# 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.

## 16 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

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .567a .321 .309 .11806

a. Predictors: (Constant), X4, X2, X1, X3

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 1.504 4 .376 26.975 .000b

Residual 3.178 228 .014

Total 4.682 232

a. Dependent Variable: WPI

b. Predictors: (Constant), X4, X2, X1, X3

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

1 (Constant) 3.200 .008 413.317 .000

X1 .040 .008 .284 5.101 .000

X2 .067 .008 .473 8.599 .000

X3 .010 .008 .069 1.231 .220

X4 .007 .008 .050 .902 .368

a. Dependent Variable: WPI

Hello,

What is your question about?

Best,

Cansu

hello,

interpretation of the findings

Hello,

This source of Scribbr could be useful for you to interpret the results in case you are using ANOVA model.

Best,

Cansu

Thank you

Welcome!