Thanks for your answer. The reason I want the data to be uniform: It's the
first step in a machine learning project I am working on. If I know the data
isn't uniformly distributed, then this means there is probably something
wrong and the following steps will be biased by the non-uniform input data.
I'm not checking an assumption for another statistical test.

Actually, the data has been normalized because it is supposed to represent a
probability distribution. That's why it sums to 1. My assumption is that,
for a vector of 5, the data at that point should look like 0.20 0.20 0.20
0.20 0.20, but of course there is variation, and I would like to test
whether the data comes close enough or not.

At the moment I am only testing whether there are more a's than b's in the
top and bottom portion of the each file (with a wilcoxon test, I have 8 reps
of the model I am trying to build). But that sort of felt like a very adhoc
solution and I figured maybe testing for uniformity would be better, or at
least a important addition. I've also been looking into testing for the
randomness of the sequence of a's and b's instead of the wilcoxon test,
although that may or may not involve R.

Kairavi.


> Yes, punif is the function to use, however the KS test (and the others)
are based on an assumption of independence, and if you know that your data
points sum to 1, then they are not independent (and not uniform if there are
more than 2).  Also note that these tests only rule out distributions (with
a given type I error rate), but cannot confirm that the data comes from a
given distribution (just that either they do, or there is not enough power
to distinguish between the actual and the test distributions).

> What is your ultimate question/goal?  Why do you care if the data is
uniform or not?

> --
> Gregory (Greg) L. Snow Ph.D.
> Statistical Data Center
> Intermountain Healthcare
> greg.snow@imail.org<https://webmail.uni-saarland.de/imp/message.php?mailbox=INBOX&index=81599#>
> 801.408.8111


[Hide Quoted Text]
-----Original Message-----
From: r-help-bounces@r-project.org<https://webmail.uni-saarland.de/imp/message.php?mailbox=INBOX&index=81599#>[mailto:
r-help-bounces@r-<https://webmail.uni-saarland.de/imp/message.php?mailbox=INBOX&index=81599#>
project.org] On Behalf Of Kairavi Bhakta
Sent: Friday, June 10, 2011 11:24 AM
To: r-help@r-project.org<https://webmail.uni-saarland.de/imp/message.php?mailbox=INBOX&index=81599#>
Subject: [R] Test if data uniformly distributed (newbie)

Hello,

I have a bunch of files containing 300 data points each with values from 0
to 1 which also sum to 1 (I don't think  the last element is relevant
though). In addition, each data point is annotated as an "a" or a "b".

I would like to know in which files (if any) the data is uniformly
distributed.

I used Google and found out that a Kolmogorov-Smirnov or a Chi-square
goodness-of-fit test could be used. Then I looked up ?kolmogorov and found
"ks.test", but the example there is for the normal distribution and I am not
sure how to adapt it for the uniform distribution. I did ?runif and read
about the uniform distribution but it doesn't say what the "cumulative
distribution" is. Is it "punif", like "pnorm"? I thought of that because I
found a message on this list where someone was told to use "pnorm" instead
of "dnorm". But the help page on the uniform distribution says punif is the
"distribution function". Are the "cumulative distribution" and the
"distribution function" the same thing? Having several names for the same
thing has always confused me very much in statistics.

Also, I am not sure whether I need to specify any parameters for the
distribution and which. I thought maybe I should specify "min=0" and "max=1"
but those appear to be the defaults. Do I need to specify q, the vector
of quantiles?

So is
ks.test(x, punif)
correct or not for what I am attempting to do?
After this I will also need to find out whether the a's and b's are
distributed randomly in each file. I would be greatful for any pointers
although I have not researched this issue yet.

Kairavi.

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