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view m-toolbox/classes/@ao/spcorr.m @ 24:056f8e1e995e database-connection-manager
Properly record history in fromRepository constructors
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
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% SPCORR calculate Spearman Rank-Order Correlation Coefficient %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Description: % % SPCORR calculates Spearman Rank-Order Correlation Coefficient % % CALL: b = spcorr(a, pl) % % INPUT: a: are real valued AO. Number of input AOs should be >= 2. % All the input AOs from the second are compared with the % first one. % % OUTPUT: b: Spearman rank-order correlation coefficients. The % procinfo of b contain further information as: % - pValue: Probability associated with the calculated rs % in the hypothesis that the correlation between the % objects is zero. % - TestRes: True or false on the basis of the test % results. The null hypothesis for the test is that the two % series are uncorrelated. % TestRes = 0 => Do not reject the null hypothesis at % significance level alpha. (pValue >= alpha) % TestRes = 1 => Reject the null hypothesis at significance % level alpha. (pValue < alpha) % % PARAMETERS: % % - ALPHA is the desired significance level. It represents the % probability of rejecting the null hypothesis when it is true. The % error done if the null hypothesis is rejected when it is true is % called a Type I Error. Therefore, if the null hypothesis is true, % alpha is the probability of a type I error. Default [0.05]. % % NOTE: % The statistic of Spearman rank-order correlation coefficient is % well approximated by a Student t distribution. Hypothesis test is % then based on such statistic. % % References: % [1] W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, % Numerical Recipes 3rd Edition: The Art of Scientific Computing, % Cambridge University Press; 3 edition (September 10, 2007). % % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'spcorr')">Parameters Description</a> % % VERSION: $Id: spcorr.m,v 1.5 2011/07/06 15:41:31 luigi Exp $ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = spcorr(varargin) % Check if this is a call for parameters if utils.helper.isinfocall(varargin{:}) varargout{1} = getInfo(varargin{3}); return end import utils.const.* utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename); % Collect input variable names in_names = cell(size(varargin)); for ii = 1:nargin,in_names{ii} = inputname(ii);end % Collect all AOs and plists [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names); if nargout == 0 error('### SPCORR cannot be used as a modifier. Please give an output variable.'); end % check input if numel(as)<2 error('### Number of input AOs must be larger or equal to two.') end % Collect input histories inhists = [as.hist]; % Apply defaults to plist pl = applyDefaults(getDefaultPlist, varargin{:}); % get parameters alpha = find(pl, 'ALPHA'); if isa(alpha, 'ao') alpha = alpha.y; end y1 = as(1).y; bs = ao.initObjectWithSize(1, numel(as)-1); % run over input aos for ii=1:numel(bs) y2 = as(ii+1).y; if size(y1,1)~=size(y2,1) % reshape y2 = y2.'; end [rs,pValue,TestRes] =... utils.math.spcorr(y1, y2, alpha); bs(ii) = ao(rs); bs(ii).setName(sprintf('SpCorr(%s,%s)', as(1).name, as(ii+1).name)); plproc = plist(... 'TestRes',TestRes,... 'pValue',pValue); bs(ii).setProcinfo(plproc); bs(ii).addHistory(getInfo('None'), pl, [ao_invars(1) ao_invars(ii+1)], [inhists(1) inhists(ii+1)]); end % Set output varargout = utils.helper.setoutputs(nargout, bs); end %-------------------------------------------------------------------------- % Get Info Object %-------------------------------------------------------------------------- function ii = getInfo(varargin) if nargin == 1 && strcmpi(varargin{1}, 'None') sets = {}; pl = []; else sets = {'Default'}; pl = getDefaultPlist(); end % Build info object 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); end %-------------------------------------------------------------------------- % Get Default Plist %-------------------------------------------------------------------------- function plout = getDefaultPlist() persistent pl; if ~exist('pl', 'var') || isempty(pl) pl = buildplist(); end plout = pl; end function plo = buildplist() plo = plist(); p = param({'ALPHA', ['ALPHA is the desired significance level. It represents'... 'the probability of rejecting the null hypothesis when it is true.'... 'The error done if the null hypothesis is rejected when it is true is'... 'called a Type I Error. Therefore, if the null hypothesis is true, alpha'... 'is the probability of a type I error.']}, paramValue.DOUBLE_VALUE(0.05)); plo.append(p); end