1................................................. PROGRAM LEFTGAMM CLUSPAC MIXTURE MODEL CLUSTERING FOR UNIVARIATE DATA (DATA ON THE LINE) WITH UNEQUAL CLASS VARIANCES AND LEFTHAND PDF GAMMA DEVELOPED AND PROGRAMMED BY DR. STANLEY L. SCLOVE VERSION 1.2 18-DEC-1998 CMS DSN = LEFTGAMM CLUSPAC COPYRIGHT (C) 1991, 1992 STANLEY L. SCLOVE SIMULATED BETA MIXTURE: 299 (1,8), 400 (3,4), 300 (7,3) N = 999 DATA: MINIMUM OF SAMPLE: 0.000592 MAXIMUM OF SAMPLE: 0.985508 MEAN = 0.4061 M.L. ESTIMATE OF VARIANCE = 0.07080 SSDEVS = 70.7244 MINUS 2 LOG LIKELIHOOD = 189.7235 STDDEV = 0.2661 AIC = 193.7235 KASHYAP CRITERION = 210.7877 1 K = 3 CLUSTERS INITIAL PRIOR PROBS, MEANS AND VARIANCES: 1 0.30 0.17 0.01 2 0.40 0.43 0.04 3 0.30 0.77 0.02 WGSS = 25.4972 MINUS 2 LOG LIKELIHOOD = 99.2035 WGMS = 0.0256 STD.ERROR=SQRT(WGMS) = 0.1600 ITERATION 1 BOUNDARIES: 0.353 0.545 MEANS: 0.147 0.412 0.720 WGSS = 23.2792 MINUS 2 LOG LIKELIHOOD = -138.9753 WGMS = 0.0234 STD.ERROR=SQRT(WGMS) = 0.1529 ITERATION 2 BOUNDARIES: 0.349 0.552 MEANS: 0.134 0.424 0.718 WGSS = 21.6337 MINUS 2 LOG LIKELIHOOD = -198.8070 WGMS = 0.0217 STD.ERROR=SQRT(WGMS) = 0.1474 ITERATION 3 BOUNDARIES: 0.346 0.560 MEANS: 0.129 0.428 0.722 WGSS = 20.5051 MINUS 2 LOG LIKELIHOOD = -208.9563 WGMS = 0.0206 STD.ERROR=SQRT(WGMS) = 0.1435 ITERATION 4 BOUNDARIES: 0.347 0.567 MEANS: 0.127 0.429 0.726 WGSS = 19.6287 MINUS 2 LOG LIKELIHOOD = -212.3623 WGMS = 0.0197 STD.ERROR=SQRT(WGMS) = 0.1404 ITERATION 5 BOUNDARIES: 0.349 0.572 MEANS: 0.127 0.429 0.730 WGSS = 18.9265 MINUS 2 LOG LIKELIHOOD = -214.4731 WGMS = 0.0190 STD.ERROR=SQRT(WGMS) = 0.1378 ITERATION 6 BOUNDARIES: 0.352 0.576 MEANS: 0.126 0.429 0.732 WGSS = 18.3635 MINUS 2 LOG LIKELIHOOD = -215.8700 WGMS = 0.0184 STD.ERROR=SQRT(WGMS) = 0.1358 ITERATION 7 BOUNDARIES: 0.354 0.579 MEANS: 0.126 0.429 0.735 WGSS = 17.9138 MINUS 2 LOG LIKELIHOOD = -216.7657 WGMS = 0.0180 STD.ERROR=SQRT(WGMS) = 0.1341 ITERATION 8 BOUNDARIES: 0.355 0.582 MEANS: 0.125 0.429 0.736 WGSS = 17.5557 MINUS 2 LOG LIKELIHOOD = -217.3161 WGMS = 0.0176 STD.ERROR=SQRT(WGMS) = 0.1328 ITERATION 9 BOUNDARIES: 0.356 0.583 MEANS: 0.125 0.429 0.737 WGSS = 17.2704 MINUS 2 LOG LIKELIHOOD = -217.6406 WGMS = 0.0173 STD.ERROR=SQRT(WGMS) = 0.1317 ITERATION 10 BOUNDARIES: 0.358 0.585 MEANS: 0.125 0.429 0.738 WGSS = 17.0429 MINUS 2 LOG LIKELIHOOD = -217.8230 WGMS = 0.0171 STD.ERROR=SQRT(WGMS) = 0.1308 ITERATION 11 BOUNDARIES: 0.358 0.586 MEANS: 0.125 0.429 0.739 WGSS = 16.8607 MINUS 2 LOG LIKELIHOOD = -217.9182 WGMS = 0.0169 STD.ERROR=SQRT(WGMS) = 0.1301 ITERATION 12 BOUNDARIES: 0.359 0.587 MEANS: 0.125 0.429 0.739 NO CASE CHANGED CLUSTERS IN THIS ITERATION. MEANS: 0.125 0.429 0.739 PROBS: 0.329 0.422 0.249 NUMBERS: 316 421 262 VARIANCES: 0.014 0.023 0.009 STD.DEVS.: 0.120 0.152 0.097 R: 1.07 B: 0.12 M.L. ESTIMATE OF COMMON VARIANCE = 0.01688 NUMBER OF PARAMETERS = 8 AIC = -201.9182 SCHWARZ CRITERION = -162.6642 KASHYAP CRITERION = -151.1120