In this recipe, we will learn how to use lowess, which is a nonparametric model, and add the resulting prediction curve to a scatter plot.
For this recipe, we don't need to load any additional libraries. We just need to type the recipe in the R prompt or run it as a script.
First, let's create a simple scatter plot with the preloaded cars dataset and add a couple of lowess lines to it:
plot(cars, main = "lowess(cars)") lines(lowess(cars), col = "blue") lines(lowess(cars, f=0.3), col = "orange")
Standard R sessions include the lowess() function. It is a smoother that uses locally weighted polynomial regression. The first argument, in this instance, is a data frame called cars that gives the x and y variables (speed and dist). So we apply the lowess function to the cars dataset and in turn pass that result to the lines() function. The result of lowess is a list with components named x and y. The lines() function...