My first thought is to use a random permutation test. In this setting the main question you need to ask is what distance measure do you want to use between variance matrices -- there are lots of choices. One that I've found useful is the absolute value of the maximum eigenvalue of the difference of the matrices.
If you have a hypothesis about how the variances may differ, then you should be able to come up with a more powerful statistic. Patrick Burns [EMAIL PROTECTED] +44 (0)20 8525 0696 http://www.burns-stat.com (home of S Poetry and "A Guide for the Unwilling S User") Aldi Kraja wrote: >Hi, >Using package gclus in R, I have created some graphs that show the >trends within subgroups of data and correlations among 9 variables (v1-v9). >Being interested for more details on these data I have produced also the >var-covar matrices. >Question: From a pair of two subsets of data (with 9 variables each, I >have two var-covar matrices for each subgroup, that differ for a >treatment on one group (treatment A) vs (non-Treatment A). > >Is there a software that can compare if two var-covar matrices are >statistically the same? > >Below are a pair of two matrices, from several others. >Thank you in advance for any input. >Aldi > > First group var-covar matrix (the data were under treatment a) > >v1 v2 v3 v4 v5 >v6 v7 v8 v9 > > > >v1 730.87 3.406 -283.41 -74.68 >107.57 -1355.13 -112.46 14.000 5.776 > >v2 3.41 24.950 105.45 -121.31 >-307.68 -285.40 29.65 -2.500 -7.796 > >v3 -283.41 105.451 6292.19 -2676.46 >-970.80 29296.23 10715.29 3.156 -66.313 > >v4 -74.68 -121.307 -2676.46 124492.30 >-2289.47 -20377.34 -409.71 183.500 563.102 > >v5 107.57 -307.681 -970.80 -2289.47 >7045.62 12118.09 954.51 38.258 96.355 > >v6 -1355.13 -285.404 29296.23 -20377.34 >12118.09 218555.93 70126.71 137.000 -130.667 > >v7 -112.46 29.645 10715.29 -409.71 >954.51 70126.71 28239.57 67.989 -26.370 > >v8 14.00 -2.500 3.16 183.50 >38.26 137.00 67.99 24.500 9.000 > >v9 5.78 -7.796 -66.31 563.10 >96.35 -130.67 -26.37 9.000 22.776 > > > > > > Second group var-covar matrix (the data were NOT under treatment a) > >v1 v2 v3 v4 v5 >v6 v7 v8 v9 > > > >v1 2696.25 27.05 201.06 2745.54 >-344.39 540.48 654.20 34.363 7.623 > >v2 27.05 86.37 -96.89 -497.28 >-1185.10 -3108.71 -910.38 -4.254 -9.115 > >v3 201.06 -96.89 10647.26 8378.07 > 595.81 66122.43 26237.21 -65.093 -51.998 > >v4 2745.54 -497.28 8378.07 408391.25 >-3887.28 40477.40 30652.01 450.539 50.311 > >v5 -344.39 -1185.10 595.81 -3887.28 >29204.00 65320.00 15238.41 -98.237 102.975 > >v6 540.48 -3108.71 66122.43 40477.40 >65320.00 549955.14 194691.90 -555.552 -95.210 > >v7 654.20 -910.38 26237.21 30652.01 >15238.41 194691.90 82698.88 -70.417 -75.585 > >v8 34.36 -4.25 -65.09 450.54 >-98.24 -555.55 -70.42 79.689 8.164 > >v9 7.62 -9.11 -52.00 50.31 >102.97 -95.21 -75.58 8.164 30.492 > >______________________________________________ >[email protected] mailing list >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > > > > > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
