[Octave-cvsupdate] SF.net SVN: octave:[7951] trunk/octave-forge/extra/NaN/inst/bland_altman.m

[<sch...@us...>]

Revision: 7951
          http://octave.svn.sourceforge.net/octave/?rev=7951&view=rev
Author:   schloegl
Date:     2010-11-25 23:37:17 +0000 (Thu, 25 Nov 2010)

Log Message:
-----------
add correlation and regression analysis, variance analysis, etc. 

Modified Paths:
--------------
    trunk/octave-forge/extra/NaN/inst/bland_altman.m

Modified: trunk/octave-forge/extra/NaN/inst/bland_altman.m
===================================================================
--- trunk/octave-forge/extra/NaN/inst/bland_altman.m	2010-11-25 18:07:59 UTC (rev 7950)
+++ trunk/octave-forge/extra/NaN/inst/bland_altman.m	2010-11-25 23:37:17 UTC (rev 7951)
@@ -14,9 +14,9 @@
 %
 %
 % References:
-%    	JM Bland and DG Altman, Measuring agreement in method comparison studies. 
-%	Statistical Methods in Medical Research, 1999; 8; 135. 
-%	doi:10.1177/09622802990080204
+% [1] JM Bland and DG Altman, Measuring agreement in method comparison studies. 
+%       Statistical Methods in Medical Research, 1999; 8; 135. 
+%       doi:10.1177/09622802990080204
 
 %	$Id$
 %	Copyright (C) 2010 by Alois Schloegl <a.s...@ie...>	
@@ -49,42 +49,56 @@
 D = data * [1;-1];
 M = data * [1;1]/2;
 
+RES.corrcoef = corrcoef(data(:,1),data(:,2),'spearman');
+[REs.cc,RES.p] = corrcoef(M,D,'spearman');
+if (RES.p<0.05)
+	warning('A regression model according to section 3.2 [1] should be used');
+	%% TODO: implement support for this type of data. 	
+	RES.a = [ones(size(data,1),1),D]\M;
+	RES.b = [ones(size(data,1),1),M]\D;
+end; 	
 
-RES.corrcoef = corrcoef(M,D,'spearman');
-
-
 if isempty(group)
 	G = [1:size(data,1)]';
 	m = ones(size(data,1),1);
 	d = D;
+	RES.Bias = mean(d,1);
+	RES.Var  = var(d);
 	
 elseif ~isempty(group)
 	%% TODO: this is not finished  	
-	[G,I,J] = unique (group)
+	warning('analysis of data with repetitions is experimental!')
+	[G,I,J] = unique (group);
+	R       = zeros(size(data));
 	m       = repmat(NaN,length(G),1);
 	n       = repmat(NaN,length(G),1);
 	d       = repmat(NaN,length(G),1);
 	d2      = repmat(NaN,length(G),1);
 	data2   = repmat(NaN,length(G),size(data,2));
 	SW2     = repmat(NaN,length(G),size(data,2));
-	for i   = 1:length(G)
-		ix = find(group==G(k));
+	for i   = 1:length(G),
+		ix         = find(group==G(i));
 		n(i)       = length(ix);
-		data2(i,:) = mean(data(ix,:));
-		SW2(i,:)   = var(data(ix,:));
+		[R(ix,:), data2(i,:)] = center(data(ix,:));
 		d(i)       = mean(D(ix,:));
 		m(i)       = mean(M(ix,:));
 		d2(i)      = mean(D(ix,:).^2);
-	end; 
-	RES.repeatability_coefficient = mean(SW2,2);
+	end;
+	RES.group = bland_altman(data2);
+	RES.repeatability_coefficient = var(R,1); 	% variance with factor group removed
+	RES.var_d_ = var(d);
+	RES.var_m_ = var(m);	
+	return; 
+
 	D = d; 
 	M = m;
 %	RES.sigma2_dw = 
+
+	RES.Bias = mean(d,1,[],n);
 end; 
 
-RES.Bias = mean(d,1,[],n);
 
 plot(M,D,'o', [min(M),max(M)]', [0,0]','k--', [min(M),max(M)]', [1,1,1; 0,1.96,-1.96]'*[RES.Bias;std(D)]*[1,1], 'k-');
-xlabel('mean')
-ylabel('difference')
+xlabel('mean');
+ylabel('difference');
 


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