LEM: log-linear and event history analysis with missing data. Developed by Jeroen K. Vermunt (c), Tilburg University, The Netherlands. Version 1.2 (July 10, 1998). *** INPUT *** * Wickens (1992) MLE of a multivariate guassian rating * model with excluded data. J Math Psych, 36, 213-234. * Subject A (Wickens & Olzak 1989) * * Log-multiplicative models: graph 3 (b) * man 3 dim 4 6 6 lab S X Y mod {S X Y XY ass2(S,X,-,7a) ass2(S,Y,-,7a) ass2(X,Y,-,7a)} ass_equ [ 1 2 4 1 3 4 2 3 4 ] ass_res [ 0 2 3 0 2 3 2 2 3 ] ass_phi [ 1 1 1 ] nco dat[ 44 4 9 7 6 7 13 30 20 8 14 7 9 23 17 17 3 0 16 17 10 20 2 2 5 4 9 10 4 0 3 3 0 1 4 1 7 4 5 5 14 69 5 7 13 15 38 37 6 7 8 10 10 15 4 12 5 13 6 14 2 3 1 1 3 5 0 0 1 1 1 3 8 2 2 1 0 4 5 5 5 5 5 3 8 10 7 4 1 1 12 17 15 13 2 2 12 17 19 18 10 4 31 29 25 24 12 12 4 1 2 0 4 37 0 4 0 1 8 25 1 3 3 7 8 15 4 4 8 17 12 21 3 12 8 11 20 20 11 8 12 11 12 33 ] *** STATISTICS *** Number of iterations = 783 Converge criterion = 0.0000009855 X-squared = 406.0903 (0.0000) L-squared = 448.6117 (0.0000) Cressie-Read = 408.9197 (0.0000) Dissimilarity index = 0.1951 Degrees of freedom = 92 Log-likelihood = -6565.28940 Number of parameters = 51 (+1) Sample size = 1399.0 BIC(L-squared) = -217.7915 AIC(L-squared) = 264.6117 BIC(log-likelihood) = 13499.9980 AIC(log-likelihood) = 13232.5788 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 44.000 19.244 5.643 1 1 2 4.000 3.340 0.361 1 1 3 9.000 5.451 1.520 1 1 4 7.000 3.766 1.667 1 1 5 6.000 5.970 0.012 1 1 6 7.000 28.422 -4.018 1 2 1 13.000 7.024 2.255 1 2 2 30.000 13.993 4.279 1 2 3 20.000 11.532 2.493 1 2 4 8.000 8.448 -0.154 1 2 5 14.000 16.348 -0.581 1 2 6 7.000 17.694 -2.542 1 3 1 9.000 7.145 0.694 1 3 2 23.000 13.049 2.755 1 3 3 17.000 10.646 1.947 1 3 4 17.000 11.548 1.604 1 3 5 3.000 6.154 -1.272 1 3 6 0.000 8.534 -2.921 1 4 1 16.000 9.709 2.019 1 4 2 17.000 14.155 0.756 1 4 3 10.000 10.847 -0.257 1 4 4 20.000 18.937 0.244 1 4 5 2.000 6.657 -1.805 1 4 6 2.000 11.734 -2.842 1 5 1 5.000 3.764 0.637 1 5 2 4.000 6.819 -1.079 1 5 3 9.000 7.144 0.694 1 5 4 10.000 8.980 0.341 1 5 5 4.000 10.028 -1.904 1 5 6 0.000 7.991 -2.827 1 6 1 3.000 4.452 -0.688 1 6 2 3.000 4.514 -0.713 1 6 3 0.000 4.399 -2.097 1 6 4 1.000 5.273 -1.861 1 6 5 4.000 5.575 -0.667 1 6 6 1.000 9.714 -2.796 2 1 1 7.000 16.439 -2.328 2 1 2 4.000 3.363 0.348 2 1 3 5.000 5.669 -0.281 2 1 4 5.000 5.175 -0.077 2 1 5 14.000 12.971 0.286 2 1 6 69.000 65.125 0.480 2 2 1 5.000 5.848 -0.351 2 2 2 7.000 13.730 -1.816 2 2 3 13.000 11.690 0.383 2 2 4 15.000 11.315 1.095 2 2 5 38.000 34.619 0.575 2 2 6 37.000 39.517 -0.400 2 3 1 6.000 4.419 0.752 2 3 2 7.000 9.511 -0.814 2 3 3 8.000 8.016 -0.006 2 3 4 10.000 11.489 -0.439 2 3 5 10.000 9.681 0.103 2 3 6 15.000 14.157 0.224 2 4 1 4.000 4.000 -0.000 2 4 2 12.000 6.873 1.956 2 4 3 5.000 5.441 -0.189 2 4 4 13.000 12.551 0.127 2 4 5 6.000 6.976 -0.370 2 4 6 14.000 12.968 0.287 2 5 1 2.000 0.619 1.755 2 5 2 3.000 1.322 1.460 2 5 3 1.000 1.430 -0.360 2 5 4 1.000 2.376 -0.893 2 5 5 3.000 4.195 -0.583 2 5 6 5.000 3.526 0.785 2 6 1 0.000 0.348 -0.590 2 6 2 0.000 0.416 -0.645 2 6 3 1.000 0.418 0.899 2 6 4 1.000 0.663 0.414 2 6 5 1.000 1.108 -0.102 2 6 6 3.000 2.036 0.676 3 1 1 8.000 9.772 -0.567 3 1 2 2.000 1.417 0.490 3 1 3 2.000 2.231 -0.155 3 1 4 1.000 1.136 -0.127 3 1 5 0.000 1.090 -1.044 3 1 6 4.000 4.895 -0.405 3 2 1 5.000 3.668 0.695 3 2 2 5.000 6.105 -0.447 3 2 3 5.000 4.855 0.066 3 2 4 5.000 2.621 1.470 3 2 5 5.000 3.070 1.102 3 2 6 3.000 3.134 -0.076 3 3 1 8.000 5.169 1.245 3 3 2 10.000 7.886 0.753 3 3 3 7.000 6.208 0.318 3 3 4 4.000 4.962 -0.432 3 3 5 1.000 1.601 -0.475 3 3 6 1.000 2.094 -0.756 3 4 1 12.000 10.962 0.314 3 4 2 17.000 13.349 0.999 3 4 3 15.000 9.871 1.632 3 4 4 13.000 12.699 0.084 3 4 5 2.000 2.702 -0.427 3 4 6 2.000 4.493 -1.176 3 5 1 12.000 11.625 0.110 3 5 2 17.000 17.591 -0.141 3 5 3 19.000 17.785 0.288 3 5 4 18.000 16.473 0.376 3 5 5 10.000 11.136 -0.340 3 5 6 4.000 8.372 -1.511 3 6 1 31.000 31.083 -0.015 3 6 2 29.000 26.325 0.521 3 6 3 25.000 24.755 0.049 3 6 4 24.000 21.869 0.456 3 6 5 12.000 13.993 -0.533 3 6 6 12.000 23.003 -2.294 4 1 1 4.000 17.545 -3.234 4 1 2 1.000 2.881 -1.108 4 1 3 2.000 4.649 -1.229 4 1 4 0.000 2.923 -1.710 4 1 5 4.000 3.969 0.016 4 1 6 37.000 18.558 4.281 4 2 1 0.000 6.460 -2.542 4 2 2 4.000 12.173 -2.343 4 2 3 0.000 9.923 -3.150 4 2 4 1.000 6.615 -2.183 4 2 5 8.000 10.963 -0.895 4 2 6 25.000 11.654 3.909 4 3 1 1.000 7.267 -2.325 4 3 2 3.000 12.554 -2.696 4 3 3 3.000 10.130 -2.240 4 3 4 7.000 10.000 -0.949 4 3 5 8.000 4.564 1.608 4 3 6 15.000 6.215 3.524 4 4 1 4.000 11.329 -2.177 4 4 2 4.000 15.623 -2.941 4 4 3 8.000 11.841 -1.116 4 4 4 17.000 18.813 -0.418 4 4 5 12.000 5.664 2.662 4 4 6 21.000 9.805 3.575 4 5 1 3.000 5.992 -1.222 4 5 2 12.000 10.269 0.540 4 5 3 8.000 10.641 -0.810 4 5 4 11.000 12.172 -0.336 4 5 5 20.000 11.641 2.450 4 5 6 20.000 9.111 3.607 4 6 1 11.000 9.117 0.623 4 6 2 8.000 8.745 -0.252 4 6 3 12.000 8.428 1.230 4 6 4 11.000 9.195 0.595 4 6 5 12.000 8.324 1.274 4 6 6 33.000 14.247 4.968 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 1.9495 7.0253 S 1 0.1892 1.2083 2 -0.3618 0.6965 3 -0.0573 0.9443 4 0.2299 1.2585 X 1 -0.1782 0.8368 2 0.2439 1.2762 3 0.0218 1.0220 4 0.2822 1.3261 5 -0.0875 0.9163 6 -0.2823 0.7541 Y 1 -0.0947 0.9096 2 -0.0250 0.9753 3 -0.0540 0.9475 4 -0.0229 0.9774 5 -0.1373 0.8717 6 0.3339 1.3963 XY 1 1 2.0761 7.9732 1 2 -0.1299 0.8782 1 3 0.3125 1.3669 1 4 -0.7417 0.4763 1 5 -1.2408 0.2892 1 6 -0.2763 0.7586 2 1 0.6180 1.8553 2 2 0.8635 2.3715 2 3 0.6250 1.8683 2 4 -0.3518 0.7034 2 5 -0.6208 0.5375 2 6 -1.1339 0.3218 3 1 0.5313 1.7012 3 2 0.8179 2.2658 3 3 0.5946 1.8123 3 4 0.2275 1.2555 3 5 -0.9737 0.3777 3 6 -1.1977 0.3019 4 1 0.1323 1.1414 4 2 0.3684 1.4455 4 3 0.1172 1.1243 4 4 0.5227 1.6866 4 5 -0.6066 0.5452 4 6 -0.5340 0.5863 5 1 -1.4523 0.2340 5 2 -0.6035 0.5469 5 3 -0.4637 0.6290 5 4 0.2844 1.3289 5 5 1.4141 4.1127 5 6 0.8210 2.2728 6 1 -1.9054 0.1488 6 2 -1.3165 0.2681 6 3 -1.1857 0.3055 6 4 0.0589 1.0606 6 5 2.0278 7.5972 6 6 2.3209 10.1848 type 2 association (row=S column=X slab=) association 1.0000 row -0.0721 -0.7361 0.6555 0.1526 adj row -0.0721 -0.7361 0.6555 0.1526 column -1.4850 -1.4403 -0.9226 -0.2154 1.3836 2.6795 adj column -1.4850 -1.4403 -0.9226 -0.2154 1.3836 2.6795 slab 0.8650 adj slab 0.8650 type 2 association (row=S column=Y slab=) association 1.0000 row -0.0721 -0.7361 0.6555 0.1526 adj row -0.0721 -0.7361 0.6555 0.1526 column 0.7999 0.5140 0.4574 -0.0278 -0.8254 -0.9181 adj column 0.7999 0.5140 0.4574 -0.0278 -0.8254 -0.9181 slab 0.8650 adj slab 0.8650 type 2 association (row=X column=Y slab=) association 1.0000 row -1.4850 -1.4403 -0.9226 -0.2154 1.3836 2.6795 adj row -1.4850 -1.4403 -0.9226 -0.2154 1.3836 2.6795 column 0.7999 0.5140 0.4574 -0.0278 -0.8254 -0.9181 adj column 0.7999 0.5140 0.4574 -0.0278 -0.8254 -0.9181 slab 0.8650 adj slab 0.8650