#NEXUS
[written Sun May 04 15:57:35 PDT 2008 by Mesquite  version 2.01+ (build j65) at Thrandina.local/10.0.1.8 (Wayne Maddison)]

BEGIN TAXA;
	TITLE species;
	DIMENSIONS NTAX=7;
	TAXLABELS
		a b c d e f g 
	;
	BLOCKID WM114cc20a14926;

END;



BEGIN TAXA;
	TITLE speciesSupplemented;
	DIMENSIONS NTAX=14;
	TAXLABELS
		a b c d e f g h i j g2 h2 i2 j2 
	;
	BLOCKID WM114cc20a14b27;

END;


BEGIN CHARACTERS;
	TITLE  space;
	LINK TAXA = species;
	DIMENSIONS  NCHAR=2;
	FORMAT DATATYPE = CONTINUOUS;
	MATRIX
	a   0.1889 0.0807
	b   0.0052 -0.0073
	c   0.0813 0.0744
	d   4.02 3.821
	e   4.03 3.942
	f   4.153 4.047
	g   -0.0721 1.149

;
		BLOCKID WM114cc20a1780;


END;
BEGIN CHARACTERS;
	TITLE  space2;
	LINK TAXA = speciesSupplemented;
	DIMENSIONS  NCHAR=2;
	FORMAT DATATYPE = CONTINUOUS;
	MATRIX
	a    0.1889 0.0807
	b    0.0052 -0.0073
	c    0.0813 0.0744
	d    4.02 3.821
	e    4.03 3.942
	f    4.153 4.047
	g    -0.0721 1.149
	h    0.0569 0.9767
	i    0.1782 0.8074
	j    0.0733 1.104
	g2   -0.028 0.9647
	h2   -0.0539 0.9679
	i2   0.0479 1.086
	j2   -0.0861 1.136

;
		BLOCKID WM114cc20a1890;


END;
BEGIN NOTES;

	TEXT  FILE TEXT = 'This file illustrates an alternative to PCA for visualizing multidimensional data among species, under development by W. Maddison & Eric Dryeson.  It''s called "Evolutionary PCA" because it finds the axes that maximize evolutionary change, not static variance.  It allows the change on the basal branch of clade B, for example, to be counted only once during the ordination.^n^nThe results are shown at left. Note that the two plots are nearly identical, because the blue clade''s influence is not dependent on the number of its species sampled.^n^nEPCA works by reconstructing ancestral states using squared-change parsimony, then performing a PCA on the vectors of change on each branch.  It provides a visualization method that emphasizes a more evolutionary interpretation of change.';

END;

BEGIN ASSUMPTIONS;
	TYPESET * UNTITLED  (CHARACTERS = space)  =  Squared:  1 -  2;
TYPESET * UNTITLED  (CHARACTERS = space2)  =  Squared:  1 -  2;

END;

BEGIN MESQUITECHARMODELS;
	ProbModelSet * UNTITLED  (CHARACTERS = space)  =  Brownian_default:  1 -  2;
ProbModelSet * UNTITLED  (CHARACTERS = space2)  =  Brownian_default:  1 -  2;
END;

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													setContextID 1603.967565686342.6573736635669045704;
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									setTaxa #835465756695001242;
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													setContextID 1733.967565688167.6875230740143230555;
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							namesAngle ?;
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							setNote Evolutionary_PCA;
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						endTell;
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						newAssistant #mesquite.ancstates.TraceCharacterHistory.TraceCharacterHistory;
					tell It;
						suspend ;
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						tell It;
							toggleLabels off;
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						endTell;
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						tell It;
							getCharacterSource  #mesquite.charMatrices.CharSrcCoordObed.CharSrcCoordObed;
							tell It;
								setCharacterSource #mesquite.charMatrices.StoredCharacters.StoredCharacters;
								tell It;
									setTaxa #1991710532467474024;
									setDataSet #3533841213786404322;
								endTell;
							endTell;
							setMethod  #mesquite.parsimony.ParsAncestralStates.ParsAncestralStates;
							tell It;
								setModelSource  #mesquite.parsimony.CurrentParsModels.CurrentParsModels;
								toggleMPRsMode off;
								getEmployee #mesquite.parsimony.ParsimonySquared.ParsimonySquared;
								tell It;
									toggleWeight on;
								endTell;
							endTell;
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						toggleShowLegend off;
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						toggleWeights on;
						resume ;
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					getEmployee #mesquite.ornamental.ColorTreeByPartition.ColorTreeByPartition;
					tell It;
						colorByPartition off;
					endTell;
				endTell;
				endTell;
				getEmployee #mesquite.trees.ColorBranches.ColorBranches;
				tell It;
					setColor Red;
					removeColor off;
				endTell;
				getEmployee #mesquite.ornamental.BranchNotes.BranchNotes;
				tell It;
					setAlwaysOn off;
				endTell;
				getEmployee #mesquite.ornamental.ColorTreeByPartition.ColorTreeByPartition;
				tell It;
					colorByPartition off;
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				getEmployee #mesquite.trees.TreeInfoValues.TreeInfoValues;
				tell It;
					panelOpen false;
				endTell;
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		newAssistant  #mesquite.ornamental.PictureWindowMaker.PictureWindowMaker;
		tell It;
			setPicture 'epca/Image2.gif';
			getWindow;
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				popAsTile false;
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			endTell;
			showWindow;
		endTell;
		newAssistant  #mesquite.basic.ExamplesNavigator.ExamplesNavigator;
		tell It;
			setNextFileName '16-epcaLiveSim.nex';
			setPrevFileName '14-epca.nex';
			getWindow;
			tell It;
					setExplanationSize 0;
					setAnnotationSize 0;
					setFontIncAnnot 0;
					setFontIncExp 0;
					setSize 412 312;
					setLocation 11 22;
					setFont SanSerif;
					setFontSize 10;
					setActive;
				setExplanation 'This file illustrates an alternative to PCA for visualizing multidimensional data among species, under development by Wayne Maddison & Eric Dryeson.  It''s called "Evolutionary PCA" because it finds the axes that maximize evolutionary change, not static variance.  It allows the change on the basal branch of clade B, for example, to be counted only once during the ordination.^n^nThe results are shown at right. Note that the two plots are nearly identical, because the blue clade''s influence is not dependent on the number of its species sampled.^n^nEPCA works by reconstructing ancestral states using squared-change parsimony, then performing a PCA on the vectors of change on each branch.  It provides a visualization method that emphasizes a more evolutionary interpretation of change.';
				setTitle EPCA;
				toggleFileNames off;
				setPrevButtonName Previous;
				setNextButtonName Next;
			endTell;
			showWindow;
		endTell;
		endTell;
end;