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 *** * Olzak & Wickens (1983) The interpretation of detection data * through direct multivariate frequency analysis. * Psych Bulletin, 93, 574-585. * * Subject B: Log-multiplicative model, 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 [ 69 6 1 1 0 0 34 20 10 3 1 0 43 24 13 9 1 0 78 40 20 6 0 1 32 38 17 5 4 0 5 14 3 2 0 0 10 5 2 11 16 28 8 5 11 43 27 38 9 6 7 28 32 45 8 6 14 19 23 22 4 5 7 6 18 18 0 1 2 3 5 8 4 1 0 0 0 0 5 3 2 1 0 0 8 6 3 1 0 0 36 25 18 3 1 0 83 69 26 6 1 0 127 50 12 7 2 0 5 0 1 4 4 9 0 1 3 6 9 27 2 3 2 11 27 20 9 12 11 10 23 31 16 7 5 19 33 40 21 14 13 20 21 61 ] *** STATISTICS *** Number of iterations = 5000 Converge criterion = 0.0008438406 X-squared = 825.7353 (0.0000) L-squared = 748.8086 (0.0000) Cressie-Read = 769.9370 (0.0000) Dissimilarity index = 0.2283 Degrees of freedom = 117 Log-likelihood = -8931.33827 Number of parameters = 26 (+1) Sample size = 2000.0 BIC(L-squared) = -140.4970 AIC(L-squared) = 514.8086 BIC(log-likelihood) = 18060.3000 AIC(log-likelihood) = 17914.6765 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 69.000 17.829 12.119 1 1 2 6.000 10.601 -1.413 1 1 3 1.000 6.432 -2.142 1 1 4 1.000 3.991 -1.497 1 1 5 0.000 1.186 -1.089 1 1 6 0.000 0.270 -0.520 1 2 1 34.000 21.345 2.739 1 2 2 20.000 12.697 2.049 1 2 3 10.000 7.742 0.812 1 2 4 3.000 4.871 -0.848 1 2 5 1.000 1.470 -0.387 1 2 6 0.000 0.341 -0.584 1 3 1 43.000 27.753 2.894 1 3 2 24.000 16.506 1.845 1 3 3 13.000 10.038 0.935 1 3 4 9.000 6.270 1.090 1 3 5 1.000 1.876 -0.640 1 3 6 0.000 0.432 -0.657 1 4 1 78.000 49.827 3.991 1 4 2 40.000 29.611 1.909 1 4 3 20.000 17.841 0.511 1 4 4 6.000 10.865 -1.476 1 4 5 0.000 3.160 -1.778 1 4 6 1.000 0.702 0.356 1 5 1 32.000 57.244 -3.337 1 5 2 38.000 34.002 0.686 1 5 3 17.000 20.369 -0.746 1 5 4 5.000 12.210 -2.063 1 5 5 4.000 3.488 0.274 1 5 6 0.000 0.758 -0.871 1 6 1 5.000 48.456 -6.243 1 6 2 14.000 28.779 -2.755 1 6 3 3.000 17.215 -3.426 1 6 4 2.000 10.278 -2.582 1 6 5 0.000 2.923 -1.710 1 6 6 0.000 0.632 -0.795 2 1 1 10.000 5.778 1.757 2 1 2 5.000 3.589 0.745 2 1 3 2.000 3.651 -0.864 2 1 4 11.000 9.316 0.552 2 1 5 16.000 13.656 0.634 2 1 6 28.000 21.553 1.389 2 2 1 8.000 9.369 -0.447 2 2 2 5.000 5.822 -0.341 2 2 3 11.000 5.952 2.069 2 2 4 43.000 15.397 7.034 2 2 5 27.000 22.922 0.852 2 2 6 38.000 36.860 0.188 2 3 1 9.000 10.378 -0.428 2 3 2 6.000 6.448 -0.176 2 3 3 7.000 6.574 0.166 2 3 4 28.000 16.885 2.705 2 3 5 32.000 24.932 1.416 2 3 6 45.000 39.698 0.841 2 4 1 8.000 10.626 -0.806 2 4 2 6.000 6.597 -0.232 2 4 3 14.000 6.664 2.842 2 4 4 19.000 16.688 0.566 2 4 5 23.000 23.946 -0.193 2 4 6 22.000 36.830 -2.444 2 5 1 4.000 8.594 -1.567 2 5 2 5.000 5.333 -0.144 2 5 3 7.000 5.356 0.710 2 5 4 6.000 13.201 -1.982 2 5 5 18.000 18.607 -0.141 2 5 6 18.000 28.006 -1.891 2 6 1 0.000 6.661 -2.581 2 6 2 1.000 4.133 -1.541 2 6 3 2.000 4.145 -1.053 2 6 4 3.000 10.175 -2.249 2 6 5 5.000 14.278 -2.455 2 6 6 8.000 21.373 -2.893 3 1 1 4.000 12.582 -2.419 3 1 2 1.000 7.125 -2.295 3 1 3 0.000 2.427 -1.558 3 1 4 0.000 0.311 -0.557 3 1 5 0.000 0.016 -0.125 3 1 6 0.000 0.000 -0.020 3 2 1 5.000 10.734 -1.750 3 2 2 3.000 6.081 -1.249 3 2 3 2.000 2.082 -0.057 3 2 4 1.000 0.270 1.405 3 2 5 0.000 0.014 -0.117 3 2 6 0.000 0.000 -0.019 3 3 1 8.000 16.692 -2.128 3 3 2 6.000 9.454 -1.123 3 3 3 3.000 3.228 -0.127 3 3 4 1.000 0.416 0.906 3 3 5 0.000 0.021 -0.145 3 3 6 0.000 0.001 -0.024 3 4 1 36.000 56.112 -2.685 3 4 2 25.000 31.756 -1.199 3 4 3 18.000 10.743 2.214 3 4 4 3.000 1.349 1.422 3 4 5 1.000 0.066 3.636 3 4 6 0.000 0.002 -0.041 3 5 1 83.000 95.411 -1.271 3 5 2 69.000 53.971 2.046 3 5 3 26.000 18.153 1.842 3 5 4 6.000 2.243 2.508 3 5 5 1.000 0.108 2.718 3 5 6 0.000 0.003 -0.052 3 6 1 127.000 89.117 4.013 3 6 2 50.000 50.405 -0.057 3 6 3 12.000 16.929 -1.198 3 6 4 7.000 2.084 3.406 3 6 5 2.000 0.100 6.019 3 6 6 0.000 0.002 -0.050 4 1 1 5.000 6.814 -0.695 4 1 2 0.000 4.222 -2.055 4 1 3 1.000 4.170 -1.552 4 1 4 4.000 9.812 -1.856 4 1 5 4.000 13.126 -2.519 4 1 6 9.000 18.543 -2.216 4 2 1 0.000 10.859 -3.295 4 2 2 1.000 6.732 -2.209 4 2 3 3.000 6.681 -1.424 4 2 4 6.000 15.938 -2.489 4 2 5 9.000 21.653 -2.719 4 2 6 27.000 31.166 -0.746 4 3 1 2.000 12.140 -2.910 4 3 2 3.000 7.524 -1.649 4 3 3 2.000 7.448 -1.996 4 3 4 11.000 17.639 -1.581 4 3 5 27.000 23.769 0.663 4 3 6 20.000 33.876 -2.384 4 4 1 9.000 12.836 -1.071 4 4 2 12.000 7.949 1.437 4 4 3 11.000 7.796 1.147 4 4 4 10.000 18.003 -1.886 4 4 5 23.000 23.576 -0.119 4 4 6 31.000 32.456 -0.256 4 5 1 16.000 10.592 1.662 4 5 2 7.000 6.556 0.173 4 5 3 5.000 6.393 -0.551 4 5 4 19.000 14.531 1.172 4 5 5 33.000 18.692 3.309 4 5 6 40.000 25.181 2.953 4 6 1 21.000 8.251 4.438 4 6 2 14.000 5.107 3.935 4 6 3 13.000 4.972 3.600 4 6 4 20.000 11.257 2.606 4 6 5 21.000 14.416 1.734 4 6 6 61.000 19.315 9.485 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 1.5813 4.8611 S 1 0.2633 1.3012 2 0.8124 2.2532 3 -1.9570 0.1413 4 0.8814 2.4142 X 1 -0.5646 0.5686 2 -0.3031 0.7385 3 -0.0836 0.9198 4 0.3508 1.4203 5 0.3954 1.4850 6 0.2051 1.2276 Y 1 1.2713 3.5656 2 0.7602 2.1386 3 0.3627 1.4372 4 0.1655 1.1800 5 -0.7322 0.4809 6 -1.8276 0.1608 type 2 association (row=S column=X slab=) association 1.0000 row -0.1199 0.4608 -0.7684 0.4275 adj row -0.1199 0.4608 -0.7684 0.4275 column 0.0049 0.0106 0.0076 -0.0028 -0.0093 -0.0109 adj column 0.0049 0.0106 0.0076 -0.0028 -0.0093 -0.0109 slab 92.9848 adj slab 92.9848 type 2 association (row=S column=Y slab=) association 1.0000 row -0.1199 0.4608 -0.7684 0.4275 adj row -0.1199 0.4608 -0.7684 0.4275 column -0.0360 -0.0352 -0.0256 0.0006 0.0302 0.0660 adj column -0.0360 -0.0352 -0.0256 0.0006 0.0302 0.0660 slab 92.9848 adj slab 92.9848 type 2 association (row=X column=Y slab=) association 1.0000 row 0.0049 0.0106 0.0076 -0.0028 -0.0093 -0.0109 adj row 0.0049 0.0106 0.0076 -0.0028 -0.0093 -0.0109 column -0.0360 -0.0352 -0.0256 0.0006 0.0302 0.0660 adj column -0.0360 -0.0352 -0.0256 0.0006 0.0302 0.0660 slab 92.9848 adj slab 92.9848