So the question really has two parts:
1) How to fit polynomials
2) How to join polynomial segments in such a way as to make them continuous at join points
1) How to fit polynomials
The "easy" way is simply to put $1,x,x^2,x^3$ etc as predictors in the regression equation.
For example in R, one might use lm( y ~ x + I(x^2) ) or even
x2 <- x^2
lm( y ~ x + x2)
to fit a quadratic.
However, care is required, because as $x$ becomes large in magnitude, the powers of $x$ all become more and more correlated and multicollinearity becomes a problem.
A simple approach that helps quite well for low order polynomials is to center the x's (and possibly, scale them) before taking powers:
for example in R, one might use lm( y ~ (x-k) + I((x-k)^2)+ I((x-k)^3) ) or even
x1 <- x-k
x2 <- x1^2
x3 <- x1^3
lm( y ~ x1 + x2 + x3)
where $k$ would often be chosen to be a value close to the mean of $x$.
(The shift on the linear term isn't strictly necessary, but it's useful for the next question.)
A more stable approach is to use orthogonal polynomials. In R: lm( y ~ poly(x,2) ) but that doesn't as directly suit our purposes here.
So here's an example fit of a quadratic in R:
carfit <- lm(dist~speed+I(speed^2),cars)
plot(dist~speed,cars)
points(cars$speed,fitted(carfit),col="magenta",pch=16)
and here's an example of a fit with the x shifted to be centered near the mean of x:
x1 <- cars$speed-15
x2 <- x1^2
carfit2 <- lm(dist~x1+x2,cars)
plot(dist~speed,cars)
points(cars$speed,fitted(carfit2),col="blue",pch=16)
points(cars$speed,fitted(carfit),col="magenta",pch=16,cex=0.7)
Here the second fit is in blue and the original fit is in magenta over the top (drawn a little smaller so you can see a little of the blue). As you see, the fits are coincident.
2) How to join polynomial segments in such a way as to make them continuous at join points
Here, we do something with that "shift" of the various terms that in (1) was used for centering.
First let's do a simple case. Imagine I have just two segments and I am just fitting straight lines (I realize you can do this case but it's the basis for the more complex ones).
a) No attempt to make them join at a particular x-value:
Here you just fit each regression on its own. The two lines will cross somewhere, but they won't be continuous at your specified join point.
Example: Here's some x and y values:
x y
1 1.540185
2 4.051166
3 5.621000
4 7.752237
5 10.700486
6 10.103224
7 12.150661
8 10.982853
9 11.108116
10 14.993672
The segments are $x\leq 5$ and $x>5$, say.
If we fit lines to the data in those segments, we get:
Which we see is discontinuous at x=5 and actually crosses way back near x=4. In this case we could improve things slightly by including the point at x=5 in both, but it doesn't actually solve the underlying problem.
2) continuous segments joining at $x=k$.
The easiest way to get the two to meet at 5 is to put all ten points into one regression with two predictors, one which is $x$ and the second which leaves the line unaltered in the left half, but changes it after 5. We do that by centering a copy of x at 5, and then zeroing out its left half (which we will denote by $(x-5)_+$, the "$+$" indicating that we only retain the positive part of $x-5$, and otherwise set it to 0. This will make the fit from the second predictor 0 in the left half, and then linearly increasing from 0 in the right half:
x (x-5)+
1 0
2 0
3 0
4 0
5 0
6 1
7 2
8 3
9 4
10 5
We then fit a multiple linear regression with both predictors, which gives the segmented fit:
This is called a linear spline fit with a knot at 5.
If you want continuous and smooth (continuous first and second derivatives), you should investigate cubic regression splines. They work very much in this vein and are widely used.
c) polynomial segments
Let's start with a simple case: linear in the left half, quadratic in the right half. Here we can just add an additional term for the quadratic in the right half, $((x-5)_+)^2$. The "+" zeroes it out below 5, so there's still only a line in the left half:
x (x-5)+ ((x-5)+)^2
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 1 1
7 2 4
8 3 9
9 4 16
10 5 25
As you see, linear on the left, quadratic on the right. You can as easily add more quadratic segments to the right using the same trick (by replacing the '5' knot in $(x-5)_+$ with whatever the next knot-value is).
Problem: It's a bit tricky dropping down a degree to the right, because you need to impose a constraint. If your degree is monotonic non-increasing across segments (that is, only goes down you can do it as above by running "backwards" (zero out to the right of the knot and then add terms as required to the left).
If you need the degree to step up and down in any fashion, in the simpler cases you can just impose the required constraints algebraically and calculate the required predictors (if you have a regression function that handles constraints you could impose them that way and save some effort). If you have a lot of predictors, probably the best way to approach it is to modify the approach of B-splines. There are a lot of possible things you might try to do, so it's a bit hard to anticipate.
Hopefully this is sufficient to get you started, anyway.
Another possible shortcut occurs to me that might work in some cases, which is to use a sequence of natural splines (or more generally, a modified version of the approach of natural splines). For example, natural cubic splines reduce the degree to linear after the last knot, so it may be possible to string several natural splines to together over disjoint subsets in such a way that transitions between cubic spline and linear spline sections as smoothly as possible.
Best Answer
Sure. You just use an interaction term with the dummy variable for break (0 before break, 1 after break) -->
variable*dummyBut are you sure you want to do this? You have the domain knowledge, but it is sort of like including the interaction without the main effect. Most would dovariable + variable*dummy, this does make interpretation harder but usually seems reasonable to assume there is some effect prior to breakpointAs far as I remember the
segmentedpackage allows you to do both. In my mind both are reasonable. If it is easy would prefer to do the latter, but often doesn't matter.I like piece-wise regression. But if you're trying to incorporate non-linearity in your model, some prefer GAM or restricted cubic splines. If this is for publication, I'd look at what the standard is in your field.