A linear relationship between two variables
y is one of the most common, effective and easy assumptions to make when trying to figure out their relationship. Sometimes however, the true underlying relationship is more complex than that, and this is when polynomial regression comes in to help.
Let see an example from economics: Suppose you would like to buy a certain quantity
q of a certain product. If the unit price is
p, then you would pay a total amount
y. This is a typical example of a linear relationship. Total price and quantity are directly proportional. To plot it we would write something like this:
p <- 0.5 q <- seq(0,100,1) y <- p*q plot(q,y,type='l',col='red',main='Linear relationship')
Now, this is a good approximation of the true relationship between
q, however when buying and selling we might want to consider some other relevant information, like: Buying significant quantities it is likely that we can ask and get a discount, or buying more and more of a certain good we might be pushing the price up.
This can lead to a scenario like this one where the total cost is no longer a linear function of the quantity:
y <- 450 + p*(q-10)^3 plot(q,y,type='l',col='navy',main='Nonlinear relationship',lwd=3)
First, always remember use to
set.seed(n) when generating pseudo random numbers. By doing this, the random number generator generates always the same numbers.
Predictor (q). Use seq for generating equally spaced sequences fast
q <- seq(from=0, to=20, by=0.1)
Value to predict (y):
y <- 500 + 0.4 * (q-10)^3
Some noise is generated and added to the real signal (y):
noise <- rnorm(length(q), mean=10, sd=80) noisy.y <- y + noise
Plot of the noisy signal:
plot(q,noisy.y,col='deepskyblue4',xlab='q',main='Observed data') lines(q,y,col='firebrick1',lwd=3)
This is the plot of our simulated observed data. The simulated datapoints are the blue dots while the red line is the signal (signal is a technical term that is often used to indicate the general trend we are interested in detecting).
Our model should be something like this: y = a*q + b*q2 + c*q3 + cost
Let’s fit it using R. When fitting polynomials you can either use
model <- lm(noisy.y ~ poly(q,3))
model <- lm(noisy.y ~ x + I(X^2) + I(X^3))
However, note that
I(q^3) will be correlated and correlated variables can cause problems. The use of
poly() lets you avoid this by producing orthogonal polynomials, therefore I’m going to use the first option.
summary(model) Call: lm(formula = noisy.y ~ poly(q, 3)) Residuals: Min 1Q Median 3Q Max -212.326 -51.186 4.276 61.485 165.960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 513.615 5.602 91.69 <2e-16 *** poly(q, 3)1 2075.899 79.422 26.14 <2e-16 *** poly(q, 3)2 -108.004 79.422 -1.36 0.175 poly(q, 3)3 864.025 79.422 10.88 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 79.42 on 197 degrees of freedom Multiple R-squared: 0.8031, Adjusted R-squared: 0.8001 F-statistic: 267.8 on 3 and 197 DF, p-value: 0
By using the
confint() function we can obtain the confidence intervals of the parameters of our model.
Confidence intervals for model parameters:
confint(model, level=0.95) 2.5 % 97.5 % (Intercept) 502.5676 524.66261 poly(q, 3)1 1919.2739 2232.52494 poly(q, 3)2 -264.6292 48.62188 poly(q, 3)3 707.3999 1020.65097
Plot of fitted vs residuals. No clear pattern should show in the residual plot if the model is a good fit
Overall the model seems a good fit as the R squared of 0.8 indicates. The coefficients of the first and third order terms are statistically significant as we expected. Now we can use the
predict() function to get the fitted values and the confidence intervals in order to plot everything against our data.
Predicted values and confidence intervals:
predicted.intervals <- predict(model,data.frame(x=q),interval='confidence', level=0.99)
Add lines to the existing plot:
lines(q,predicted.intervals[,1],col='green',lwd=3) lines(q,predicted.intervals[,2],col='black',lwd=1) lines(q,predicted.intervals[,3],col='black',lwd=1)
Add a legend:
legend("bottomright",c("Observ.","Signal","Predicted"), col=c("deepskyblue4","red","green"), lwd=3)
A word of caution: Polynomials are powerful tools but might backfire: in this case we knew that the original signal was generated using a third degree polynomial, however when analyzing real data, we usually know little about it and therefore we need to be cautious because the use of high order polynomials (n > 4) may lead to over-fitting. Over-fitting happens when your model is picking up the noise instead of the signal: even though your model is getting better and better at fitting the existing data, this can be bad when you are trying to predict new data and lead to misleading results.
A gist with the full code for this example can be found here.
Thank you for reading this post, leave a comment below if you have any question.