Creating a frequency distribution from a randomised data set

Hi Guys, I have a data set containing clouds of points for three distinct groups. I am looking to shuffle the data set over the groups to create a distribution of random euclidean distances that I can then compare the true euclidean distance values to. Is there any function that can help me do this?

Cheers

[James Tursa]

The rand function is the obvious suggestion, but you need to provide more information about your problem (variables, sizes, calculations, etc) before we can suggest anything in more detail.

[Image Analyst]

OK, so you have three clusters of points. And each point has distances to every other point, both in it's same cluster and to those points in different clusters. Now, what are the "true" Euclidean distances and what are the "random" Euclidean distances? The distances are what they are . I don't understand how you have two types of distances. Why are some distances "true" and other distances "not true"? Just because you select two points at random does not make the distance between them not "true". Please clarify.

In addition to rand() you might take a look at randi() to get random indexes or integers, and randperm() to get integers that are guaranteed to have no repeats.

[Nathanael Walker-Hale]

Hi guys, sorry for being unclear, and thanks for your comments. I'm basically structuring this as I go along, so a lot of the theory might be a bit off. When I say the true euclidean distances I mean that of the unrandomised data set, which I've taken as the distance between the mean of the x and y values for each cloud (i.e. the approximate centre). Then I want to scramble the dataset, and get a new, random set of euclidean distances between those points. Then I want to repeat that however many times to get a distribution of randomised euclidean distances (using the values from within the dataset) which I can then compare the original values to.

[Nathanael Walker-Hale]

I have three groups of n = 30, and I'm really only concerned with two of the three possible euclidean distances - those between each of the two outer groups and the central one. Hope that helps.