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 (a) * man 3 dim 4 6 6 lab S X Y mod {S X Y 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 = 5000 Converge criterion = 0.0002492560 X-squared = 673.2197 (0.0000) L-squared = 645.1227 (0.0000) Cressie-Read = 640.4613 (0.0000) Dissimilarity index = 0.2492 Degrees of freedom = 117 Log-likelihood = -6663.54490 Number of parameters = 26 (+1) Sample size = 1399.0 BIC(L-squared) = -202.3683 AIC(L-squared) = 411.1227 BIC(log-likelihood) = 13515.4211 AIC(log-likelihood) = 13379.0898 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 44.000 9.518 11.177 1 1 2 4.000 10.152 -1.931 1 1 3 9.000 9.170 -0.056 1 1 4 7.000 10.461 -1.070 1 1 5 6.000 10.486 -1.385 1 1 6 7.000 17.768 -2.555 1 2 1 13.000 10.760 0.683 1 2 2 30.000 11.476 5.468 1 2 3 20.000 10.365 2.993 1 2 4 8.000 11.810 -1.109 1 2 5 14.000 11.789 0.644 1 2 6 7.000 19.942 -2.898 1 3 1 9.000 8.266 0.255 1 3 2 23.000 8.809 4.781 1 3 3 17.000 7.956 3.206 1 3 4 17.000 8.981 2.676 1 3 5 3.000 8.681 -1.928 1 3 6 0.000 14.495 -3.807 1 4 1 16.000 10.486 1.703 1 4 2 17.000 11.167 1.745 1 4 3 10.000 10.084 -0.027 1 4 4 20.000 11.295 2.590 1 4 5 2.000 10.624 -2.646 1 4 6 2.000 17.547 -3.712 1 5 1 5.000 6.688 -0.653 1 5 2 4.000 7.113 -1.167 1 5 3 9.000 6.422 1.017 1 5 4 10.000 7.079 1.098 1 5 5 4.000 6.303 -0.917 1 5 6 0.000 10.182 -3.191 1 6 1 3.000 5.222 -0.972 1 6 2 3.000 5.547 -1.081 1 6 3 0.000 5.008 -2.238 1 6 4 1.000 5.441 -1.904 1 6 5 4.000 4.609 -0.284 1 6 6 1.000 7.298 -2.331 2 1 1 7.000 10.604 -1.107 2 1 2 4.000 11.436 -2.199 2 1 3 5.000 10.342 -1.661 2 1 4 5.000 13.451 -2.304 2 1 5 14.000 21.200 -1.564 2 1 6 69.000 43.109 3.943 2 2 1 5.000 11.322 -1.879 2 2 2 7.000 12.209 -1.491 2 2 3 13.000 11.041 0.590 2 2 4 15.000 14.342 0.174 2 2 5 38.000 22.511 3.264 2 2 6 37.000 45.698 -1.287 2 3 1 6.000 5.594 0.172 2 3 2 7.000 6.027 0.396 2 3 3 8.000 5.450 1.092 2 3 4 10.000 7.014 1.128 2 3 5 10.000 10.660 -0.202 2 3 6 15.000 21.360 -1.376 2 4 1 4.000 4.890 -0.403 2 4 2 12.000 5.266 2.935 2 4 3 5.000 4.761 0.110 2 4 4 13.000 6.079 2.807 2 4 5 6.000 8.991 -0.998 2 4 6 14.000 17.820 -0.905 2 5 1 2.000 1.470 0.437 2 5 2 3.000 1.580 1.129 2 5 3 1.000 1.429 -0.359 2 5 4 1.000 1.795 -0.594 2 5 5 3.000 2.514 0.307 2 5 6 5.000 4.873 0.057 2 6 1 0.000 0.580 -0.762 2 6 2 0.000 0.623 -0.789 2 6 3 1.000 0.563 0.582 2 6 4 1.000 0.698 0.362 2 6 5 1.000 0.930 0.073 2 6 6 3.000 1.766 0.928 3 1 1 8.000 3.634 2.290 3 1 2 2.000 3.827 -0.934 3 1 3 2.000 3.452 -0.781 3 1 4 1.000 3.384 -1.296 3 1 5 0.000 2.011 -1.418 3 1 6 4.000 2.760 0.747 3 2 1 5.000 4.389 0.292 3 2 2 5.000 4.621 0.176 3 2 3 5.000 4.168 0.408 3 2 4 5.000 4.082 0.455 3 2 5 5.000 2.415 1.664 3 2 6 3.000 3.309 -0.170 3 3 1 8.000 5.616 1.006 3 3 2 10.000 5.910 1.683 3 3 3 7.000 5.329 0.724 3 3 4 4.000 5.170 -0.515 3 3 5 1.000 2.962 -1.140 3 3 6 1.000 4.006 -1.502 3 4 1 12.000 10.955 0.316 3 4 2 17.000 11.519 1.615 3 4 3 15.000 10.387 1.431 3 4 4 13.000 9.998 0.949 3 4 5 2.000 5.574 -1.514 3 4 6 2.000 7.457 -1.998 3 5 1 12.000 16.670 -1.144 3 5 2 17.000 17.504 -0.121 3 5 3 19.000 15.782 0.810 3 5 4 18.000 14.952 0.788 3 5 5 10.000 7.890 0.751 3 5 6 4.000 10.324 -1.968 3 6 1 31.000 28.624 0.444 3 6 2 29.000 30.021 -0.186 3 6 3 25.000 27.063 -0.397 3 6 4 24.000 25.273 -0.253 3 6 5 12.000 12.689 -0.193 3 6 6 12.000 16.274 -1.059 4 1 1 4.000 7.794 -1.359 4 1 2 1.000 8.277 -2.529 4 1 3 2.000 7.473 -2.002 4 1 4 0.000 8.104 -2.847 4 1 5 4.000 6.819 -1.080 4 1 6 37.000 10.768 7.994 4 2 1 0.000 9.007 -3.001 4 2 2 4.000 9.565 -1.799 4 2 3 0.000 8.635 -2.939 4 2 4 1.000 9.353 -2.731 4 2 5 8.000 7.837 0.058 4 2 6 25.000 12.356 3.597 4 3 1 1.000 8.208 -2.516 4 3 2 3.000 8.710 -1.935 4 3 3 3.000 7.862 -1.734 4 3 4 7.000 8.437 -0.495 4 3 5 8.000 6.845 0.441 4 3 6 15.000 10.652 1.332 4 4 1 4.000 12.023 -2.314 4 4 2 4.000 12.750 -2.450 4 4 3 8.000 11.508 -1.034 4 4 4 17.000 12.252 1.356 4 4 5 12.000 9.675 0.748 4 4 6 21.000 14.892 1.583 4 5 1 3.000 10.257 -2.266 4 5 2 12.000 10.862 0.345 4 5 3 8.000 9.803 -0.576 4 5 4 11.000 10.272 0.227 4 5 5 20.000 7.678 4.447 4 5 6 20.000 11.559 2.483 4 6 1 11.000 10.424 0.178 4 6 2 8.000 11.026 -0.911 4 6 3 12.000 9.949 0.650 4 6 4 11.000 10.277 0.226 4 6 5 12.000 7.308 1.736 4 6 6 33.000 10.784 6.765 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 2.0334 7.6400 S 1 0.1775 1.1942 2 -0.3502 0.7046 3 -0.0506 0.9506 4 0.2233 1.2502 X 1 0.0618 1.0637 2 0.1897 1.2089 3 -0.0323 0.9682 4 0.2406 1.2720 5 -0.1383 0.8708 6 -0.3215 0.7251 Y 1 -0.0686 0.9337 2 -0.0072 0.9928 3 -0.1094 0.8964 4 -0.0143 0.9858 5 -0.1384 0.8707 6 0.3380 1.4021 type 2 association (row=S column=X slab=) association 1.0000 row -0.0873 -0.7312 0.6569 0.1616 adj row -0.0873 -0.7312 0.6569 0.1616 column -0.0927 -0.0868 -0.0409 -0.0022 0.0759 0.1467 adj column -0.0927 -0.0868 -0.0409 -0.0022 0.0759 0.1467 slab 14.9515 adj slab 14.9515 type 2 association (row=S column=Y slab=) association 1.0000 row -0.0873 -0.7312 0.6569 0.1616 adj row -0.0873 -0.7312 0.6569 0.1616 column 0.0267 0.0255 0.0254 0.0118 -0.0352 -0.0542 adj column 0.0267 0.0255 0.0254 0.0118 -0.0352 -0.0542 slab 14.9515 adj slab 14.9515 type 2 association (row=X column=Y slab=) association 1.0000 row -0.0927 -0.0868 -0.0409 -0.0022 0.0759 0.1467 adj row -0.0927 -0.0868 -0.0409 -0.0022 0.0759 0.1467 column 0.0267 0.0255 0.0254 0.0118 -0.0352 -0.0542 adj column 0.0267 0.0255 0.0254 0.0118 -0.0352 -0.0542 slab 14.9515 adj slab 14.9515