Table of Contents
To plot the results from a linear model in R, you can use the plot() function. This function takes the linear model object as an argument and creates a scatterplot with a regression line that shows the relationship between the two variables. Additionally, you can use the abline() function to draw confidence intervals around the regression line. Finally, you can use the summary() function to get a summary of the linear model’s results.
You can use the following methods to plot the results of the lm() function in R:
Method 1: Plot lm() Results in Base R
#create scatterplot plot(y ~ x, data=data) #add fitted regression line to scatterplot abline(fit)
Method 2: Plot lm() Results in ggplot2
library(ggplot2) #create scatterplot with fitted regression line ggplot(data, aes(x = x, y = y)) + geom_point() + stat_smooth(method = "lm")
The following examples shows how to use each method in practice with the built-in in R.
Example 1: Plot lm() Results in Base R
The following code shows how to plot the results of the lm() function in base R:
#fit regression model
fit <- lm(mpg ~ wt, data=mtcars)
#create scatterplot
plot(mpg ~ wt, data=mtcars)
#add fitted regression line to scatterplot
abline(fit)
The points in the plot represent the raw data values and the straight diagonal line represents the fitted regression line.
Example 2: Plot lm() Results in ggplot2
The following code shows how to plot the results of the lm() function using the data visualization package:
library(ggplot2)
#fit regression model
fit <- lm(mpg ~ wt, data=mtcars)
#create scatterplot with fitted regression line
ggplot(mtcars, aes(x = x, y = y)) +
geom_point() +
stat_smooth(method = "lm")
The blue line represents the fitted regression line and the grey bands represent the 95% confidence interval limits.
To remove the confidence interval limits, simply use se=FALSE in the stat_smooth() argument:
library(ggplot2)
#fit regression model
fit <- lm(mpg ~ wt, data=mtcars)
#create scatterplot with fitted regression line
ggplot(mtcars, aes(x = x, y = y)) +
geom_point() +
stat_smooth(method = "lm", se=FALSE)
You can also add the fitted regression equation inside the chart by using the stat_regline_equation() function from the ggpubr package:
library(ggplot2)
library(ggpubr)
#fit regression model
fit <- lm(mpg ~ wt, data=mtcars)
#create scatterplot with fitted regression line
ggplot(mtcars, aes(x = x, y = y)) +
geom_point() +
stat_smooth(method = "lm", se=FALSE) +
stat_regline_equation(label.x.npc = "center")
The following tutorials explain how to perform other common tasks in R: