Contents

- 1 What package is ROC curve in R?
- 2 How do you get an AUC in R?
- 3 What is DeLong test?
- 4 What is ROC value?
- 5 What is ROC curve in data mining?
- 6 How do you make a ROC curve?
- 7 How do you get AUC from ROC curve?
- 8 How do you compare ROC curves in SPSS?
- 9 What is area under the curve in statistics?
- 10 What does the ROC curve in are mean?
- 11 Do you need Proc package for ROC curve?
- 12 Which is the best program for drawing ROC curves?
- 13 Do you need a ProB type for rocr?

## What package is ROC curve in R?

the pROC package

The basic unit of the pROC package is the roc function. It will build a ROC curve, smooth it if requested (if smooth=TRUE), compute the AUC (if auc=TRUE), the confidence interval (CI) if requested (if ci=TRUE) and plot the curve if requested (if plot=TRUE).

## How do you get an AUC in R?

How to Calculate AUC (Area Under Curve) in R

- Step 1: Load the Data. First, we’ll load the Default dataset from the ISLR package, which contains information about whether or not various individuals defaulted on a loan.
- Step 2: Fit the Logistic Regression Model.
- Step 3: Calculate the AUC of the Model.

## What is DeLong test?

A widely used test to compare the difference between two AUCs relies on the method developed in a seminal paper by DeLong et al. [9] (henceforth ‘the DeLong test’). It provides a confidence interval and standard error of the difference between two (or more) correlated AUCs.

## What is ROC value?

Receiver operating characteristic (ROC) curves compare sensitivity versus specificity across a range of values for the ability to predict a dichotomous outcome. Area under the ROC curve is another measure of test performance.

## What is ROC curve in data mining?

An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate.

## How do you make a ROC curve?

Creating a ROC curve A ROC curve is constructed by plotting the true positive rate (TPR) against the false positive rate (FPR). The true positive rate is the proportion of observations that were correctly predicted to be positive out of all positive observations (TP/(TP + FN)).

## How do you get AUC from ROC curve?

The AUC for the ROC can be calculated using the roc_auc_score() function. Like the roc_curve() function, the AUC function takes both the true outcomes (0,1) from the test set and the predicted probabilities for the 1 class. It returns the AUC score between 0.0 and 1.0 for no skill and perfect skill respectively.

## How do you compare ROC curves in SPSS?

Comparing two or more ROC curves

- Select a cell in the dataset.
- On the Analyse-it ribbon tab, in the Statistical Analyses group, click Diagnostic, and then under the Accuracy heading, click:
- In the True state drop-down list, select the true condition variable.

## What is area under the curve in statistics?

The area under (a ROC) curve is a measure of the accuracy of a quantitative diagnostic test. The interpretation of the AUC is: The average value of sensitivity for all possible values of specificity (Zhou, Obuchowski, McClish, 2001) .

## What does the ROC curve in are mean?

ROC stands for Receiver Operating Characteristics, and it is used to evaluate the prediction accuracy of a classifier model. ROC curve is a metric describing the trade-off between the sensitivity (true positive rate, TPR) and specificity (false positive rate, FPR) of a prediction in all probability cutoffs (thresholds).

## Do you need Proc package for ROC curve?

ROC curve can obiviously be plotted in many ways, and it is not necessary to use the pROC package. In case some of you wish to use it, here are few points to keep in mind: roc function by default will give a curve between Senstivity and Specificity and not (1-Specificity). So, the x axis will have a reverse axis.

## Which is the best program for drawing ROC curves?

ROCR has been around for almost 14 years, and has be a rock-solid workhorse for drawing ROC curves. I particularly like the way the , parameters. Not only is this reassuringly transparent, it shows the flexibility to calculate nearly every performance measure for a binary classifier by entering the appropriate parameter.

## Do you need a ProB type for rocr?

But when you plot that, ROCR generates a single meaningful point on ROC curve. For having many points on your ROC curve, you really need the probability associated with each prediction – i.e. use type=’prob’ in generating predictions.