understanding derivatives of a regression spline

Question

I am trying to understand why regression splines are continuous at their knots

Suppose I am fitting a regression spline

$$ E[Y|X] = \alpha + \beta_1 x + \beta_2 (x - t)^+ $$

where $(x - t)^+ = \max (x-t,0)$.

Now, if I take the partial derivative of $y$ wrt $x$, I find

$$ \frac{\partial y}{\partial x} = \begin{cases} \beta_1 \quad \text{if} \quad x \leq t,\\ \beta_1 + \beta_2 \quad \text{if} \quad x > t \end{cases} $$

For starters, is this correct?

If it is, I don't see how this shows the derivative at $t$ is continuous - it looks like there is a discrete change rather than smooth change

Answer 1

You correctly found the derivative.

With linear splines, the function is continuous but its first derivative is not; there's a jump discontinuity at each knot.