Compiling fixes from other forks

Contents.m

@@ -60,4 +60,3 @@
 %
 % Author:
 %   Philipp Berens & Marc J. Velasco, 2009
-

README.md

@@ -1,4 +1,86 @@
-circstat-matlab
-===============
+CircStat for Matlab
+=======================
 
-Matlab Circular Statistics Toolbox
+Toolbox for circular statistics with Matlab. 
+
+## Authors:
+Philipp Berens & Marc J. Velasco
+
+*Email*: [email protected]
+
+*Homepage*: http://www.kyb.tuebingen.mpg.de/~berens/circStat.html
+
+## Contributors:
+Tal Krasovsky
+
+## Reference:
+P. Berens, CircStat: A Matlab Toolbox for Circular Statistics, Journal of Statistical Software, Volume 31, Issue 10, 2009
+http://www.jstatsoft.org/v31/i10
+
+Please cite this paper when the provided code is used. See licensing terms for details.
+
+## Contents:
+- `circ_r` 				Resultant vector length
+- `circ_mean` 			Mean direction of a sample of circular data
+- `circ_axial`			Mean direction for axial data
+- `circ_median`			Median direction of a sample of circular data
+- `circ_std` 			Dispersion around the mean direction (std, mardia)
+- `circ_var` 			Circular variance
+- `circ_skewness`		Circular skewness
+- `circ_kurtosis`		Circular kurtosis
+- `circ_moment`			Circular p-th moment
+- `circ_dist`			Distances around a circle
+- `circ_dist2`			Pairwise distances around a circle
+- `circ_confmean` 		Confidence intervals for mean direction
+- `circ_stats`			Summary statistics
+
+ 
+- `circ_rtest`			Rayleigh's test for nonuniformity
+- `circ_otest`			Hodges-Ajne test (omnibus test) for nonuniformity
+- `circ_raotest`		Rao's spacing test for nonuniformity
+- `circ_vtest`			V-Test for nonuniformity with known mean direction
+- `circ_medtest`		Test for median angle
+- `circ_mtest`			One-sample test for specified mean direction
+- `circ_wwtest`			Multi-sample test for equal means, one-factor ANOVA
+- `circ_hktest` 		Two-factor ANOVA
+- `circ_ktest`      	Test for equal concentration parameter
+- `circ_symtest`		Test for symmetry around median angle
+- `circ_kuipertest`	 	Test whether two distributions are identical (like KS test)
+
+ 
+- `circ_corrcc`			Circular-circular correlation coefficient
+- `circ_corrcl`			Circular-linear correlation coefficient
+
+ 
+- `circ_kappa` 			Compute concentration parameter of a von Mises distribution
+- `circ_plot`			Visualization for circular data
+- `circ_clust`    		Simple clustering for circular data
+- `circ_samplecdf`	 	Evaluate CDF of a sample of angles
+
+ 
+- `rad2ang`				Convert radian to angular values
+- `ang2rad`				Convert angular to radian values
+
+All functions take arguments in radians (expect for `ang2rad`). For a detailed description of arguments and outputs consult the help text in the files.
+
+Since 2010, most functions for descriptive statistics can be used in Matlab style matrix computations. As a last argument, add the dimension along which you want to average. This changes the behavior slightly from previous relaeses, in that input is not reshaped anymore into vector format. Per default, all computations are performed columnwise (along dimension 1). If you prefer to use the old functions, for now they are contained in the subdirectory 'old'.
+
+## References:
+- E. Batschelet, Circular Statistics in Biology, Academic Press, 1981
+- N.I. Fisher, Statistical analysis of circular data, Cambridge University Press, 1996
+- S.R. Jammalamadaka et al., Topics in circular statistics, World Scientific, 2001
+- J.H. Zar, Biostatistical Analysis, Prentice Hall, 1999
+
+
+The implementation follows in most cases 'Biostatistical Analysis' and all referenced equations and tables are taken from this book, if not otherwise noted. In some cases, the other books were preferred for implementation was more straightforward for solutions presented there.
+
+If you have suggestions, bugs or feature requests or want to contribute code, please email us.
+
+## Disclaimer:
+All functions in this toolbox were implemented with care and tested on the examples presented in 'Biostatistical Analysis' were possible. Nevertheless, they may contain errors or bugs, which may affect the outcome of your analysis. We do not take responsibility for any harm coming from using this toolbox, neither if it is caused by errors in the software nor if it is caused by its improper application. Please email us any bugs you find.
+
+By Philipp Berens and Marc J. Velasco, 2009
+
+[email protected] , [email protected] - www.kyb.mpg.de/~berens/circStat.html
+
+Distributed under Open Source BSD License

circ_ang2rad.m

@@ -8,4 +8,5 @@
 % By Philipp Berens, 2009
 % [email protected] - www.kyb.mpg.de/~berens/circStat.html
 
-alpha = alpha * pi /180;
+alpha = alpha * pi /180;
+end

circ_axial.m

@@ -20,10 +20,11 @@
 % By Philipp Berens, 2009
 % [email protected] - www.kyb.mpg.de/~berens/circStat.html
 
-% TESTING GIT
+
 
 if nargin < 2
     p = 1;
 end
 
 alpha = mod(alpha*p,2*pi);
+end

circ_axialmean.m

@@ -1,4 +1,4 @@
-function [r mu] = circ_axialmean(alphas, m, dim)
+function [r, mu] = circ_axialmean(alphas, m, dim)
 %
 % mu = circ_axialmean(alpha, w)
 %   Computes the mean direction for circular data with axial 
@@ -7,7 +7,7 @@
 %   Input:
 %     alpha	sample of angles in radians
 %     [m		axial correction (2,3,4,...)]
-%     [dim      statistic computed along this dimension, 1]
+%     [dim      statistic computed along this dimension, default: 1st non-singular dimension]
 %
 %   Output:
 %     r		mean resultant length
@@ -27,7 +27,10 @@
 % Distributed under Open Source BSD License
 
 if nargin < 3
-  dim = 1;
+  dim = find(size(alphas) > 1, 1, 'first');
+  if isempty(dim)
+    dim = 1;
+  end
 end
 
 if nargin < 2 || isempty(m)
@@ -38,4 +41,4 @@
 
 r = abs(zbarm);
 mu = angle(zbarm)/m;
-
+end

circ_clust.m

@@ -138,14 +138,13 @@ function plotColor(x, y, c, varargin)
 colors={'y', 'b', 'r', 'g', 'c', 'k', 'm'};
 
 hold on;
-for j=1:length(csmall);
+for j=1:length(csmall)
 
    ci = (c == csmall(j));
    plot(x(ci), y(ci), strcat(pstring, colors{mod(j, length(colors))+1}), 'MarkerSize', ms);
 
 end
 if ~overlay, hold off; end
 figure(figurenum)
-
-
-
+end
+end

circ_cmtest.m

@@ -1,4 +1,4 @@
-function [pval med P] = circ_cmtest(varargin)
+function [pval, med, P] = circ_cmtest(varargin)
 %
 % [pval, med, P] = circ_cmtest(alpha, idx)
 % [pval, med, P] = circ_cmtest(alpha1, alpha2)
@@ -55,7 +55,8 @@
 end
 
 if any(n<10)
-  warning('Test not applicable. Sample size in at least one group to small.') %#ok<WNTAG>
+  warning('CIRCSTAT:circ_cmtest:sampleSizeTooSmall', ...
+      'Test not applicable. Sample size in at least one group too small.') %#ok<WNTAG>
 end
 
 
@@ -67,7 +68,7 @@
 if pval < 0.05
   med = NaN;
 end
-
+end
 
 
 
@@ -87,4 +88,4 @@
 else
   error('Invalid use of circ_wwtest. Type help circ_wwtest.')
 end
-
+end

circ_confmean.m

@@ -10,7 +10,7 @@
 %     [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]
+%     [dim  compute along this dimension, default: 1st non-singular dimension]
 %
 %   Output:
 %     t     mean +- d yields upper/lower (1-xi)% confidence limit
@@ -28,7 +28,10 @@
 % [email protected] - www.kyb.mpg.de/~berens/circStat.html
 
 if nargin < 5
-  dim = 1;
+  dim = find(size(alpha) > 1, 1, 'first');
+  if isempty(dim)
+    dim = 1;
+  end
 end
 
 if nargin < 4 || isempty(d)
@@ -67,13 +70,11 @@
     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.');
+    warning('CIRCSTAT:circ_confmean:requirementsNotMet', ...
+        'Requirements for confidence levels not met.');
   end
 end
 
 % apply final transform
 t = acos(t./R);
-
-
-
-
+end

circ_corrcc.m

@@ -1,6 +1,6 @@
-function [rho pval] = circ_corrcc(alpha1, alpha2)
+function [rho, pval] = circ_corrcc(alpha1, alpha2)
 %
-% [rho pval ts] = circ_corrcc(alpha1, alpha2)
+% [rho pval] = circ_corrcc(alpha1, alpha2)
 %   Circular correlation coefficient for two circular random variables.
 %
 %   Input:
@@ -50,4 +50,4 @@
 
 ts = sqrt((n * l20 * l02)/l22) * rho;
 pval = 2 * (1 - normcdf(abs(ts)));
-
+end

circ_corrcl.m

@@ -1,6 +1,6 @@
-function [rho pval] = circ_corrcl(alpha, x)
+function [rho, pval] = circ_corrcl(alpha, x)
 %
-% [rho pval ts] = circ_corrcc(alpha, x)
+% [rho pval] = circ_corrcc(alpha, x)
 %   Correlation coefficient between one circular and one linear random
 %   variable.
 %
@@ -47,4 +47,4 @@
 
 % compute pvalue
 pval = 1 - chi2cdf(n*rho^2,2);
-
+end

circ_dist.m

@@ -25,4 +25,5 @@
   error('Input dimensions do not match.')
 end
 
-r = angle(exp(1i*x)./exp(1i*y));
+r = angle(exp(1i*x)./exp(1i*y));
+end

circ_dist2.m

@@ -1,6 +1,6 @@
 function r =  circ_dist2(x,y)
 %
-% r = circ_dist(alpha, beta)
+% r = circ_dist2(alpha, beta)
 %   All pairwise difference x_i-y_j around the circle computed efficiently.
 %
 %   Input:
@@ -33,4 +33,5 @@
 end
 
 r = angle(repmat(exp(1i*x),1,length(y)) ...
-       ./ repmat(exp(1i*y'),length(x),1));
+       ./ repmat(exp(1i*y'),length(x),1));
+end

circ_hktest.m

@@ -1,4 +1,4 @@
-function [pval table] = circ_hktest(alpha, idp, idq, inter, fn)
+function [pval, table] = circ_hktest(alpha, idp, idq, inter, fn)
 
 %
 % [pval, stats] = circ_hktest(alpha, idp, idq, inter, fn)
@@ -238,16 +238,4 @@
     end
 
   end
-
-
 end
-
-
-
-
-
-
-
-
-
-

circ_kappa.m

@@ -55,3 +55,4 @@
     kappa = (N-1)^3*kappa/(N^3+N);
   end
 end
+end

circ_ktest.m

@@ -39,7 +39,8 @@
 rbar = (R1+R2)/(n1+n2);
 
 if rbar < .7
-    warning('resultant vector length should be > 0.7') %#ok<WNTAG>
+    warning('CIRCSTAT:circ_ktest:vectorTooShort', ...
+        'Resultant vector length should be > 0.7') %#ok<WNTAG>
 end
 
 % calculate test statistic
@@ -50,10 +51,4 @@
   f = 1/f; 
   pval = 2*(1-fcdf(f, n2, n1));
 end
-
-
-
-
-
-
-
+end