Logistisk regression i r
In general, the lower this rate the better the model is able to predict outcomes, so this particular model turns out to be very good at predicting whether an individual will default or not. My name is Zach Bobbitt. Values close to 0 indicate that the model has no predictive power.
Step-by-Step Guide to Logistic Regression in R
These results match up nicely with the p-values from the model. Logistic regression is a method we can use to fit a regression model when the response variable is binary. Thus, any individual with a probability of defaulting of 0. This tutorial provides a step-by-step example of how to perform logistic regression in R. We can use the following code to load and view a summary of the dataset:.
Since none of the predictor variables in our models have a VIF over 5, we can assume that multicollinearity is not an issue in our model. However, there is no such R 2 value for logistic regression. We can see that the AUC is 0. Higher values indicate more importance. A value of 0. Logistic regression uses a method known as maximum likelihood estimation to find an equation of the following form:. Lastly, we can analyze how well our model performs on the test dataset.
This indicates that our model does a good job of predicting whether or not an individual will default. We can use the following code to calculate the probability of default for every individual in our test dataset:. Using this threshold, we can create a confusion matrix which shows our predictions compared to the actual defaults:. In practice, values over 0. We will use student status, bank balance, and income to build a logistic regression model that predicts the probability that a given individual defaults.
Thus, when we fit a logistic regression model we can use the following equation to calculate the probability that a given observation takes on a value of We then use some probability threshold to classify the observation as either 1 or 0.
In typical linear regression , we use R 2 as a way to assess how well a model fits the data. For example, a one unit increase in balance is associated with an average increase of 0. We can also compute the importance of each predictor variable in the model by using the varImp function from the caret package:. Lastly, we can plot the ROC Receiver Operating Characteristic Curve which displays the percentage of true positives predicted by the model as the prediction probability cutoff is lowered from 1 to 0.
The higher the AUC area under the curve , the more accurately our model is able to predict outcomes:. For example, we might säga that observations with a probability greater than or equal to 0. However, we can find the optimal probability to use to maximize the accuracy of our model bygd using the optimalCutoff function from the InformationValue package:. The formula on the right side of the equation predicts the log odds of the response variable taking on a value of 1.
This tells us that the optimal probability cutoff to use fryst vatten 0. We can also calculate the VIF values of each variable in the model to see if multicollinearity is a problem:. As a rule of thumb, VIF values above 5 indicate severe multicollinearity. The complete R code used in this tutorial can be found here. The p-values in the output also give us an idea of how effective each predictor variable is at predicting the probability of default:.
Balance fryst vatten by far the most important predictor variabel, followed by student status and then income. We can see that balance and lärling status seem to be important predictors since they have low p-values while income fryst vatten not nearly as important. By default, any individual in the test dataset with a probability of default greater than 0. Hey there.
How to Perform Logistic Regression in R (Step-by-Step)
The total misclassification error rate is 2. This number ranges from 0 to 1, with higher values indicating better model fit. The coefficients in the output indicate the average change in log odds of defaulting. This dataset contains the following information about 10, individuals:.