# Spline – basis functions

I am trying to wrap my head around splines and the concept of basis functions using the Elements of Statistical Learning. I understand that the goal is to find polynomials that are continuous at first and second derivatives. However, following the picture below, I don't understand whether

• a) the spline consists of a different cubic function $(a+bx+cx^2+dx^3)$ in each of the three regions, or
• b) whether the spline is the linear addition of the 6 basis functions per below across the entire domain, or
• c) whether there are 6 basis functions with different parameters in each of the 3 regions (hence 18 different functions). Much appreciated...

• this seems to belong to a math forum – Aksakal Mar 2 '14 at 21:20
• @Aksakal On what basis? (No pun intended). Splines are a useful and widespread tool for nonparametric regression and the example has been taken from a classic statistical text book. – M. Berk Mar 2 '14 at 22:40

This looks like a truncated power basis. The answer is b) although $h_5(X)$ will only be non-zero if $X$ is greater than $\xi_1$ and similarly for $h_6(X)$ and $\xi_2$
• @user1885116 bear in mind that ns produces a different basis to the one shown here as it's a natural B-spline rather than the truncated power basis. As for the dimensions of the output - what is your data, how many knots are you using and where are they placed? – M. Berk Mar 3 '14 at 19:01