You can use the following syntax to extract the **root mean square error (RMSE)** from the lm() function in R:

sqrt(mean(model$residuals^2))

The following example shows how to use this syntax in practice.

**Related: **How to Interpret Root Mean Square Error (RMSE)

**Example: Extract RMSE from lm() in R**

Suppose we fit the following multiple linear regression model in R:

#create data frame df <- data.frame(rating=c(67, 75, 79, 85, 90, 96, 97), points=c(8, 12, 16, 15, 22, 28, 24), assists=c(4, 6, 6, 5, 3, 8, 7), rebounds=c(1, 4, 3, 3, 2, 6, 7)) #fit multiple linear regression model model <- lm(rating ~ points + assists + rebounds, data=df)

We can use the **summary()** function to view the entire summary of the regression model:

#view model summary summary(model) Call: lm(formula = rating ~ points + assists + rebounds, data = df) Residuals: 1 2 3 4 5 6 7 -1.5902 -1.7181 0.2413 4.8597 -1.0201 -0.6082 -0.1644 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 66.4355 6.6932 9.926 0.00218 ** points 1.2152 0.2788 4.359 0.02232 * assists -2.5968 1.6263 -1.597 0.20860 rebounds 2.8202 1.6118 1.750 0.17847 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.193 on 3 degrees of freedom Multiple R-squared: 0.9589, Adjusted R-squared: 0.9179 F-statistic: 23.35 on 3 and 3 DF, p-value: 0.01396

To only extract the root mean square error (RMSE) of the model, we can use the following syntax:

#extract RMSE of regression model sqrt(mean(model$residuals^2)) [1] 2.090564

The RMSE of the model is **2.090564**.

This represents the average distance between the predicted values from the model and the actual values in the dataset.

Note that the lower the RMSE, the better a given model is able to “fit” a dataset.

When comparing several different regression models, the model with the lowest RMSE is said to be the one that “fits” the dataset the best.

**Additional Resources**

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

How to Perform Simple Linear Regression in R

How to Perform Multiple Linear Regression in R

How to Create a Residual Plot in R