You can use the following methods to extract p-values from the lm() function in R:

**Method 1: Extract Overall P-Value of Regression Model**

#define function to extract overall p-value of model overall_p <- function(my_model) { f <- summary(my_model)$fstatistic p <- pf(f[1],f[2],f[3],lower.tail=F) attributes(p) <- NULL return(p) } #extract overall p-value of model overall_p(model)

**Method 2: Extract Individual P-Values for Regression Coefficients**

summary(model)$coefficients[,4]

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

**Example: Extract P-Values 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

At the very bottom of the output we can see that the overall p-value for the regression model is **0.01396**.

If we would like to only extract this p-value from the model, we can define a custom function to do so:

#define function to extract overall p-value of model overall_p <- function(my_model) { f <- summary(my_model)$fstatistic p <- pf(f[1],f[2],f[3],lower.tail=F) attributes(p) <- NULL return(p) } #extract overall p-value of model overall_p(model) [1] 0.01395572

Notice that the function returns the same p-value as the model output from above.

To extract the p-values for the individual regression coefficients in the model, we can use the following syntax:

#extract p-values for individual regression coefficients in model summary(model)$coefficients[,4] (Intercept) points assists rebounds 0.002175313 0.022315418 0.208600183 0.178471275

Notice that the p-values shown here match the ones from the **Pr(> |t|)** column in the regression output above.

**Related:** How to Extract R-Squared from lm() Function in R

**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