MC.samplemean {animation} | R Documentation |
Integrate a function from 0 to 1 using the Sample Mean Monte Carlo algorithm
MC.samplemean(FUN = function(x) x - x^2, n = ani.options("nmax"), col.rect = c("gray", "black"), adj.x = TRUE, ...)
FUN |
the function to be integrated |
n |
number of points to be sampled from the Uniform(0, 1) distribution |
col.rect |
colors of rectangles (for the past rectangles and the current one) |
adj.x |
should the locations of rectangles on the x-axis be adjusted? If TRUE , the rectangles will be laid side by side and it is informative for us to assess the total area of the rectangles, otherwise the rectangles will be laid at their exact locations.
|
... |
other arguments passed to rect
|
Sample Mean Monte Carlo integration can compute
I=int_0^1 f(x) dx
by drawing random numbers x_i from Uniform(0, 1) distribution and average the values of f(x_i). As n goes to infinity, the sample mean will approach to the expectation of f(X) by Law of Large Numbers.
The height of the i-th rectangle in the animation is f(x_i) and the width is 1/n, so the total area of all the rectangles is sum f(x_i) 1/n, which is just the sample mean. We can compare the area of rectangles to the curve to see how close is the area to the real integral.
A list containing
x |
the Uniform random numbers |
y |
function values evaluated at x |
n |
number of points drawn from the Uniform distribtion |
est |
the estimated value of the integral |
This function is for demonstration purpose only; the integral might be very inaccurate when n
is small.
Yihui Xie <http://yihui.name>
http://animation.yihui.name/compstat:sample_mean_monte_carlo
oopt = ani.options(interval = 0.2, nmax = 50) par(mar = c(4, 4, 1, 1)) ## when the number of rectangles is large, use border = NA MC.samplemean(border = NA)$est integrate(function(x) x - x^2, 0, 1) ## when adj.x = FALSE, use semi-transparent colors MC.samplemean(adj.x = FALSE, col.rect = c(rgb(0, 0, 0, 0.3), rgb(1, 0, 0)), border = NA) ## another function to be integrated MC.samplemean(FUN = function(x) x^3 - 0.5^3, border = NA)$est integrate(function(x) x^3 - 0.5^3, 0, 1) ## Not run: ## HTML animation page ani.options(interval = 0.5, title = "Sample Mean Monte Carlo Integration", description = "Generate Uniform random numbers and compute the average function values.") ani.start() MC.samplemean(n = 100, border = NA) ani.stop() ## End(Not run) ani.options(oopt)