# How do I run a ROC curve in SAS?

Table of Contents

## How do I run a ROC curve in SAS?

How to Create a ROC Curve in SAS

- Step 1: Create the Dataset.
- Step 2: Fit the Logistic Regression Model & Create ROC Curve.
- Step 3: Interpret the ROC Curve.
- Additional Resources.

### What is the ROC score for logistic regression?

The Area Under the ROC curve (AUC) is an aggregated metric that evaluates how well a logistic regression model classifies positive and negative outcomes at all possible cutoffs. It can range from 0.5 to 1, and the larger it is the better.

**What is ROC curve in SAS?**

➢ ROC (Receiver Operating Characteristic) curve is a fundamental tool for. diagnostic test evaluation. It is increasingly used in many fields, such as data mining, financial credit scoring, weather forecasting etc. ➢ ROC curve plots the true positive rate (sensitivity) of a test versus its false.

**How do you read ROC curve results?**

Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR). The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test.

## How do you get an AUC in SAS?

Steps of calculating AUC of validation data

- Split data into two parts – 70% Training and 30% Validation.
- Run logistic regression model on training sample.
- Note coefficients (estimates) of significant variables coming in the model run in Step 2.

### What does ROC curve tell us?

ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition the area under the ROC curve gives an idea about the benefit of using the test(s) in question.

**How do you interpret a ROC curve in logistic regression?**

The more that the ROC curve hugs the top left corner of the plot, the better the model does at classifying the data into categories. To quantify this, we can calculate the AUC (area under the curve) which tells us how much of the plot is located under the curve. The closer AUC is to 1, the better the model.

**What is a good ROC curve?**

AREA UNDER THE ROC CURVE In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.

## What is a good ROC curve score?

80 – 90

Based on a rough classifying system, AUC can be interpreted as follows: 90 -100 = excellent; 80 – 90 = good; 70 – 80 = fair; 60 – 70 = poor; 50 – 60 = fail. In figure 1, the line (A) represents the ROC for an ideal diagnostic test. This curve represents a sensitivity and specificity of 100%.

### What is the AUC in SAS?

The area under the curve (AUC) relative to a baseline value can be a useful tool to summarize such data. SAS DATA steps along with the MEANS or SQL procedure can be used to calculate the AUC for longitudinal data arranged in a stacked data set.

**What is a good ROC AUC score?**

The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6.

**How can we interpret the coefficients of a logistic model?**

The logistic regression coefficient β associated with a predictor X is the expected change in log odds of having the outcome per unit change in X. So increasing the predictor by 1 unit (or going from 1 level to the next) multiplies the odds of having the outcome by eβ.

## What does AUC 0.75 mean?

An AUC of 0.75 would actually mean that lets say we take two data points belonging to separate classes then there is 75% chance model would be able to segregate them or rank order them correctly i.e positive point has a higher prediction probability than the negative class. (

### What is a bad ROC curve?

“Accuracy is measured by the area under the ROC curve. An area of 1 represents a perfect test; an area of . 5 represents a worthless test.

**How do you calculate AUC from ROC curve?**

ROC AUC is the area under the ROC curve and is often used to evaluate the ordering quality of two classes of objects by an algorithm. It is clear that this value lies in the [0,1] segment. In our example, ROC AUC value = 9.5/12 ~ 0.79.

**How do you calculate AUC in ROC curve?**

## What is a good AUC for logistic regression?

### How do you report logistic regression results?

We can use the following general format to report the results of a logistic regression model: Logistic regression was used to analyze the relationship between [predictor variable 1], [predictor variable 2], … [predictor variable n] and [response variable].

**What do coefficients of logistic regression mean?**

A regression coefficient describes the size and direction of the relationship between a predictor and the response variable. Coefficients are the numbers by which the values of the term are multiplied in a regression equation.