How to Estimate a Polynomial Regression Model in R (Example Code)
This tutorial shows how to fit a polynomial regression model in R programming.
Creation of Example Data
data(iris) # Iris flowers as example data head(iris) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa |
data(iris) # Iris flowers as example data head(iris) # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa
Example: Fitting Third Order Polynomial Using poly() Function
lm(Sepal.Length ~ poly(Sepal.Width, 3), data = iris) # Use orthogonal polynomials # Call: # lm(formula = Sepal.Length ~ poly(Sepal.Width, 3), data = iris) # # Coefficients: # (Intercept) poly(Sepal.Width, 3)1 poly(Sepal.Width, 3)2 poly(Sepal.Width, 3)3 # 5.843 -1.188 -1.416 1.923 |
lm(Sepal.Length ~ poly(Sepal.Width, 3), data = iris) # Use orthogonal polynomials # Call: # lm(formula = Sepal.Length ~ poly(Sepal.Width, 3), data = iris) # # Coefficients: # (Intercept) poly(Sepal.Width, 3)1 poly(Sepal.Width, 3)2 poly(Sepal.Width, 3)3 # 5.843 -1.188 -1.416 1.923