Is "k" the count?
What are x and y? are both measured? Don't the two k's in the "exp" term cancel? Is there a reference? glen_b wrote: > > > 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@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-tp24798791p24839164.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@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.
