Often you may want to plot the predicted values of a regression model in R in order to visualize the differences between the predicted values and the actual values.

This tutorial provides examples of how to create this type of plot in base R and ggplot2.

**Example 1: Plot of Predicted vs. Actual Values in Base R**

The following code shows how to fit a multiple linear regression model in R and then create a plot of predicted vs. actual values:

#create data df <- data.frame(x1=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12), x2=c(6, 6, 7, 7, 8, 9, 11, 13, 14, 14), y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36)) #fit multiple linear regression model model <- lm(y ~ x1 + x2, data=df) #plot predicted vs. actual values plot(x=predict(model), y=df$y, xlab='Predicted Values', ylab='Actual Values', main='Predicted vs. Actual Values') #add diagonal line for estimated regression line abline(a=0, b=1)

The x-axis displays the predicted values from the model and the y-axis displays the actual values from the dataset. The diagonal line in the middle of the plot is the estimated regression line.

Since each of the data points lies fairly close to the estimated regression line, this tells us that the regression model does a pretty good job of fitting the data.

We can also create a data frame that shows the actual and predicted values for each data point:

#create data frame of actual and predicted values values <- data.frame(actual=df$y, predicted=predict(model)) #view data frame values actual predicted 1 22 22.54878 2 24 23.56707 3 24 23.96341 4 25 24.98171 5 25 25.37805 6 27 26.79268 7 29 28.60366 8 31 30.41463 9 32 33.86585 10 36 34.88415

**Example 2: Plot of Predicted vs. Actual Values in ggplot2**

The following code shows how to create a plot of predicted vs. actual values using the ggplot2 data visualization package:

library(ggplot2) #create data df <- data.frame(x1=c(3, 4, 4, 5, 5, 6, 7, 8, 11, 12), x2=c(6, 6, 7, 7, 8, 9, 11, 13, 14, 14), y=c(22, 24, 24, 25, 25, 27, 29, 31, 32, 36)) #fit multiple linear regression model model <- lm(y ~ x1 + x2, data=df) #plot predicted vs. actual values ggplot(df, aes(x=predict(model), y=y)) + geom_point() + geom_abline(intercept=0, slope=1) + labs(x='Predicted Values', y='Actual Values', title='Predicted vs. Actual Values')

Once again, the x-axis displays the predicted values from the model and the y-axis displays the actual values from the dataset.

**Additional Resources**

How to Create a Residual Plot in R

How to Create a Histogram of Residuals in R

How to Calculate Standardized Residuals in R