How to Calculate R-Squared for glm in R

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Often when we fit a linear regression model, we use R-squared as a way to assess how well a model fits the data.

R-squared represents the proportion of the variance in the response variable that can be explained by the predictor variables in a regression model.

This number ranges from 0 to 1, with higher values indicating a better model fit.

However, there is no such R-squared value for general linear models like logistic regression models and Poisson regression models.

Instead, we can calculate a metric known as McFadden’s R-Squared, which ranges from 0 to just under 1, with higher values indicating a better model fit.

We use the following formula to calculate McFadden’s R-Squared:

McFadden’s R-Squared = 1 – (log likelihood_model / log likelihood_null)

where:

• log likelihood_model: Log likelihood value of current fitted model
• log likelihood_null: Log likelihood value of null model (model with intercept only)

In practice, values over 0.40 indicate that a model fits the data very well.

The following example shows how to calculate McFadden’s R-Squared for a logistic regression model in R.

Example: Calculating McFadden’s R-Squared in R

For this example, we’ll use the Default dataset from the ISLR package. We can use the following code to load and view a summary of the dataset:

#install and load ISLR package
install.packages('ISLR')
library(ISLR)

#define dataset
data #view summary of dataset
summary(data)

 default    student       balance           income     
 No :9667   No :7056   Min.   :   0.0   Min.   :  772  
 Yes: 333   Yes:2944   1st Qu.: 481.7   1st Qu.:21340  
                       Median : 823.6   Median :34553  
                       Mean   : 835.4   Mean   :33517  
                       3rd Qu.:1166.3   3rd Qu.:43808  
                       Max.   :2654.3   Max.   :73554  

#find total observations in dataset
nrow(data)

[1] 10000

This dataset contains the following information about 10,000 individuals:

• default: Indicates whether or not an individual defaulted.
• student: Indicates whether or not an individual is a student.
• balance: Average balance carried by an individual.
• income: Income of the individual.

We will use student status, bank balance, and income to build a logistic regression model that predicts the probability that a given individual defaults:

#fit logistic regression model
model binomial', data=data)

#view model summary
summary(model)

Call:
glm(formula = default ~ balance + student + income, family = "binomial", 
    data = data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4691  -0.1418  -0.0557  -0.0203   3.7383  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.087e+01  4.923e-01 -22.080  

Next, we’ll use the following formula to calculate McFadden’s R-squared value for this model:

#calculate McFadden's R-squared for model
with(summary(model), 1 - deviance/null.deviance)

[1] 0.4619194

McFadden’s R-squared value turns out to be 0.4619194. This value is fairly high, which indicates that our model fits the data well and has high predictive power.

Also note that we could use the pR2() function from the pscl package to calculate McFadden’s R-square value for the model as well:

#install and load pscl package
install.packages('pscl')
library(pscl)

#calculate McFadden's R-squared for model
pR2(model)['McFadden']

 McFadden 
0.4619194

Notice that this value matches the one calculated earlier.

Additional Resources

The following tutorials explain how to perform other common tasks in R:

How to Calculate R-Squared in R
How to Calculate Adjusted R-Squared in R
What is a Good R-squared Value?