Extract Multiple & Adjusted R-Squared from Linear Regression Model in R (2 Examples)
In this tutorial you’ll learn how to return multiple and adjusted R-squared in the R programming language.
The tutorial is structured as follows:
Let’s get started!
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 – It consists of six variables, whereby the column y is containing our outcome and the remaining columns are used as predictors.
Let’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, I’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. Don’t hesitate to let me know in the comments below, in case you have any further questions or comments. Furthermore, don’t forget to subscribe to my email newsletter in order to receive updates on new tutorials.
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hello i want to extract feature by using r square with R studio kindly guide the codding and procedure as well..thanks
I’m not sure if I get your question. Could you please tell some more details?
Do you have any idea how to derive outcome from summary(stan_glm). Thanks
Could you explain what exactly you mean with “outcome”?
And how can I pull if I have a list with 24 results of lm inside?
So your list contains 24 different model outputs? Please clarify your question 🙂
Can i get a coeficient of determination for the test predictions?
The coefficient of determination (i.e. R-Squared) is extracted in Example 1 of this tutorial. Or am I misinterpreting your question?
I would like to know how can I get an adjusted regression. Is it the adjusted R-square?
Could you please explain what exactly you mean with “adjusted regression”?