# Extract Multiple & Adjusted R-Squared from Linear Regression Model in R (2 Examples)

In this tutorial youâ€™ll learn how to **return multiple and adjusted R-squared** in the R programming language.

The tutorial is structured as follows:

Letâ€™s get started!

## Example Data

First, we have to create some example data:

set.seed(96149) # Create randomly distributed data x1 <- rnorm(300) x2 <- rnorm(300) - 0.1 * x1 x3 <- rnorm(300) + 0.1 * x1 - 0.5 * x2 x4 <- rnorm(300) - 0.4 * x2 - 0.1 * x3 x5 <- rnorm(300) + 0.1 * x1 - 0.2 * x3 y <- rnorm(300) + 0.5 * x1 + 0.5 * x2 + 0.15 * x3 - 0.4 * x4 - 0.25 * x5 data <- data.frame(y, x1, x2, x3, x4, x5) head(data) # Show first six lines # y x1 x2 x3 x4 x5 # 1 -0.2553708 0.4399836 0.2144276 -0.24921404 0.7626867 -0.000145643 # 2 0.9582395 0.1866435 -0.8674311 0.56741079 0.2266811 -0.482339176 # 3 0.5354913 0.5123466 0.8521783 -0.15192973 -0.5772924 1.598023729 # 4 -0.1751357 -0.2642710 -0.6622039 0.91587607 -0.8784139 0.175314482 # 5 0.4741015 -1.2264237 1.1414974 -0.02544234 -1.4704185 2.154123548 # 6 -2.4687764 -0.4832017 -0.6963652 -0.27676098 3.5740767 1.535050595

The previous output of the RStudio console illustrates the structure of our example data â€“ It consists of six variables, whereby the column y is containing our outcome and the remaining columns are used as predictors.

Letâ€™s use our data to fit a linear regression model in R:

mod_summary <- summary(lm(y ~ ., data)) # Run linear regression model mod_summary # Summary of linear regression model

The previous image shows the output of our linear regression analysis. I have marked the values we are interested in in this example in red.

## Example 1: Extracting Multiple R-squared from Linear Regression Model

This Example shows how to pull out the multiple R-squared from our output.

mod_summary$r.squared # Returning multiple R-squared # 0.4131335

The RStudio console shows our result: The multiple R-squared of our model is 0.4131335.

## Example 2: Extracting Adjusted R-squared from Linear Regression Model

Alternatively to the multiple R-squared, we can also extract the adjusted R-squared:

mod_summary$adj.r.squared # Returning adjusted R-squared # 0.4031528

The adjusted R-squared of our linear regression model is 0.4031528.

## Video, Further Resources & Summary

Do you need further info on the R programming codes of this tutorial? Then you may want to watch the following video of my YouTube channel. In the video, Iâ€™m explaining the R programming code of this tutorial.

*The YouTube video will be added soon.*

In addition to the video, you might read the other tutorials of this homepage. I have published numerous tutorials about linear regression models already.

- Extract F-Statistic, Number of Predictor Variables/Categories & Degrees of Freedom
- Extract Regression Coefficients of Linear Model
- R Programming Examples

This page illustrated how to **pull out multiple and adjusted R-squared from regressions** in the R programming language. Donâ€™t hesitate to let me know in the comments below, in case you have any further questions or comments. Furthermore, donâ€™t forget to subscribe to my email newsletter in order to receive updates on new tutorials.

### Statistics Globe Newsletter

## 10 Comments. Leave new

hello i want to extract feature by using r square with R studio kindly guide the codding and procedure as well..thanks

Hi Aroosa,

I’m not sure if I get your question. Could you please tell some more details?

Thanks!

Joachim

Hi, Joachim,

Do you have any idea how to derive outcome from summary(stan_glm). Thanks

Hey Jason,

Could you explain what exactly you mean with “outcome”?

Regards,

Joachim

And how can I pull if I have a list with 24 results of lm inside?

Hey Lais,

So your list contains 24 different model outputs? Please clarify your question ðŸ™‚

Regards,

Joachim

Hi Joachim,

Can i get a coeficient of determination for the test predictions?

Hey Fernando,

The coefficient of determination (i.e. R-Squared) is extracted in Example 1 of this tutorial. Or am I misinterpreting your question?

Regards,

Joachim

Hey Joachim,

I would like to know how can I get an adjusted regression. Is it the adjusted R-square?

Thanks,

Christian

Hi Christian,

Could you please explain what exactly you mean with “adjusted regression”?

Regards,

Joachim