You can use the following methods to extract regression coefficients from the lm() function in R:

**Method 1: Extract Regression Coefficients Only**

model$coefficients

**Method 2: Extract Regression Coefficients with Standard Error, T-Statistic, & P-values**

summary(model)$coefficients

The following example shows how to use these methods in practice.

**Example: Extract Regression Coefficients 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 view the regression coefficients only, we can use **model$coefficients** as follows:

#view only regression coefficients of model model$coefficients (Intercept) points assists rebounds 66.435519 1.215203 -2.596789 2.820224

We can use these coefficients to write the following fitted regression equation:

Rating = 66.43551 + 1.21520(points) – 2.59678(assists) + 2.82022(rebounds)

To view the regression coefficients along with their standard errors, t-statistics, and p-values, we can use **summary(model)$coefficients** as follows:

#view regression coefficients with standard errors, t-statistics, and p-values summary(model)$coefficients Estimate Std. Error t value Pr(>|t|) (Intercept) 66.435519 6.6931808 9.925852 0.002175313 points 1.215203 0.2787838 4.358942 0.022315418 assists -2.596789 1.6262899 -1.596757 0.208600183 rebounds 2.820224 1.6117911 1.749745 0.178471275

We can also access specific values in this output.

For example, we can use the following code to access the** p-value** for the **points** variable:

#view p-value for points variable summary(model)$coefficients["points", "Pr(>|t|)"] [1] 0.02231542

Or we could use the following code to access the p-value for each of the regression coefficients:

#view p-value for all variables summary(model)$coefficients[, "Pr(>|t|)"] (Intercept) points assists rebounds 0.002175313 0.022315418 0.208600183 0.178471275

The p-values are shown for each regression coefficient in the model.

You can use similar syntax to access any of the values in the regression output.

**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