Question

So let me start off by saying that I do not have the statistics toolbox for Matlab so I am trying to find a way to work around this. In any case, what I am trying to do is to replicate the R sample function. For example, in R

> x = sample(1:5,20,replace=T,prob=c(.1,.1,.1,.1,.6))
> x
 [1] 5 5 5 4 5 2 5 5 1 5 5 5 5 5 5 3 5 1 5 5

so I am sampling the integers 1,2,3,4,5 with replacement. But furthermore, I am sampling each integer with a certain proportion, i.e., the integer 5 should be sampled about 60% of the time.

So my question that I would like to find a solution to is how to achieve this in Matlab?

Answer 1

Here’s how you can perform weighted sampling with replacement (something Matlab’s randsample doesn’t support, btw);

function r = sample(pop,n,weights)
%# each weight creates a "bin" of defined size. If the value of a random number
%# falls into the bin, we pick the value

%# turn weights into a normed cumulative sum
csWeights = cumsum(weights(:))/sum(weights);
csWeights = [0;csWeights(1:end-1)];

%# for each value: pick a random number, check against weights
idx = sum(bsxfun(@ge,rand(1,n),csWeights),1);

r = pop(idx);