[R] R-help Time Series

[(Ted Harding)]

On 26-Feb-05 Uwe Ligges wrote:
> So let's be immensely unfair and do some speculation ...
> 
> Assuming 1000MB/month means a compressed archive file of (very) 
> *roughly* 250MB.
> 
> Looking at the data with linear models,
>     lm(sqrt(MB) ~ monthindex)
> seems not to be the worst model (removing the first observation,
> perhaps).

Well, Uwe, disregarding the confusion over units, your model
(and caveat) coincides with mine, leading to

  KB = (2.448731+0.282701*T + noise)^2

where T is in months and the first observation is at T=1.

(Actually, I think the initial "burn-in" might be a bit longer,
say over 3-4 months, and there is a slight suggestion that the
growth has been slightly flattening out recently).

And what intrigued me is the question: what mechanism might
lead to a quadratic growth law?

One possible interpretation is the following:

Suppose that the number of postings is proportional to the
number of R users. A quadratic has first difference linear in T.
So the average number of additional postings per month can be
seen as a sum of two components:

a) a constant "kernel"
b) a component proportional to the increment in postings
   in the previous month.

Interpreting this as number of users, it could suggest that
recruitment to R could be due to two causes: recruitment by
a "core" of fixed size, and recruitment by recent recruits!

Of course this is far from the only possibility. Another might
be that postings to R-help reflect the number of issues that
users are concerned to get help or information on.

This might reflect:

a) a "core" of die-hard FAQs asked by more and more people;
b) a growing corpus of packages which more and more people
   need guidance with.

And so on. An essential missing piece of data (where I'm
concerned) is the sequence of numbers of subscribers to
R-help.

Ted.


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E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk>
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Date: 26-Feb-05                                       Time: 14:00:46
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