# How to Calculate R-Squared for glm in R

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 likelihoodmodel / log likelihoodnull)

where:

• log likelihoodmodel: Log likelihood value of current fitted model
• log likelihoodnull: 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 <- ISLR::Default

#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 <- glm(default~student+balance+income, family='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  < 2e-16 ***
balance      5.737e-03  2.319e-04  24.738  < 2e-16 ***
studentYes  -6.468e-01  2.363e-01  -2.738  0.00619 **
income       3.033e-06  8.203e-06   0.370  0.71152
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 2920.6  on 9999  degrees of freedom
Residual deviance: 1571.5  on 9996  degrees of freedom
AIC: 1579.5

Number of Fisher Scoring iterations: 8
```

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

0.4619194
```

Notice that this value matches the one calculated earlier.

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

## 3 Replies to “How to Calculate R-Squared for glm in R”

1. Xavier says:

Hi, I get slightly different values when I calculate McFadden’s R-squared using the first method compared to the second for the same glm output.
Method 1:
#calculate McFadden’s R-squared for model
with(summary(model), 1 – deviance/null.deviance)

Method 2:
#calculate McFadden’s R-squared for model

Any idea why?

2. Anon Coder says:

Hi,
I’ve tried running both the pR2 and the with(summary(model) methods for 7 of my models and have got different results from the two methods for each model. Do you know why? My models are negative binomial and binomial GLMs.

3. Mehmet Akif says:

Hi,
Thank you for this wonderful source. I just have one question.
Can I use the pR2() function you mentioned above for Poisson regression as well?

This is my model:
model <- glm (UC ~ Pasture + Rainfall + Temp + offset(log(Pop)), family = poisson(link = "log"), data = data)

I installed the package and just typed: