**plot**- We can use the rocsnr function to calculate and plot ROC curves. (mathworks.com)
- Instead of individually calculating Pd and Pfa values for a given SNR, we can view the results in a plot of ROC curves. (mathworks.com)
- Calling the rocsnr function with an input vector of four SNR values and no output arguments produces a plot of the ROC curves. (mathworks.com)
- In the plot we can select the data cursor button in the toolbar (or in the Tools menu) and then select the SNR = 8 dB curve at the point where Pd = 0.9 to verify that Pfa is approximately 0.01. (mathworks.com)
- ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. (wikipedia.org)
- Since TPR is equivalent to sensitivity and FPR is equal to 1 − specificity, the ROC graph is sometimes called the sensitivity vs (1 − specificity) plot. (wikipedia.org)
- ROC curves plot the sensitivity of a biomarker on the y axis, against the false discovery rate (1- specificity) on the x axis. (wikipedia.org)
- The value a can be used to plot a summary ROC (SROC) curve. (wikipedia.org)
- Procedures for method evaluation and method comparison include ROC curve analysis, Bland-Altman plot, as well as Deming and Passing-Bablok regression. (wikipedia.org)

**graphs**- Figure 2 shows the resultant ROC graphs while Table 1 shows the corresponding AUC scores. (nih.gov)
- ROC graphs enable to compare two or more supervised learning algorithms, they have properties that make them especially useful for domains with skewed class distribution and unequal classification error costs. (blogspot.com)
- Multiple Antigen LOOCV ROC curves.The LOOCV ROC graphs show classifiers with increasing number of human serodiagnostic antigens. (nih.gov)

**sensitivity**- specificity was 76%, while sensitivity was 62% (see figure 4 for ROC curve). (nih.gov)
- The ROC curve is thus the sensitivity as a function of fall-out. (wikipedia.org)
- The best possible prediction method would yield a point in the upper left corner or coordinate (0,1) of the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives). (wikipedia.org)
- In medical biomarker studies it is becoming increasingly common to report this tradeoff in sensitivity and specificity using a Receiver Operating Characteristic (ROC) curve. (wikipedia.org)
- ROC curves provide a simple visual method for one to determine the boundary limit (or the separation threshold) of a biomarker or a combination of biomarkers for the optimal combination of sensitivity and specificity. (wikipedia.org)

**classifier**- AUC (Area Under Curve) reduces ROC performances to a single scalar value, which enables to compare several classifiers: this area is equivalent to the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance. (blogspot.com)
- To draw a ROC curve, only the true positive rate (TPR) and false positive rate (FPR) are needed (as functions of some classifier parameter). (wikipedia.org)

**Detection**- ROC curves are plots of the probability of detection (Pd) vs. the probability of false alarm (Pfa) for a given signal-to-noise ratio (SNR). (mathworks.com)
- The detection performance is characterized by the area under the Receiver Operating Characteristic (ROC) Curve (AUC), which is a simple statistical performance measuring metric that varies between 0.5 and 1. (techrepublic.com)
- In general, if the probability distributions for both detection and false alarm are known, the ROC curve can be generated by plotting the cumulative distribution function (area under the probability distribution from − ∞ {\displaystyle -\infty } to the discrimination threshold) of the detection probability in the y-axis versus the cumulative distribution function of the false-alarm probability on the x-axis. (wikipedia.org)
- The ROC curve was first developed by electrical engineers and radar engineers during World War II for detecting enemy objects in battlefields and was soon introduced to psychology to account for perceptual detection of stimuli. (wikipedia.org)

**plots**- The rocsnr function plots the ROC curves by default if no output arguments are specified. (mathworks.com)
- The Gini coefficient is usually defined mathematically based on the Lorenz curve, which plots the proportion of the total income of the population (y axis) that is cumulatively earned by the bottom x% of the population (see diagram). (wikipedia.org)

**Characteristic**- This example shows how you can assess the performance of both coherent and noncoherent systems using receiver operating characteristic (ROC) curves. (mathworks.com)
- Receiving Operating Characteristic (ROC) curves are basically used in judgement of usefulness of diagnostic tests (in healthcare) or in wider sense in objective quantification of decision methods with two outcomes (like healthy or diseased in case of a diagnostic tool). (smart-statistics.com)
- Receiver operating characteristic (ROC) curve analyses. (nih.gov)
- Receiver operating characteristic (ROC) curve analyses.Total-anti-HBc (full line) and anti-HBc-IgM (dotted line). (nih.gov)
- To establish a collection of antigens able to accurately distinguish brucellosis cases from controls, leave one out cross-validation (LOOCV) receiver operating characteristic (ROC) curves were generated for individual serodiagnostic antigens to assess the ability to separate the control and disease cases (Fig. 4). (nih.gov)
- In statistics, a receiver operating characteristic curve, i.e. (wikipedia.org)
- The ROC is also known as a relative operating characteristic curve, because it is a comparison of two operating characteristics (TPR and FPR) as the criterion changes. (wikipedia.org)
- Receiver Operating Characteristic Curve Explorer and Tester (ROCCET) is an open-access web server for performing biomarker analysis using ROC (Receiver Operating Characteristic) curve analyses on metabolomic data sets. (wikipedia.org)
- 1. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. (wikipedia.org)
- Youden's index is often used in conjunction with receiver operating characteristic (ROC) analysis. (wikipedia.org)

**predictive**- ROC curve analysis with OptimalCutpoints, predictive values. (smart-statistics.com)
- In Survival Model Predictive Accuracy and ROC curve (Heaherty and Zheng 2005), there are 2 examples, the first one is VA lung cancer data and the second one is mayo PBC data. (codegur.com)
- Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. (wikipedia.org)

**confidence intervals**- In the univariate module single variables are evaluated (by a t-test) and ranked for their separation performance (i.e. the AUC of the ROC), including confidence intervals (CI) and a computed optimal threshold. (wikipedia.org)

**Accuracy**- The AUC (area under the curve) of the ROC curve reflects the overall accuracy and the separation performance of the biomarker (or biomarkers), and can be readily used to compare different biomarker combinations or models. (wikipedia.org)

**area**- The serodiagnostic antigens were ordered by decreasing single antigen area under the curve (AUC). (nih.gov)
- The largest area under these three ROC curves is 0.898, indicating that statistically the best combination is VPG as reference with the CBG results as the screening tool. (nih.gov)
- The smaller area under the curve for the case where HbA1c is the reference and VPG is the screening tool is 0.733. (nih.gov)
- The area under the ROC curve for TGF-β1 (0.693) and MCP-1 (0.653) indicated that these cytokines could serve as biomarkers for the progression of CWP. (nih.gov)
- K {\displaystyle K} denotes lifetime morbid risk, h L 2 {\displaystyle h_{L}^{2}} denotes heritability of liability, A U C {\displaystyle AUC} denotes area under the ROC curve. (wikipedia.org)
- The index is represented graphically as the height above the chance line, and it is also equivalent to the Area under the Curve subtended by a single operating point. (wikipedia.org)

**prediction**- Each prediction result or instance of a confusion matrix represents one point in the ROC space. (wikipedia.org)

**characteristics**- The resulting analysis produces the top-performing multi-variable model(s) based on their ROC curve characteristics. (wikipedia.org)

**varying-coefficient**- ROC curves are estimated using a varying-coefficient Cox model with the derived model score as the single predictor. (codegur.com)

**statistical**- What are the statistical challenges in management of ROC curves? (smart-statistics.com)
- Statistical indicators for the interpretation of the three ROC curves are displayed in Table 3. (nih.gov)

**analysis**- The discriminative capacity of the 2 assays was comparable, even if total-anti-HBc performed better than IgM-anti-HBc (p = 0.212 and p = 0.062 for ROC analysis a and b respectively). (nih.gov)
- ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. (wikipedia.org)
- ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making. (wikipedia.org)
- ROC analysis since then has been used in medicine, radiology, biometrics, forecasting of natural hazards, meteorology, model performance assessment, and other areas for many decades and is increasingly used in machine learning and data mining research. (wikipedia.org)
- ROCCET's ROC curve generation and analysis is specifically tailored for metabolomics datasets. (wikipedia.org)
- In the multivariate module one can choose between three different techniques - SVM (support vector machine), PLS-DA (partial least squares discriminant analysis) and Random Forests for classifying and selecting metabolites or clinical variables for an optimal ROC performance. (wikipedia.org)

**graph**- An ROC graph depicts relative trade-offs between true positives rate and false positives rate. (blogspot.com)

**methods**- Smooth of a ROC curve (sometimes the classification is based on a discrete scale (e.g. for a cancer diagnostic tool: "normal", "benign", "probably benign", "suspicious", "malignant") and smoothing methods fit a continuous curve based on the assumptions of the background distribution). (smart-statistics.com)

**Discovery**- ROC curve and false discovery rates (FDR) for phenotypic similarities between diseases provided by PhenUMA and PhenomeNET. (nih.gov)

**radar**- ROC curves are often used to assess the performance of a radar or sonar detector. (mathworks.com)

**threshold**- The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. (wikipedia.org)

**diagnostic**- The index is defined for all points of an ROC curve, and the maximum value of the index may be used as a criterion for selecting the optimum cut-off point when a diagnostic test gives a numeric rather than a dichotomous result. (wikipedia.org)

**Figure**- The resulting ROC curves from Resnik's and Robinson's measures give better results than those provided by PhenomeNET (Figure 6A). (nih.gov)
- The ROC curve with CBG values as the screening variable and VPG values as the reference (positive actual state 7.0 mmol/L (126 mg/dl)) is shown in Figure 1 and the ROC curves with CBG and VPG as the screening variables and HbA1c as the reference (positive actual state 7%) are provided in Figure 2. (nih.gov)
- An image of different ROC curves is shown in Figure 1. (wikipedia.org)

**performance**- Examining a detector's ROC curves provides insight into its performance. (mathworks.com)

**respectively**- The areasunder the ROC curve for TGF-β1 and MCP-1 were 0.69 (95% CI, 0.55-0.84,p=0.012) and 0.65 (95% CI, 0.50-0.81,p=0.048), respectively. (nih.gov)
- The ROC curves of Δ%MPV and Δ%PDW are shown in Figures 1 and 2 respectively. (nih.gov)
- A ROC space is defined by FPR and TPR as x and y axes, respectively, which depicts relative trade-offs between true positive (benefits) and false positive (costs). (wikipedia.org)

**values**- One feature of the rocsnr function is that you can specify a vector of SNR values and rocsnr calculates the ROC curve for each of these SNR values. (mathworks.com)

**distribution**- The same value may result from many different distribution curves. (wikipedia.org)

**equivalent**- An alternative approach is to define the Gini coefficient as half of the relative mean absolute difference, which is mathematically equivalent to the Lorenz curve definition. (wikipedia.org)

**example**- We add a very tiny ridge penalty to obtain a more stable e.Res for example the ROC curve and AUC belong to this category. (rgsinhibitor.com)

**test**- A ROC curve starts at the (0,0) coordinate representing the case when all test results are negative and ends at (1,1) coordinate, representing that all test results are positive. (smart-statistics.com)

**better**- As a rule of thumb, the fewer the biomarkers that one uses to maximize the AUC of the ROC curve, the better. (wikipedia.org)

**maximum**- It other words this is the J is the maximum vertical distance between the ROC curve and the diagonal. (smart-statistics.com)

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