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