Circular Statistics Toolbox (Directional Statistics)

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08 Apr 2006 (Updated )

Compute descriptive and inferential statistics for circular or directional data.

Editor's Notes:

This file was selected as MATLAB Central Pick of the Week

circ_confmean(alpha, xi, w, d, dim)
function t = circ_confmean(alpha, xi, w, d, dim)
%
% t = circ_mean(alpha, xi, w, d, dim)
%   Computes the confidence limits on the mean for circular data.
%
%   Input:
%     alpha	sample of angles in radians
%     [xi   (1-xi)-confidence limits are computed, default 0.05]
%     [w		number of incidences in case of binned angle data]
%     [d    spacing of bin centers for binned data, if supplied 
%           correction factor is used to correct for bias in 
%           estimation of r, in radians (!)]
%     [dim  compute along this dimension, default is 1]
%
%   Output:
%     t     mean +- d yields upper/lower (1-xi)% confidence limit
%
% PHB 7/6/2008
%
% References:
%   Statistical analysis of circular data, N. I. Fisher
%   Topics in circular statistics, S. R. Jammalamadaka et al. 
%   Biostatistical Analysis, J. H. Zar
%
% Circular Statistics Toolbox for Matlab

% By Philipp Berens, 2009
% berens@tuebingen.mpg.de - www.kyb.mpg.de/~berens/circStat.html

if nargin < 5
  dim = 1;
end

if nargin < 4 || isempty(d)
  % per default do not apply correct for binned data
  d = 0;
end

if nargin < 3 || isempty(w)
  % if no specific weighting has been specified
  % assume no binning has taken place
	w = ones(size(alpha));
else
  if size(w,2) ~= size(alpha,2) || size(w,1) ~= size(alpha,1) 
    error('Input dimensions do not match');
  end 
end

% set confidence limit size to default
if nargin < 2 || isempty(xi)
  xi = 0.05;
end

% compute ingredients for conf. lim.
r = circ_r(alpha,w,d,dim);
n = sum(w,dim);
R = n.*r;
c2 = chi2inv((1-xi),1);

% check for resultant vector length and select appropriate formula
t = zeros(size(r));

for i = 1:numel(r)
  if r(i) < .9 && r(i) > sqrt(c2/2/n(i))
    t(i) = sqrt((2*n(i)*(2*R(i)^2-n(i)*c2))/(4*n(i)-c2));  % equ. 26.24
  elseif r(i) >= .9
    t(i) = sqrt(n(i)^2-(n(i)^2-R(i)^2)*exp(c2/n(i)));      % equ. 26.25
  else 
    t(i) = NaN;
    warning('Requirements for confidence levels not met.');
  end
end

% apply final transform
t = acos(t./R);
  



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