File EXAMPLE_E.dat
This example shows the dangers of local minima.

TITLE= Cox and Cox Local Minima Test Data
SUBTITLE= This example shows the danger of getting caught in a local minima.

Nobjects=12
DissimilarityList
0
0.099	0
0.033	0.022	0
0.183	0.114	0.042	0
0.148	0.224	0.059	0.068	0
0.198	0.039	0.053	0.085	0.051	0
0.462	0.266	0.322	0.435	0.268	0.025	0
0.628	0.442	0.444	0.406	0.240	0.129	0.014	0
0.113	0.070	0.046	0.047	0.034	0.002	0.106	0.129	0
0.173	0.119	0.162	0.331	0.177	0.039	0.089	0.237	0.071	0
0.434	0.419	0.339	0.505	0.469	0.390	0.315	0.349	0.151	0.430	0
0.762	0.633	0.781	0.700	0.758	0.625	0.469	0.618	0.440	0.538	0.607	0

STARTMDSAnalysisTypeNum=4
STARTBadnessFunctionNum=1
STARTDistanceFunctionNum=0
STARTDimensionsNum=2


Cox and Cox show a particularly bad test case taken from an SPSS manual 
that gives bad advice about the frequency of occurrence of local minima.  
It is quoted in a draft paper by Cox and Cox, available on the Internet at
http://www.ncl.ac.uk/mds/

Cox and Cox get Stress1=9.39% using a slightly different ordinal procedure 
than that used by PERMAP.  Before putting in an autoscaling function
for ordinal MDS, PERMAP (centered) gets Stress1=9.26%.  Now that PERMAP autoscales
it gets Stress1=13.4% about half the time.  Because Ordinal MDS is relative, 
the difference indicates nothing more than a scale change.










