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### Highlights from Circular Statistics Toolbox (Directional Statistics)

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# Circular Statistics Toolbox (Directional Statistics)

08 Apr 2006 (Updated )

Compute descriptive and inferential statistics for circular or directional data.

### Editor's Notes:

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File Information
Description

CircStat for Matlab
=======================

Toolbox for circular statistics with Matlab.

Authors: Philipp Berens
Email: philipp@bethgelab.org
Homepage: http://philippberens.wordpress.com/code/circstats/

Contributors:
Marc Velasco, 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 (not the technical report!). 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 vm distribution

circ_plot Visualization for circular data
circ_clust Simple clustering for circular data
circ_samplecdf Evaluate CDF of a sample of angles

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).

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

If you have suggestions, bugs or feature requests or want to contribute code, please email me.

Acknowledgements

Circular Cross Correlation inspired this file.

Required Products Statistics Toolbox
MATLAB release MATLAB 7.14 (R2012a)
09 Feb 2014

Actually, ignore the inverse_cdf function I have provided. It should generate a vlaue for kappa and it needs adjusting for values of thetahat other than zero.

07 Feb 2014

Great submission. It would be nice to have cdf and inversion cdf for the vmpdf functions. Here's what I wrote for my needs
function p = circ_vmcdf(alpha, thetahat, kappa)
%integrates the pdf from an angle of -pi to an angle alpha
F = @(x)circ_vmpdf(x, thetahat, kappa);
end

function theta = circ_vminv(p, thetahat, kappa)
%computes the inverse of the abovecirc_vmcdf.
fun =@(alpha)(circ_vmcdf(alpha, thetahat, kappa)-p);
theta = fzero(fun,[-pi pi]);
end

05 Feb 2014

Hi,
Thanks for the great contribution.
Can you please let me know if it is ok to get negative mean or median?
Shall I add 360 to the final angle to make it positive?
Cheers,
Sep

09 Oct 2013

Thanks for the toolkit.

Does anybody have a clue how to do multiple-regression with circular data?

26 Jul 2013

Hi everyone,
Quick question regarding circ_hktest - I quite often get NaNs as an output in the 'Interaction' row. Any idea what am I doing wrong?

13 Jun 2013

Mear - could you be more specific regarding your doubts about the wwtest?

The negative values for circ_mean are a result of the way circ_mean is implemented. If you prefer them to be between 0 and 2pi, just edit the function to provide the data in that format.

I'll update the von Mises function in a future release.

10 Jun 2013

Just a quick question concerning the circ_mean function: I usually get the results in negative values, despite all the input angles being in positive degrees (conv. to rads). It's hardly a big deal to translate this to [0,360] degrees, but it is a bit annoying and seems unneccesary. Is this how it should be? I'm also getting some results for the wwtest which seem very wrong to me (but make sense in light of the negative mean values), and it's making me question the accuracy of this toolbox.

21 May 2013

Marc, thanks, you are right.
I generated a von mises distribution with the mu and kappa estimated from my angles, say x, i.e.:
[mu kappa] = circ_vmpar(x)
and then
vonmis = circ_randvm(mu,kappa,length(x))

Then I use the kuiper test to see whether the two distribution x and vonmis differ significantly (the difference can be in any property, such as mean, location and dispersion):
[H,pValue] = circ_kuipertest(x, vonmis)

However I was wondering if it is possible to have more accurate p-value estimates in the Kuiper test, as already asked by another user before.

18 May 2013

Dear Mark,
thanks for your tip, however I'm not really convinced.
Both the circ_ktest and the circ_kuipertest are not described in the pdf:
http://www.jstatsoft.org/v31/i10/paper

Anyway, circ_ktest is a parametric two-sample test to determine whether two concentration parameters are different.
The circ_kuipertest is a two-sample test which allow to test whether two input samples differ significantly. The difference can be in any property, such as mean location and dispersion. It is a circular analogue of the Kolmogorov-Smirnov test.

I do not understand how these tests could help me with a goodness-of-fit test for the Von Mises-Fisher distribution, but probably is my limit.

Could anyone being of any help?

Regards,

Sergio

14 May 2013

sergio - did you see the pdf with descriptions? (http://www.jstatsoft.org/v31/i10/paper)

You probably want either the ktest of the kuipertest.

13 May 2013

Hi guys, I'm new to circular statistics and I've downloaded this package.

Given some vectors, I'd like to test if they are distributed following a Von Mises-Fisher distribution.
Do you know what instructions of the package I should use?
Can you help?

02 Apr 2013

After some testing I figured the previous bug has to do with recurrence of unique values in the data. I took care of it by using
alpha=alpha+0.00001*(1:numel(alpha)), but this is obviously a workaround which isn't satisfactory for a self-respecting algorithm.

At any rate, I forgot to mention how great this toolbox is. It has been of great help, and saved me a lot of time and work.

02 Apr 2013

Hi, I'm getting wrong clustering using circ_clust.
for example, if I give as an input circ_clust([1 1 1 1 3.5 4 5.5 0.5],2)

I get
ans =
1
2
1
1
1
1
1
1

sometimes the clustering does work, but I don't know why it does/doesn't...

I'm using Matlab 2012b...

14 Feb 2013

Great toolbox. I was wondering if it is possible to have more accurate p-value estimates in the Kuiper test?

11 Dec 2012

Hi,
great toolbox, thanks.
By the way, I agree with Allan's comment (see below) that regarding the Von Mises distribution, it may be useful to have an implementation with higher numerical stability. In particular, I added this trivial function, which returns the log-pdf of the Von Mises distrib:

function [p alpha] = circ_vm_logpdf(alpha, thetahat, kappa)

% if no angles are supplied, 100 evenly spaced points around the circle are
% chosen
if nargin < 1 || isempty(alpha)
alpha = linspace(0, 2*pi, 101)';
alpha = alpha(1:end-1);
end
if nargin < 3
kappa = 1;
end
if nargin < 2
thetahat = 0;
end

alpha = alpha(:);

% evaluate pdf
C = -log( 2*pi*besseli(0,kappa) );
p = C + kappa*cos(alpha-thetahat);

Thanks to the greater numerical stability log-pdfs are often used in place of pdfs, so this little function may be of help to others...

06 Nov 2012

Hi there, great toolbox. I propose a change to avoid numerical instability in circ_vmpdf.m.
Current code to evaluate the pdf:

C = 1/(2*pi*besseli(0,kappa));
p = C * exp(kappa*cos(alpha-thetahat));

Proposed replacement code:
C = log(1)-log(2*pi*besseli(0,kappa,1))+(kappa*cos(alpha-thetahat))-kappa;
p = exp(C);

Examples:

circ_vmpdf(0,0,1000)

Old code result: NaN
New code result: 12.6141

18 Sep 2012

Ryan, the average is in the dot product w'*exp(...) which in the simplest case is a vector of ones - so this is the sum operation. exp(i*angle) decomposes the angle into its sine and cosine components. Finally, angle is atan2. Compare the results of your and my code - they should be identical with my code likely running a bit fast due to matrix style computations.

Bst
Philipp

16 Aug 2012

I haven't run through this toolbox yet, so I apologize if I am missing something with this question (I just glanced through the source code because I am interested in directional stats).

When you calculate the mean, the formula you use is:

% compute weighted sum of cos and sin of angles
r = w'*exp(1i*alpha);

% obtain mean by
mu = angle(r);

Now, correct me if I'm wrong, but this doesn't seem to calculate the average at all? It seems to me that here we are inputting a data array into the angle command, which will output the phase angle of each element of that array, not a singular mean.

Wouldn't a better way of calculating the average be to use atan2? Something like:

for i = 1:w
S(i) = sin(alpha(i));
C(i) = cos(alpha(i));
end

X = sum(S)*(1/w);
Y = sum(C)*(1/w);

mu = atan2(X,Y);

15 Aug 2012

Thanks for the great toolbox! I have a question: I have a set of directional stochastic variables that are mutually correlated. I have used circ_corrcc to construct a correlation matrix for these variables, but I’m also interested in their covariance matrix. There does not appear to be a function for this in the current toolbox.

Not having any previous experience with circular statistics, I’m wondering if it makes sense to construct a covariance matrix by de-normalizing the correlation matrix, multiplying each element by the two corresponding circular standard deviations? Perhaps a covariance matrix could be a useful addition to the toolbox.

Thanks,
Marnix

18 Jul 2012

Hi Francesco, if you have orientations, multiply all orientations by 2 to obtain directions. If you want to obtain the mean resultant vector, devide its orientation by 2 again.

14 Jul 2012

excuse my previous post! I just realize what p-axial truly meant.

For further reference this will solve the previously cited problem

%% uniform distribution test
% in the interval [0, 180)
p180 = circ_otest(y180)
% in the interval [0 360)
p360 = circ_otest(y360)

14 Jul 2012

excuse my previous post! I just realize what p-axial truly meant.

For further reference this will solve the previously cited problem

%% uniform distribution test
% in the interval [0, 180)
p180 = circ_otest(y180)
% in the interval [0 360)
p360 = circ_otest(y360)

14 Jul 2012

I am testing the toolbox out with not much of a prior knowledge on the subject. It seems a really good piece of software and it's helping me out grasping some of the theory.

I have a question: if I am dealing with orientations [0, 180) degrees more than directions [0 360), is there a proper way to transform may data prior to using the function in the toolbox?

For example, if I am trying to test for circular uniformity with a population that is uniformly distributed in [0 180) - which I'd like to have a p>0.05 - I obtained a very small value, which is consistent with the test looking over the full interval.

Suggestions? Thanks
Francesco

---Example code ----
p180 = circ_otest(y180)
% in the interval [0 360)
p360 = circ_otest(y360)

27 Jun 2012

Great toolbox! Exactly what I needed. However, my data has many NaN's - do you have a way to work around data with gaps?

30 May 2012

This is a great toolbox - very helpful. A few bug reports:

1. formatSubPlot calls "parseVarArgs" that's not standard matlab, or part of this toolbox. Could you add a pointer to where to download this.

2. In Example 2 the descriptive stats cell needs updating to respect the matrix style computations. So, line 67 should read:

stats(i,1) = circ_mean(ori,spk,2);

and similar for all the other lines of code.

Thanks for supporting this toolbox.

16 May 2012

I confirm Dillon's report on circ_wwtest bug.

26 Apr 2012

Great toolbox but I think there is an error in the logic used at circ_wwtest.m -> checkAssumption() lines 107-115.

if n > 10 && rw<.45
warning('Test not applicable. Average resultant vector length < 0.45.') %#ok<WNTAG>
elseif n > 6 && rw<.5
warning('Test not applicable. Average number of samples per population < 11 and average resultant vector length < 0.5.') %#ok<WNTAG>
elseif n >=5 && rw<.55
warning('Test not applicable. Average number of samples per population < 7 and average resultant vector length < 0.55.') %#ok<WNTAG>
elseif n < 5
warning('Test not applicable. Average number of samples per population < 5.') %#ok<WNTAG>
end

Notice that the if/else statements do not match the warning text. Particularly when n>5 the user will always be warned when the resultant vector, rw<0.55 which is not captured by the warning. The corrected if/else statements are as follows:
if n >= 11 && rw<.45
warning('Test not applicable. Average resultant vector length < 0.45.') %#ok<WNTAG>
elseif n<11 && n >= 7 && rw<.5
warning('Test not applicable. Average number of samples per population < 11 and average resultant vector length < 0.5.') %#ok<WNTAG>
elseif n<7 && n >=5 && rw<.55
warning('Test not applicable. Average number of samples per population < 7 and average resultant vector length < 0.55.') %#ok<WNTAG>
elseif n < 5
warning('Test not applicable. Average number of samples per population < 5.') %#ok<WNTAG>
end

I've assumed that the warning statements are correct but if the if/else statements are correct it would be more compact to warn the user under only 2 conditions: n<5 and rw<0.55.

Thanks again for the very useful toolbox.

24 Apr 2012

Thanks for the recent feedback and bugreports. I was away for a while and will start taking care of them soon.

30 Mar 2012

Parametric and nonparametric paired sample tests, Zar (2010) Biostatistical Analysis, sections 27.13 and 27.14

16 Mar 2012

Sorry wrong line number. The error is at line 169!
in the function circ_hktest.m
pI = 1 - chi2pdf(chiI, df_i);
It should be
pI = 1 - chi2cdf(chiI, df_i);

Great toolbox.

15 Mar 2012

Great toolbox!

15 Mar 2012

I found an error in the function circ_hktest.m at line 160
pI = 1 - chi2pdf(chiI, df_i);
It should be
pI = 1 - chi2cdf(chiI, df_i);

01 Mar 2012

Thank you very much for such a useful toolbox. Now, I have a question related to circ_ktest (two-smple test to compare concentration). The F statistic is defined only in case of rbar>.7, Mardia (pag 133, 1999) compute F in the case where resultant vector length is <0.45 :
n1 = length(alpha1);
n2 = length(alpha2);

R1avg=circ_r(alpha1);
R2avg = circ_r(alpha2);

R1 = n1*circ_r(alpha1);
R2 = n2*circ_r(alpha2);

%make sure that rbar > .7
rbar = (R1+R2)/(n1+n2);

if rbar > .7

f = ((n2-1)*(n1-R1))/((n1-1)*(n2-R2));

elseif rbar< .45 %taken from Mardia 1999 p.133 (Baschelet report: Mardia 1972 pag 161)

g11= asin(2*sqrt(3/8)*(R1avg));
g12= asin(2*sqrt(3/8)*(R2avg));

f= (2/sqrt(3))*((g11-g12)/(1/(n1-4)+ 1/(n2-4)).^(1/2));

But here Sample 1 and Sample 2 define the sign of F... and so S1 and S2 will be defined depending on Ravg value being S1>S2 for computation of F. Is this right?
Thank you!
natalia

11 Jan 2012

Great tool.
I do have to say that circ_mtest is a bit weird.
The input is [pval, z] but output is set as [h,mu,ul,ll]

08 Dec 2011

Thanks for this excellent toolbox!
I have only some problems with the example files, that I downloaded from http://www.jstatsoft.org/v31/i10
example1:
??? Undefined function or method 'parseVarArgs', therefore the figure 2 isn't complete, and it hasn't axis labels.

example2:
??? Error using ==> mtimes
Inner matrix dimensions must agree.

Error in ==> example2 at 42
zm = r*exp(i*phi);
Perhaps a dot is absent, but after this modification zm = r.*exp(i*phi); the same error occurs:
??? Undefined function or method 'parseVarArgs'
Can somebody help me to fix this problems?
Thank you very much!

11 Nov 2011

@Christopher: Thank you so much for your kindness and help. I really appreciate it.

09 Nov 2011

@Fuh: indeed it should and when I step carefully through the function, sometimes the result comes out correct and sometimes it doesn't, somewhat dependent on the numbers in alpha. To fix the problem go to lines 45 and 46 of circ_median (ver 2011f). You see two inequalities, dd>=0 and dd<0. The two inequalities should be identical for consistency and the correct result. Edit line 46 to read:
m2 = sum(dd<=0,1);
Now the function seems to behave as expected.

09 Nov 2011

I am new in circular statistics, so don't laugh at me... But I do have a question about the circ_median() function.

Say I have a data set that contains six angles [0.1 0.2 0.3 0.4 0.5 0.6]. when I feed these data into circ_median(), the function returns a median = 0.4

I thought that, when a data set contains an even number of observations, the median would be calculated as the average of the middle two numbers (i.e., (0.3+0.4)/2 = 0.35).

My code is listed below.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
alpha = [0.1 0.2 0.3 0.4 0.5 0.6]';
med = circ_median(alpha)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Can anyone help me with this?

Sincerely,
Fuh

28 Sep 2011

@Christopher: The ~ has been introduced as a placeholder in the latest MATLAB versions for output arguments that are not needed. I will go back to some dummy variable with the next upload.

@Heida: I don't see an easy way of doing what you suggest with the functions implemented.

@Omzaz: The multi-sample tests assume independent samples. I don't know about repeated-measures ANOVA etc. for circular data. If you find anything let me know.

The option to ignore NaNs... I think this is a tricky thing, because you always make a specific choice how NaNs are treated and each user might have different preferences. I will think about it though.

20 Sep 2011

Can any of the multi-sample tests in this toolbox be used with repeated measures data or do they all assume independent samples?

30 Aug 2011

Very useful toolbox. Option to ignore NaNs in the calculations would make it even better.

22 Aug 2011

Good toolbox! I added a function for kernel smoothing density estimate for circular data here: http://www.mathworks.com/matlabcentral/fileexchange/32614-kernel-smoothing-density-estimate-for-circular-data .

17 Jul 2011

Thanks people, this toolbox is really helpful and easy to use.

I have one stats question -- forgive me that it is not a direct question about this toolbox but perhaps someone could help nonetheless.

I have repeated measures of circular data for multiple participants, lets say 15 participants where each participant contributes four angles. I have reason to believe that the angular distributions are going to be multipolar and not von Mises distributed. This would in essence require some kind of non-parametric repeated measures test which I am not sure has been developed for circular data. Is there a way to test for circular uniformity in this data set by using the procedures in the circstat toolbox, perhaps using some kind of p-value correction?

09 Jun 2011

Um, what happened to my comment? What I wrote was that an typo error appears to have been introduced in circ_kuipertest.m in the advance to version 2011f. In 2010e, line 48 of the file reads:
[phis2 cdf2 phiplot2 cdfplot2] = circ_samplecdf(alpha2, res);

and in version 2011f, that line reads:
[~, cdf2 phiplot2 cdfplot2] = circ_samplecdf(alpha2, res);

and matlab complains of incorrect statement or expression.

09 Jun 2011

Sorry, the error is in circ_kuipertest.m, not circ_kuiper.m as previously written!

10 Mar 2011

I'm sorry for this silly question. When I use circ_vmpdf to calculate for the case kappa is big and data has close value with the mean, it return the value bigger than 1. For example:

circ_vmpdf(pi,pi,35)
ans =
2.3516

Did I mis-understand anything here?
I saw in the graphs of two functions based on kappa:

f1 = 1/(2*pi*besseli(0,kappa))
f2 = exp(kappa)

With high value of kappa, the increasing rate of second one is much higher than the first one so that it's not strange if the above case happened.

01 Mar 2011

Needed a circular statistic means and got what I needed... thanks!

15 Dec 2010

Hi Christopher, thanks for your feedback. I will update circ_plot with the next upload.

01 Dec 2010
28 Nov 2010

Another issue with circ_plot.m:
A typo in line 121, should read
s = varargin{3};

26 Nov 2010

I am seeing an anomaly in circ_plot.m that does not make sense to me, can someone please explain or concur that it is a bug.

According to the help hist can plot either count or normalized bins.
In line 110 of circ_plot rose is called to calculate the bins
110] [t,r] = rose(alpha,x);
and in line 112 the normalized bins are plotted:
112] polar(t,r/sum(r),formats)
Now the vectors t and r returned by rose are such that they can be used to plot the bins directly and have the layout [0 n1 n1 0 0 n2 n2 0 0 ...] i.o.w. each element of bin count appears twice, and sum(r) is equal to 2.*length(alpha). So to truly normalize the r, we should divide by half of sum(r) and each bin should be twice as tall. Check by comparison to hist, which returns the same kind of information, but for the first and last bins.
I propose the following replacements for lines 112 and 113
112] polar(t,2.*r./sum(r),formats)
113] mr = max(2.*r./sum(r));

09 Nov 2010

I found circ_median very slow. There might be a more efficient algorithm to get the median, but at least note that around line 42 m1 and m2 are each determined by calculating the same circ_dist2(beta,beta). circ_dist2 takes a LONG time. I would use an intermediate variable, and calculate circ_dist2 only once (to almost halve the time for the function to run, down from 2 seconds to 1 second for 2000 data points).

27 Oct 2010

Nice work

31 Aug 2010

Excellent toolbox!!! Thanks Philipp!

29 Jun 2010

I fixed the bug in circ_clust.

18 Jun 2010

This toolbox is great!! I'm getting an error with circ_clust though. Is there a bug? I haven't been able to solve it myself. This is the error message i get.

??? In an assignment A(I) = B, the number of elements in B and
I must be the same.

Error in ==> circ_clust at 53
mu(j) = circ_mean(alpha(cid==j));

07 Jan 2010

There has been a slight (and unfortunately undocumented) change in semantics from 2009 to the later versions.

vmpdf computes the density, i.e. it evaluates the probability density function of the von mises distribution at the designated points. The earlier version computed the approximate probability in a small bin with width (alpha(2)-alpha(1) ), as is needed if you want to plot histograms. As you will see, you can easily recover the old behavior by

p = circ_vmpdf(points(1:end-1).', mu, kappa);
p = p * diff(points(1:2));

to obtain approximate probabilities.

06 Jan 2010

Ops sorry I must have meesed up with the posts....
I'll repost my doubt properly:

Try to run

points = -pi:((4*pi)/(2*Nbin)):3*pi;
mu = -2.838;
kappa = 0.5125;

p = circ_vmpdf(points(1:end-1).', mu, kappa);

It seems to me that results from version circStat2009 (which I think were correct) differ from those of CircStat2009d and CircStat2010b. Maybe I'm doing something wrong. Please let me know.
Thanks a lot,
Cesare

06 Jan 2010

also with the 2010b.
Thanks,
Cesare

04 Jan 2010

I fixed the bugs reported in December in the first upload of 2010.

I also added the functionality asked for by chairmanK. The functions for descriptive statistics now handle N dimensional arrays and the computations can be performed 'Matlab-style'. As a backup, the new release comes with a folder 'old', which contains the functions that are thus replaced as backup. If you experience problems or issues with the new functions or would like to see additional functions converted let me know. Unused arguments in between can be left empty.

circ_vmpdf seems to work fine with me and produces data with the correct moments. Please be more specific.

28 Dec 2009

I think, in circ_skewness, the eponent for the denominator of the last equation should be (3/2), rather than (2/3), according to formula (2.29) of Fisher 1993.

24 Dec 2009

something's wrong in the new circ_vmpdf....can't replicate results from previous release

10 Dec 2009

Functions do not gracefully handle N-dimensional arrays for powerful MATLAB-style computations; instead, inputs are coerced to be column vectors. There are also numerous errors. One example, in circ_moment.m:
cbar = sum(cos(p*alpha'*w))/n;
(p*alpha'*w) is a SCALAR dot product, so clearly this is not a weighted sum of cosines as it ought to be.
There are many other bugs like this. Please fix!

05 Nov 2009

Issue is fixed in the upload of 11/5/09.

05 Nov 2009

Encountering same issue as Shiquan

30 Oct 2009

bug report:
function stats = circ_stats(alpha, w, d)
line50:stats.std_mardia = circ_std(alpha,w,d,'mardia');
the function circ_std(alpha, w, d) doesn't accept parameter 'mardia'.

17 Oct 2009

This is great work! Thanks.

12 Oct 2009

Dear Florin,

thanks for the error report.

With regards to 1: Fixed all bugs. I tested the output on the example in Harrison & Kanji.

With regards to 2: This is unfortunate. The current (and more recent) toolbox version returns both, angular deviation and circular standard deviation as first and second return argument.

Philipp

06 Oct 2009

Good work! So far it helped me a lot. But there are some
errors arround!

1.) function circ_hktest

Line 55 found -> qm = zeros(p,1); qr = qm; qn = pm;
corrected? -> qm = zeros(p,1); qr = qm; qn = qm;

Line 94 found -> eff_2 = sum(qr.^2 ./ sum(cn,2)) - tr.^2/n;
corrected? -> eff_2 = sum(qr.^2 ./ sum(cn,1)') - tr.^2/n;

Line 107 found -> beta = 1/(1-1/(5*kk)-1/(10*(kk^2)));
comment -> beta overloads the beta function (help beta)
An other name like betaF should be used

Line 144 found -> F1 = beta * ms_1 / ms_r;
comment -> if inter is set to 0/false beta is not defined!

2.) function circ_std

The documentation in the paper

In CircStat , theangular deviation is computed as
>>s=circ_std(alpha);
and the circular standard deviation as
>>s0=circ_std(alpha,[],[],'mardia');

does not fit with neither - paper and Matlab Central - implementation.

29 Sep 2009

Both definitions are around... I will optionally add computing both with the next update.

23 Sep 2009

Sorry, but I think circ_var returns s = (1-r) when it should be s = 2*(1-r).

for this reason, circ_std and circ_var will not agree

01 Jul 2009

This is a great submission, filling an obvious gap in the statistical world out there. Easy to use, well done, and the author provides great feedback.

Great!

29 Jun 2009

Excellent toolbox, helped me a lot. Greatly appreciated!

30 Apr 2009

this is a great toolbox

19 Feb 2009

PS:
I think there is an error in the circ_dist function

I think this

r = angle(repmat(exp(1i*x(:)'),length(y),1) ...
./ repmat(exp(1i*y(:)),1,length(x)));

Let me know if it's correct

19 Feb 2009

I wish I saw it before :)
Very nicely done.

15 Oct 2008

I take that back -- guilty of confusing 'r' and 'R'

15 Oct 2008

I believe I have found an error in one of your functions:
circ_rtest.m
Line 51: z = R^2 / n;
should be
Line 51: z = R^2 * n;

08 Oct 2008

Would include skewness and kurtosis!

18 May 2007

Exactly what I needed

29 Jan 2007

thanks.

01 Jul 2009

Changed licensing

20 Jul 2009

A number of small bug fixes.

21 Sep 2009

Removed some bugs.

Added new, more complicated tests (ANOVA like testing).

23 Sep 2009

Updated reference for paper

09 Oct 2009

Two new tests

14 Oct 2009

Bug fix.

26 Oct 2009

Bug fix in circ_dist and circ_clust.

04 Nov 2009

Bug fix in circ_skewness and circ_kurtosis. Thanks to Shiquan Wang.

05 Nov 2009

Bug fix in circ_stats.

04 Jan 2010

05 Jan 2010

Small bugfixes, mainly in the help sections

09 Jun 2010

Bugfix in circ_hktest: lines 159 and 163 were switched.

29 Jun 2010

Bug in circ_clust fixed.

01 Dec 2010

19 Apr 2011

Updates fixing the bugs reported in the last few months.

Touched files:
kuipertest, plot, kurtosis, clust, axialmean, vmrnd, skewness, moment, median

08 Jun 2012

Bugfixes in wwtest, kuiper, median, hktest and added example files from paper.

08 Jun 2012

Update on median, wwtest, hktest, kuiper. Also added examples from paper.