How to Get R-Squared and Adjusted R-Squared From lm in R

Renesh Bedre    2 minute read

get R-squared from lm

When you perform a regression analysis using the lm() function, you can get the summary statistics of regression model using the summary() function.

The summary statistics give detailed information for fitted regression model including model formula, regression coefficients, residuals, and other statistical information such as standard error and R-Squared.

Sometimes, we are interested to only getting R-Squared and adjusted R-Squared values instead of getting detailed summary statistics.

You can use the following syntax to get R-Sqaured and adjusted R-Squared from summary statistics.

# R-Sqaured
summary(model)$r.squared

# adjusted R-Sqaured
summary(model)$adj.r.squared

The multiple regression example below illustrates how to get the R-Squared and adjusted R-Squared values based on the fitted model.

Load blood pressure example dataset to fit the regression model,

df = read.csv("https://reneshbedre.github.io/assets/posts/reg/bp.csv")

# view first 5 rows
head(df)
   BP Age Weight  BSA Dur Pulse Stress
1 105  47   85.4 1.75 5.1    63     33
2 115  49   94.2 2.10 3.8    70     14
3 116  49   95.3 1.98 8.2    72     10
4 117  50   94.7 2.01 5.8    73     99
5 112  51   89.4 1.89 7.0    72     95
6 121  48   99.5 2.25 9.3    71     10

Fit the multiple regression model using the lm() function with BP as a dependent variable,

model <- lm(BP ~ ., data = df)

# summary statistics
summary(model)

# output
Call:
lm(formula = BP ~ ., data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.93213 -0.11314  0.03064  0.21834  0.48454 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -12.870476   2.556650  -5.034 0.000229 ***
Age           0.703259   0.049606  14.177 2.76e-09 ***
Weight        0.969920   0.063108  15.369 1.02e-09 ***
BSA           3.776491   1.580151   2.390 0.032694 *  
Dur           0.068383   0.048441   1.412 0.181534    
Pulse        -0.084485   0.051609  -1.637 0.125594    
Stress        0.005572   0.003412   1.633 0.126491    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4072 on 13 degrees of freedom
Multiple R-squared:  0.9962,	Adjusted R-squared:  0.9944 
F-statistic: 560.6 on 6 and 13 DF,  p-value: 6.395e-15

The summary statistics provide values of R-squared (0.9962) and adjusted R-squared (0.9944) along with other regression model statistics. The R-squared and adjusted R-squared are used for checking the regression model performance.

The adjusted R-squared value helps interpret the performance of the multiple regression model as it corrects for sample size and regression coefficients.

If you only want to get R-squared and adjusted R-squared from summary statistics, you can extract those values using the $ operator.

Extract R-squared,

# R-Sqaured
summary(model)$r.squared
0.9961503

Extract adjusted R-squared

summary(model)$adj.r.squared
0.9943734

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