model predictions by plotting sensitivity versus (1-specificity) of a classification test (as the threshold varies over an entire range of diagnostic test results). Step 2: Fit the logistic regression model. . lsens, gensens(sens2) genspec(spec2) replace nodraw accuracy of the marker (the ROC curve) is the same in the two centers. twoway scatter roc_disease fpr_disease, connect(J) sort || /// Submitted by . MedCalc offers the following unique advanced options: Estimation of sensitivity and specificity at fixed specificity and sensitivity: an option to compile a table with estimation of sensitivity and specificity (with a BC a bootstrapped 95% confidence interval) for a fixed and prespecified specificity and sensitivity of 80%, 90%, 95% and 97.5% (Zhou et al . London SW3 6LR * http://www.stata.com/help.cgi?search Our 24-hour fitness center features yoga and stretching space, with more room for relaxation on the fourth-floor pool terrace. Sdr. same status variable. curves can be compared using the -somersd- package, also downloadable We present a comprehensive suite of Stata commands for performing ROC analysis. An alternative possibility to -roccomp- is to use the -senspec- package, gen spec_2 = 1-spec2 The area under the ROC curve (AUC) is a widely used summary index for comparing multiple ROC curves. Here is a short answer to Kim's question. Using ROC Curve, you have created multiple curves in order to compare three competing classification models. ROC-curves in machine learning. These metrics include: classification accuracy, confusion matrix, Precision, Recall and Specificity, and ROC curve. . However, with lroc you cannot compare the areas under the ROC curve for two different models.