See the methods here:
http://arxiv.org/abs/cond-mat/0412004
You can simply use the formula given here for continuous data.
Discrete data is a bit more tricky, this is the function I'm using:
# -------------------------------------------------
power.law.fit <- function(x, xmin=NULL, start=2, ...) {
if (length(x) == 0) {
error("zero length vector")
}
if (length(x) == 1) {
error("vector should be at least of length two")
}
require(stats4)
if (is.null(xmin)) { xmin <- min(x) }
n <- length(x)
x <- x[ x >= xmin]
if (length(x) != n) {
warning("too small values eliminated from vector")
n <- length(x)
}
mlogl <- function(alpha) {
C <- 1/sum( (xmin:10000)^-alpha )
-n*log(C)+alpha*sum(log(x))
}
alpha <- mle(mlogl, start=list(alpha=start), ...)
alpha
}
# -------------------------------------------------
The function (with is manual page) is also included in the igraph package,
see http://cneurocvs.rmki.kfki.hu/igraph
Hope this helps,
Gabor
On Wed, Feb 15, 2006 at 08:07:50AM -0500, Glazko, Galina wrote:
> Dear list,
>
>
>
> Does anyone know how to fit the power law distribution?
>
> I have the empirical distribution and would like to check whether it fits
>
> power law (with the power estimated from the data).
>
>
>
> Any hints are appreciated
>
>
>
> Best regards
>
> Galina
>
>
>
> [[alternative HTML version deleted]]
>
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--
Csardi Gabor <csardi at rmki.kfki.hu> MTA RMKI, ELTE TTK