In a previous article I discussed the situation where
you have a sequence of (*x,y*) points and you want to find the area under the curve that is defined by those points.
I pointed out that usually you need to use statistical modeling before it makes sense to compute the area.

However, there is a numerical technique that is very useful for a wide range of numerical integration scenarios, and that is the trapezoidal rule.

The following graph illustrates the trapezoidal rule. Given a set of points
(*x _{1}, y_{1}*),
(

*x*), ..., (

_{2}, y_{2}*x*), with

_{n}, y_{n}*x*≤

_{1}*x*≤ ... ≤

_{2}*x*, the trapezoidal rule computes the area of the piecewise linear curve that passes through the points. You can compute the area under the piecewise linear segments by summing the area of the trapezoids A1, A2, A3, and A4. (Sometimes a trapezoid is degenerate and is actually a rectangle or a triangle.)

_{n}### Implementing the Trapezoidal Rule in SAS/IML Software

It is easy to use SAS/IML software (or the SAS DATA step) to implement the trapezoidal rule.
The area of a trapezoid defined by
(*x _{i}, y_{i}*) and
(

*x*) is

_{i+1}, y_{i+1}The first term is just the width of the subinterval [x( x_{i+1}– x_{i}) ( y_{i}+ y_{i+1}) / 2

_{i}, x

_{i+1}] and the second term is the average of the heights at each end of the subinterval.

The following user-defined function computes the area under the piecewise linear segments that connect the points. The function does not check that the `x` values are sorted in nondecreasing order.

proc iml; /**Given two vectors x,y where y=f(x), this module approximates the definite integral int_a^b f(x) dx by the trapezoid rule. The vector x is assumed to be in numerically increasing order so that a=x[1] and b=x[nrow(x)]. The module does not assume equally spaced intervals. The formula is Integral = Sum( (x[i+1] - x[i]) * (y[i] + y[i+1])/2 ) **/ start TrapIntegral(x,y); N = nrow(x); dx = x[2:N] - x[1:N-1]; meanY = ( y[2:N] + y[1:N-1] )/2; return( dx` * meanY ); finish; /** test it **/ x = {0.0, 0.2, 0.4, 0.8, 1.0}; y = {0.5, 0.8, 0.9, 1.0, 1.0}; area = TrapIntegral(x,y); print area; |

Notice that the implementation does not require equally spaced points. The width of all subintervals are computed in a single statement and assigned to the vector `dx`. Similarly, the average of the heights are computed in a single statement and assigned to the vector `meanY`. The summation of all the areas is then computed by using a dot product of vectors. (Equivalently, the module could also return the quantity `sum(dx # meanY)`, but the dot product is the more efficient computation.)

The simplicity of the trapezoidal rule makes it an ideal for many numerical integration tasks. Also, the trapezoidal rule is *exact* for piecewise linear curves such as an ROC curve.
Also, as John D. Cook points out, there are other situations in which the trapezoidal rule performs more accurately than other, fancier, integration techniques.

The trapezoidal rule is not as accurate as Simpson's Rule when the underlying function is smooth, because Simpson's rule uses quadratic approximations instead of linear approximations. The formula is usually given in the case of an odd number of equally spaced points. Leave a comment to discuss the relative advantages and disadvantages of Simpson's rule as compared to the trapezoidal rule.

In a future blog post, I will use the `TrapIntegral` function to integrate some functions that arise in statistical data analysis.

## 8 Comments

Fantastic work! I applied it in my daily routine immediately. Thanks a lot.

Pingback: A statistical application of numerical integration: The area under an ROC curve - The DO Loop

Pingback: The area under a density estimate curve: Nonparametric estimates - The DO Loop

If you could state the strengths and limitations of both simpson's rule and trapezodial rule that would be greatly appreciated. However, the work already posted is great.

The strength of the trapezoidal rule is that it is fast and it is exact for piecewise linear functions. The strength of Simpson's rule is that it is usually more accurate: it has a smaller error when integrating smooth functions. The limitations for both algorithms is that they are designed for finite intervals. They are also require that you choose the number of subdivisions in advance, which is why I use the QUAD subroutine in SAS/IML software for serious work. The QUAD subroutine implements an adaptive Romberg-type algorithm that is is more accurate than Simposon's Rule and can work on infinite domains.

I need some help on the application of trapezoidal rule or simpson rule in mining metallurgy, where exactly can we apply these two rules?

These rules enable you to numerically approximate an integral. Integrals appear in many areas of material science, including loads, shears, stresses, torsion, etc. See any engineering or calculus textbook.

Pingback: An easy way to approximate a cumulative distribution function - The DO Loop