[R] fitting a truncated power law

[glen_b]

Let me rephrase. You have some counts. You have some other measurement or
measurements. Presumably you are trying to predict (fit) expected count in
terms of the measurements. Can you identify which variable is the count and
how your model describes the expected count?

Glen


glen_b wrote:
> 
> 
> Hang on, now I'm very confused.  What is the information you have
> collected? Is it x and y? k and x? which one is the count?
> 
> 
> John Sanders-2 wrote:
>> 
>> The function I'm trying to fit has the form:
>> 
>> P(k)
>> ~ k^(-y) exp (– k ⁄ kx) 
>> 
>> And deals with count data. I'm a newbie, so any more specific suggestion
>> would be greatly appreciated.
>> 
>> John Sanders-2 wrote:
>>>
>>> How can I fit a truncated power law to a vector? I can't find a function
>>> to do that. If the function provides an AIC, even better.
>>>
>> 
>> Okay, "power law" I understand - f(x) = k.x^a, or on the log-scale
>> log(f(x))
>> = log(k) + a log(x) (linear)
>> 
>> I was unfamiliar with the term "truncated power law", but after looking
>> on
>> the internet I see that the term implies what appears to be replacing the
>> linear fit with a linear spline fit to log(y) in terms of log(x)  - but
>> the
>> usual application seems to be to fit probability distribution to count
>> data;
>> in this case you fit essentially a two-part Pareto distribution (or Zipf
>> if
>> the variable is discrete) - again the log-fitted-density is like a linear
>> spline in the logs.
>> 
>> Is the vector of data you have counts to which you wish to fit a
>> distribution, or is it a set of measurements?
>> 
>> If I understand the problem correctly, I think it could probably be done
>> using linear splines with GLMs, which can be done in a couple of
>> packages.
>> 
>> 
>> 
>> 
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>> 
>> 
> 
> 

-- 
View this message in context: http://www.nabble.com/fitting-a-truncated-power-law-tp24798791p24819300.html
Sent from the R help mailing list archive at Nabble.com.