Mercurial > hg > ltpda

annotate m-toolbox/classes/@ao/spcorr.m @ 45:a59cdb8aaf31 database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Tue, 06 Dec 2011 19:07:22 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % SPCORR calculate Spearman Rank-Order Correlation Coefficient
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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3 % Description:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % SPCORR calculates Spearman Rank-Order Correlation Coefficient
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % CALL: b = spcorr(a, pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % INPUT: a: are real valued AO. Number of input AOs should be >= 2.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % All the input AOs from the second are compared with the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % first one.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % OUTPUT: b: Spearman rank-order correlation coefficients. The
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % procinfo of b contain further information as:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % - pValue: Probability associated with the calculated rs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 % in the hypothesis that the correlation between the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % objects is zero.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18 % - TestRes: True or false on the basis of the test
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Daniele Nicolodi <nicolodi@science.unitn.it>
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19 % results. The null hypothesis for the test is that the two
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 % series are uncorrelated.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 % TestRes = 0 => Do not reject the null hypothesis at
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 % significance level alpha. (pValue >= alpha)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 % TestRes = 1 => Reject the null hypothesis at significance
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24 % level alpha. (pValue < alpha)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 % PARAMETERS:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 % - ALPHA is the desired significance level. It represents the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 % probability of rejecting the null hypothesis when it is true. The
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 % error done if the null hypothesis is rejected when it is true is
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 % called a Type I Error. Therefore, if the null hypothesis is true,
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 % alpha is the probability of a type I error. Default [0.05].
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 % NOTE:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 % The statistic of Spearman rank-order correlation coefficient is
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 % well approximated by a Student t distribution. Hypothesis test is
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 % then based on such statistic.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 % References:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 % [1] W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery,
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 % Numerical Recipes 3rd Edition: The Art of Scientific Computing,
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 % Cambridge University Press; 3 edition (September 10, 2007).
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'spcorr')">Parameters Description</a>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 % VERSION: $Id: spcorr.m,v 1.5 2011/07/06 15:41:31 luigi Exp $
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 function varargout = spcorr(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 % Collect all AOs and plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 if nargout == 0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 error('### SPCORR cannot be used as a modifier. Please give an output variable.');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 % check input
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Daniele Nicolodi <nicolodi@science.unitn.it>
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74 if numel(as)<2
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75 error('### Number of input AOs must be larger or equal to two.')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 % Collect input histories
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 inhists = [as.hist];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 % Apply defaults to plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 pl = applyDefaults(getDefaultPlist, varargin{:});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 % get parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 alpha = find(pl, 'ALPHA');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 if isa(alpha, 'ao')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 alpha = alpha.y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90 y1 = as(1).y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 bs = ao.initObjectWithSize(1, numel(as)-1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 % run over input aos
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 for ii=1:numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 y2 = as(ii+1).y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 if size(y1,1)~=size(y2,1)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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97 % reshape
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 y2 = y2.';
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 [rs,pValue,TestRes] =...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 utils.math.spcorr(y1, y2, alpha);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 bs(ii) = ao(rs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 bs(ii).setName(sprintf('SpCorr(%s,%s)', as(1).name, as(ii+1).name));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 plproc = plist(...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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106 'TestRes',TestRes,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107 'pValue',pValue);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 bs(ii).setProcinfo(plproc);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 bs(ii).addHistory(getInfo('None'), pl, [ao_invars(1) ao_invars(ii+1)], [inhists(1) inhists(ii+1)]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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111
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112 % Set output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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113 varargout = utils.helper.setoutputs(nargout, bs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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114
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Daniele Nicolodi <nicolodi@science.unitn.it>
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115 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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116
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117
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Daniele Nicolodi <nicolodi@science.unitn.it>
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118 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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119 % Get Info Object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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121 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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122 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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123 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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124 pl = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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125 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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126 sets = {'Default'};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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127 pl = getDefaultPlist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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128 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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129 % Build info object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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130 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: spcorr.m,v 1.5 2011/07/06 15:41:31 luigi Exp $', sets, pl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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131 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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132
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Daniele Nicolodi <nicolodi@science.unitn.it>
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133 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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134 % Get Default Plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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135 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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136 function plout = getDefaultPlist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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137 persistent pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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138 if ~exist('pl', 'var') || isempty(pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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139 pl = buildplist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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140 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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141 plout = pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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142 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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143
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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144 function plo = buildplist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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145 plo = plist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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146
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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147 p = param({'ALPHA', ['ALPHA is the desired significance level. It represents'...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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148 'the probability of rejecting the null hypothesis when it is true.'...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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149 'The error done if the null hypothesis is rejected when it is true is'...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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150 'called a Type I Error. Therefore, if the null hypothesis is true, alpha'...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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151 'is the probability of a type I error.']}, paramValue.DOUBLE_VALUE(0.05));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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152 plo.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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153
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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154 end