Comments and Ratings

Descriptive help, reliable error checking, new version is really efficient, does exactly what the name says.

CLOCK -> DATE -> DATESTR -> STR2DOUBLE waste a lot of time: str2double(datestr(date,10))
Faster: c = clock; default = {c(1)};

Checking the number of inputs can be done easier and faster without VARARGIN, e.g.:
  function x = isleap(Year)
  switch nargin
  case 1, % Your error checks...
  case 0, c = clock; Year = c(1);
  otherwise error(...);
  end

The permanent disabling of the warning for logical conversion is not secure. You can omit this by simplifying the engine - instead of:
  warning('OFF','MATLAB:conversionToLogical')
  A=logical(mod(Year,4));
  B=logical(mod(Year,100));
  C=logical(mod(Year,400));
  x=ismember(Year,Year(~C | (~A & B)));
call just:
  x = ~mod(Year, 4) & (mod(Year, 100) | ~mod(Year, 400));
And suddenly the function is 150 times faster (400 times if called without argument).
Good help, H1-line, date, author and reference included. I'll rate this, if its efficiency is encreased.

Whenever I imput any other matrix than a 5 x 10 matrix into matlab, using your function "fleiss(X)"it gives an error message as follows:

EDU>> fleiss(X)
??? Error using ==> fleiss at 107
The raters are not the same for each rows

Can you tell me how to fix this?
 Thx

Thanks for the really quick reply. I appreciate it.

However, the program still crashes for me, with the same error, when I try the syntax you suggest. Maybe the version on the File Exchange is not quite the same as yours?

It seems to me that this function does not work for the case when none of the data are censored. (That shouldn't be a problem, should it?) For example, using only the first columns of your sample x1 and x2:

>> logrank(x1(:,1),x2(:,1))

causes a crash for me.

Thanks

I tested the new release and it is agreeing with other codes now. Michael, you may also wish to verify that the new version is working correctly.

I also think adding the cut off points as an additional x or y axis would be very useful to understand the trade off between sensitivity and sensibility.

Nice function, but I think it may have a bug, I am getting different results for significantly overlaping distributions when I compare to medcalc, and online calculators.

email me for more details: cpabrego@gmail.com

Thanks.

Great function! While the level of accuracy does not affect my needs, you may want to let others know if they are in need to to the minute or second, may want to validate the results. Comparing this functions results to the US Naval Observatory, the results differ by approximately 1-minute. I'm guessing this is an artifact of matlab and complier precision. Anything dealing with astronomy and floating point precision becomes significant.

http://aa.usno.navy.mil/data/docs/EarthSeasons.php

Perhaps, if you email me the data I can understand the error. I think that your input data matrix are in a wrong format.

You have partially right. I have fixed the bug and uploaded the new file. Thank you very much!

this m file is good,easy and useful.many thanks Giuseppe

I cannot use it

Jon was right. I have fixed the bug and uploaded the new version

I think there is an error in the quadratic loop. small caps j needs to be J.

good

Good function

Good stuff.

Is there an algorithm to use the max numbers of coins? Of course there is, as anybody who has taken half a course in dynamic programming will tell you. Your own code solves the problem in less than 10 lines :)

Only to correct and clarify my previous comment, and to recall to all that, with respect to the non parametric tests between two samples, two classes exist: (1) Independent, that can be both of the same size or not, being been able to utilize the Mann-Whitney’s test or the Wilcoxon ranks test; and (2) Dependent, that necessarily them should be of the same size, since is the same sample observed in two different circumstances or times, where applies the Wilcoxon signs and ranks test.

Also, I should clarify that in the personal thing I do not use the term unpaired to assign independent samples, since unpaired is synonym of unbalanced; neither paired for assign dependent, since paired is synonym of balanced. This by the fundamental reason that the concepts of independent or dependent arise for the itself nature of the data, and not because they are unpaired or paired.

The m-file here developed by Giuseppe it is a Wilcoxon’s test for two dependent samples for it is correct his comment that the data vectors must have the same length, and, if it is not, delete those uncoupled data. There isn't any other solutions.

So, my previous review comment is obsolete and should be ignored.

Thanks,

Prof. Antonio Trujillo-Ortiz

The code is writen very clearly, instructions are detailed and to the point. It did not take me more than 5 minutes to get run the test.

I agree with Urs. This submission does not provide anything valuable or useful (in terms of contents or coding) to the Matlab community. Yet it is rated amazingly high by a few of those people, who are usually very quick in annihilating such surrealistic contributions. The fact that a guru(?) has an emotional bond with the idea behind a program certainly cannot be sufficient to give out a 'reward'.
Walter.