Seems to work here. (See the code that ran below.)
Did you download the function and put it somewhere in your MATLAB path?
What do you get if you type
which ginicoeff
at the command line?
% Define some random data
p = rand(5,3);
w = rand(5,3);
g = ginicoeff(p,w)
function g = ginicoeff(p, w)
% The function computes the Gini Coefficient for populations p associated
% to wealth levels w, i.e. p(i, j) people have a total wealth of w(i, j).
% The function operates on the columns of p and w.
% p and w must be 2D matrices: only positive values are allowed for p, only
% non-negative values are allowed for w (with at least one element i such
% that w(i, j) > 0, for all j).
% The Gini Coefficient is a measure of inequality, i.e. a measure of wealth
% concentration.
%
% http://en.wikipedia.org/wiki/Lorenz_curve
% http://en.wikipedia.org/wiki/Gini_coefficient
% http://en.wikipedia.org/wiki/Theil_index
% http://en.wikipedia.org/wiki/Atkinson_index
%
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
%
% % Example 1: Uniform U(0, 1)
% N = 1000; % Number of populations
% p = rand(N, 1); w = rand(N, 1); % Population and Wealth extracted from a Uniform U(0, 1)
% g = ginicoeff(p, w)
% g =
%
% 0.476192486513232
%
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
%
% % Example 2: Standard Normal(4, 1)
% N = 1000; % Number of populations
% p = rand(N, 1); % Population extracted from a Uniform U(0, 1)
% mu = 4;
% w = mu + randn(N, 1); % Wealth extracted from a Normal N(mu, 1)
% w = abs(w); % Be careful: all values must be strictly positive!!!
% g = ginicoeff(p, w)
% g =
%
% 0.383791430802560
%
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
%
% % Example 3: LogNormal logN(0, 1)
% N = 1000; % Number of populations
% p = rand(N, 1); % Population extracted from a Uniform U(0, 1)
% w = exp(randn(N, 1)); % Wealth extracted from a LogNormal logN(0, 1)
% g = ginicoeff(p, w)
% g =
%
% 0.610656424432874
%
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
%
% % Example 4: Power Law PL(kappaPL, alphaPL)
% N = 1000; % Number of populations
% p = rand(N, 1); % Population extracted from a Uniform U(0, 1)
% kappaPL = 1;
% alphaPL = 2;
% w = kappaPL * (rand(N, 1) .^ (-1/alphaPL)); % Wealth extracted from a Power Law PL(kappaPL, alphaPL)
% g = ginicoeff(p, w)
% g =
%
% 0.481396918139056
%
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
%
% % Example 5: Mixture of LogNormal logN(0, 1) and Power Law PL(kappaPL, alphaPL)
% N = 1000; % Number of populations
% p = rand(N, 1); % Population extracted from a Uniform U(0, 1)
% alpha = floor(0.92 * N); % Number of populations whose wealth is extracted from a LogNormal logN(0, 1)
% w = exp(randn(alpha, 1)); % Wealth extracted from a LogNormal logN(0, 1)
% kappaPL = 1;
% alphaPL = 2;
% w((alpha + 1):N) = kappaPL * (rand(N - alpha, 1) .^ (-1/alphaPL)); % Wealth extracted from a Power Law PL(kappaPL, alphaPL)
% g = ginicoeff(p, w)
% g =
%
% 0.623694428395574
%
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
% -*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
%
%-*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-*%
% %
% Author: Liber Eleutherios %
% E-Mail: libereleutherios@gmail.com %
% Date: 19 May 2008 %
% %
%-*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-* -*-*%
ctrl = isnumeric(p) & isreal(p) & isnumeric(w) & isreal(w);
ctrl = ctrl & all(p(:) > 0) & all(w(:) >= 0) & all(any(w > 0));
[n, k] = size(p);
ctrl = ctrl & all([n, k] == size(w)) & (n ~= 1);
if ~ctrl
error('Values for either Population or Wealth or both are incorrect!')
end
% Normalize total population and total wealth to 1.
p = p ./ repmat(sum(p), n, 1);
w = w ./ repmat(sum(w), n, 1);
% Keep the smallest population, needed to normalize the Gini coefficient
minpop = min(p);
% Store in a single array
pw = p; pw(:, :, 2) = w; pw(:, :, 3) = w ./ p;
% Sort with respect to Wealth Per Capita
for h = 1:k
pw(:, h, :) = sortrows(squeeze(pw(:, h, :)), 3);
end
pw(:, :, 3) = [];
pw = [zeros(1, k, 2); pw];
% Cumulative p & w
pw = cumsum(pw);
% Average bases and height for right trapezoids
height = diff(pw(:, :, 1));
base = (pw(1:(end - 1), :, 2) + pw(2:end, :, 2)) / 2;
% The Gini Coefficient is normalized with respect to its highest possible
% value which is obtained if the smallest population owns all the existing
% wealth.
g = (1 - 2 * sum(height .* base)) ./ (1 - minpop);
end
