You may want to study this article:
https://pdfs.semanticscholar.org/574f/8f7347a258f6431fb8316716ca51b9b7eacb.pdf

it should give you a clue or two of what you may want to implement.


-----Original Message-----
From: users [mailto:users-boun...@lists.scilab.org] On Behalf Of Heinz
Sent: Friday, February 09, 2018 7:44 PM
To: 'Users mailing list for Scilab' <users@lists.scilab.org>
Subject: Re: [Scilab-users] spatial correlation coefficient: Is the MATLAB 
corrcoef function doing the job?

1       The nearest neighbour distribution of N points in a 3d-Volume with
radius R is given by
W(r) = 1 - exp[ - N (r/r)^3] and this is a Weibull distribution and not a 
Poisson distribution. The WIKI article, while correct, is a typical case where 
high level statisticians are trying to make simple things look very complicated.

2       Comparing the experimental distribution with the above prediction is
made complicated as the manufactured object has small hard spheres that push 
each other apart in the manufacturing process and the Weibull is modified 
anyway.

3       I am sill searching for a 3d spatial characterization coefficient
that differentiates between purely random arrangements and gradual regular 
arrangements.

Heinz

-----Original Message-----
From: users [mailto:users-boun...@lists.scilab.org] On Behalf Of Rafael Guerra
Sent: 09 February 2018 10:17
To: Users mailing list for Scilab <users@lists.scilab.org>
Subject: Re: [Scilab-users] spatial correlation coefficient: Is the MATLAB 
corrcoef function doing the job?


Q: "I have 10,000 xyz data and want to know, if there is some regularity in 
them or if they are more or less random"
A:  look for tests of spatial randomness 
(https://en.wikipedia.org/wiki/Complete_spatial_randomness ) . If The 
experimental nearest neighbor distribution obtained previously can be compared 
to the expected Poisson distribution.


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