@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.
Thank you for your inquiry. We decided not to switch this feature on in the default version (although the readme said so). In the version uploaded today, you just need to change line 33 to line 34 in sampleDichGauss01.m to use this feature.
Generally, higham.m may be used to obtain the closest correlation matrix to a predescribed matrix which is not a valid correlation matrix. In the case of sampling from binary variables, you can use this function to obtain a valid latent correlation matrix in case the usual transformation does not result in one.
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));
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!
@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.
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).
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!
@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.
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
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