/usr/share/octave/packages/statistics-1.3.0/gamfit.m is in octave-statistics 1.3.0-1.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ## Author: Martijn van Oosterhout <kleptog@svana.org>
## This program is granted to the public domain.
## -*- texinfo -*-
## @deftypefn {Function File} {@var{MLE} =} gamfit (@var{data})
## Calculate gamma distribution parameters.
##
## Find the maximum likelihood estimators (@var{mle}s) of the Gamma distribution
## of @var{data}. @var{MLE} is a two element vector with shape parameter
## @var{A} and scale @var{B}.
##
## @seealso{gampdf, gaminv, gamrnd, gamlike}
## @end deftypefn
## This function works by minimizing the value of gamlike for the vector R.
## Just about any minimization function will work, all it has to do a
## minimize for one variable. Although the gamma distribution has two
## parameters, their product is the mean of the data. so a helper function
## for the search takes one parameter, calculates the other and then returns
## the value of gamlike.
## FIXME is this still true???
## Note: Octave uses the inverse scale parameter, which is the opposite of
## Matlab. To work for Matlab, value of b needs to be inverted in a few
## places (marked with **)
function res = gamfit(R)
if (nargin != 1)
print_usage;
endif
avg = mean(R);
# This can be just about any search function. I choose this because it
# seemed to be the only one that might work in this situaition...
a=nmsmax( @gamfit_search, 1, [], [], avg, R );
b=a/avg; # **
res=[a 1/b];
endfunction
# Helper function so we only have to minimize for one variable. Also to
# inverting the output of gamlike, incase the optimisation function wants to
# maximize rather than minimize.
function res = gamfit_search( a, avg, R )
b=a/avg; # **
res = -gamlike([a 1/b], R);
endfunction
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