I’ve been playing around with using R for simulations recently, and I ran into a use case that required I sample from a skewed distribution with min and max values of 0 and 1. A beta distribution was a nice representative form (http://en.wikipedia.org/wiki/Beta_distribution#Mode), and base R has a built in rbeta function; however, I needed to be able to feed into rbeta the proper alpha and beta parameters to produce the required distribution. So, I created two simple R functions that allowed me to start with an alpha value (take your pick) and either a target mean or mode, and the function produced the appropriate beta that I could then use to produce a beta distribution and my random sample.

# first by mean a1 <- 20 mu1 <- .95 solmean <- function(alpha, mean) (alpha-(mean*alpha))/mean beta <- solmean(a1, mu1) x1 <- (rbeta(1000, shape1=a1, shape2=beta)) # sample of n=1000 from the beta dist head(x1) x1 <- round(x1,3) hist(x1, breaks=100)# let's give it a look mean(x1) x2 <- table(x1) which.max(x2) # let's see what the mode is quantile(x1) # more descriptives # Now by mode a2 <- 20 mo1 <- .95 solmode <- function(alpha, mode) (alpha-1-(mode*alpha)+(2*mode))/mode beta <- solmode(a2, mo1) x1 <- (rbeta(1000, shape1=a2, shape2=beta)) # sample of n=1000 from the beta dist head(x1) x1 <- round(x1,3) hist(x1, breaks=100)# let's give it a look mean(x1) x2 <- table(x1) which.max(x2) # let's see what the mode is quantile(x1) # more descriptives ## Here's the code I used to make the plots that are above: set.seed(123) # Now by mode a2 <- 20 mo1 <- .95 solmode <- function(alpha, mode) (alpha-1-(mode*alpha)+(2*mode))/mode beta <- solmode(a2, mo1) x1 <- (rbeta(1000, shape1=a2, shape2=beta)) # sample of n=1000 from the beta dist head(x1) x1 <- round(x1,3) xlimits <- pretty(range(x1))[c(1,length(pretty(range(x1))))] hist(x1, breaks=100, xlim=xlimits, main="")# let's give it a look test <- (x1, breaks=100, xlim=xlimits, main="") u <- par("usr") rect(u[1], u[3], u[2], u[4], col = "gray88", border = "black") abline(v = pretty((range(x1))), col='white') ## draw h lines abline(h = pretty(test$counts), col='white') ## draw h lines par(new=T) hist(x1, breaks=100, xlim=xlimits, col='darkgrey', main=paste('Beta Distr. with mode=', mo1, 'and alpha=', a2))# let's give it a look # Now by mode a2 <- 40 mo1 <- .95 solmode <- function(alpha, mode) (alpha-1-(mode*alpha)+(2*mode))/mode beta <- solmode(a2, mo1) x2 <- (rbeta(1000, shape1=a2, shape2=beta)) # sample of n=1000 from the beta dist head(x2) x2 <- round(x2,3) hist(x2, breaks=50, xlim=xlimits, main="")# let's give it a look test <- hist(x2, breaks=50, xlim=xlimits, main="") u <- par("usr") rect(u[1], u[3], u[2], u[4], col = "gray88", border = "black") abline(v = pretty((range(x1))), col='white') ## draw h lines abline(h = pretty(test$counts), col='white') ## draw h lines par(new=T) hist(x2, breaks=50, xlim=xlimits, col='darkgrey', main=paste('Beta Distr. with mode=', mo1, 'and alpha=', a2))# let's give it a look # Now by mean a3 <- 40 mu1 <- .95 solmean <- function(alpha, mean) (alpha-(mean*alpha))/mean beta <- solmean(a3, mu1) x3 <- (rbeta(1000, shape1=a1, shape2=beta)) # sample of n=1000 from the beta dist head(x3) mean(x3) x3 <- round(x3,3) hist(x3, breaks=50, xlim=xlimits, main="")# let's give it a look test <- hist(x3, breaks=50, xlim=xlimits, main="") u <- par("usr") rect(u[1], u[3], u[2], u[4], col = "gray88", border = "black") abline(v = pretty((range(x1))), col='white') ## draw h lines abline(h = pretty(test$counts), col='white') ## draw h lines par(new=T) hist(x3, breaks=50, xlim=xlimits, col='darkgrey', main=paste('Beta Distr. with mean=', mu1, 'and alpha=', a3))# let's give it a look

That’s it. I’ve noticed that changing your prespecified alpha does some interesting things to the kurtosis of the resulting beta distribution, but it’s up to you to play around with things until you get a distribution that suits your needs!

One last thing… if you are really into fat-tailed distributions (let’s say your a black swan fan), and you really don’t require a “curvy” form. One great way to sample from a skewed distribution that is realtively easy to specify (in terms of a mode–that is), I’ve found that the triangle package (http://cran.r-project.org/web/packages/triangle/index.html) a nice way to go…. and the code is super easy to run.

require(triange) tri <- round(rtriangle(10000, 0, 1, .85),3) # just feed it the sample size, min bound, max bound, and mode... and voila! hist(tri)

ENJOY!

I need some help understanding rbeta. My problem is as following…

Use simulation to approximate P(S <=10), by simulating 100,000 of each of X1;…… ;X20. Note that Xi have a beta distribution. Give your approximation value here.

I need some help with rbeta. Following is my problem…

Problem: Use simulation to approximate P(S <=10), by simulating 100,000 of each of X1; ;X20. Note that Xi have a beta distribution. Give your approximation value here.