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-linear models using 4 variables: (HL,X,Y) * man 4 dim 2 2 6 6 lab L H X Y mod {L H X Y HL } 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 = 2 Converge criterion = 0.0000000000 X-squared = 1255.6950 (0.0000) L-squared = 1150.9452 (0.0000) Cressie-Read = 1171.6683 (0.0000) Dissimilarity index = 0.3650 Degrees of freedom = 130 Log-likelihood = -6916.45615 Number of parameters = 13 (+1) Sample size = 1399.0 BIC(L-squared) = 209.2885 AIC(L-squared) = 890.9452 BIC(log-likelihood) = 13927.0780 AIC(log-likelihood) = 13858.9123 Eigenvalues information matrix 1841.9051 1422.7427 1400.0002 1400.0002 1396.9988 264.8771 247.0271 223.7283 216.5818 216.5181 208.0163 200.9442 196.4153 *** FREQUENCIES *** L H X Y observed estimated std. res. 1 1 1 1 44.000 9.343 11.338 1 1 1 2 4.000 9.914 -1.878 1 1 1 3 9.000 8.949 0.017 1 1 1 4 7.000 9.650 -0.853 1 1 1 5 6.000 8.729 -0.924 1 1 1 6 7.000 14.783 -2.024 1 1 2 1 13.000 10.369 0.817 1 1 2 2 30.000 11.002 5.728 1 1 2 3 20.000 9.931 3.195 1 1 2 4 8.000 10.710 -0.828 1 1 2 5 14.000 9.687 1.386 1 1 2 6 7.000 16.405 -2.322 1 1 3 1 9.000 7.330 0.617 1 1 3 2 23.000 7.778 5.458 1 1 3 3 17.000 7.021 3.766 1 1 3 4 17.000 7.571 3.427 1 1 3 5 3.000 6.849 -1.471 1 1 3 6 0.000 11.598 -3.406 1 1 4 1 16.000 9.419 2.144 1 1 4 2 17.000 9.994 2.216 1 1 4 3 10.000 9.021 0.326 1 1 4 4 20.000 9.729 3.293 1 1 4 5 2.000 8.800 -2.292 1 1 4 6 2.000 14.903 -3.342 1 1 5 1 5.000 7.634 -0.953 1 1 5 2 4.000 8.100 -1.441 1 1 5 3 9.000 7.312 0.624 1 1 5 4 10.000 7.885 0.753 1 1 5 5 4.000 7.132 -1.173 1 1 5 6 0.000 12.079 -3.475 1 1 6 1 3.000 9.040 -2.009 1 1 6 2 3.000 9.591 -2.128 1 1 6 3 0.000 8.658 -2.942 1 1 6 4 1.000 9.337 -2.728 1 1 6 5 4.000 8.445 -1.530 1 1 6 6 1.000 14.302 -3.517 1 2 1 1 7.000 9.370 -0.774 1 2 1 2 4.000 9.942 -1.885 1 2 1 3 5.000 8.974 -1.327 1 2 1 4 5.000 9.678 -1.504 1 2 1 5 14.000 8.754 1.773 1 2 1 6 69.000 14.825 14.070 1 2 2 1 5.000 10.399 -1.674 1 2 2 2 7.000 11.033 -1.214 1 2 2 3 13.000 9.959 0.964 1 2 2 4 15.000 10.740 1.300 1 2 2 5 38.000 9.715 9.075 1 2 2 6 37.000 16.452 5.066 1 2 3 1 6.000 7.351 -0.498 1 2 3 2 7.000 7.800 -0.286 1 2 3 3 8.000 7.041 0.362 1 2 3 4 10.000 7.593 0.874 1 2 3 5 10.000 6.868 1.195 1 2 3 6 15.000 11.631 0.988 1 2 4 1 4.000 9.446 -1.772 1 2 4 2 12.000 10.023 0.625 1 2 4 3 5.000 9.047 -1.346 1 2 4 4 13.000 9.757 1.038 1 2 4 5 6.000 8.825 -0.951 1 2 4 6 14.000 14.946 -0.245 1 2 5 1 2.000 7.656 -2.044 1 2 5 2 3.000 8.123 -1.798 1 2 5 3 1.000 7.333 -2.339 1 2 5 4 1.000 7.908 -2.456 1 2 5 5 3.000 7.153 -1.553 1 2 5 6 5.000 12.113 -2.044 1 2 6 1 0.000 9.065 -3.011 1 2 6 2 0.000 9.619 -3.101 1 2 6 3 1.000 8.682 -2.607 1 2 6 4 1.000 9.363 -2.733 1 2 6 5 1.000 8.470 -2.567 1 2 6 6 3.000 14.343 -2.995 2 1 1 1 8.000 9.370 -0.448 2 1 1 2 2.000 9.942 -2.519 2 1 1 3 2.000 8.974 -2.328 2 1 1 4 1.000 9.678 -2.790 2 1 1 5 0.000 8.754 -2.959 2 1 1 6 4.000 14.825 -2.811 2 1 2 1 5.000 10.399 -1.674 2 1 2 2 5.000 11.033 -1.816 2 1 2 3 5.000 9.959 -1.571 2 1 2 4 5.000 10.740 -1.752 2 1 2 5 5.000 9.715 -1.513 2 1 2 6 3.000 16.452 -3.317 2 1 3 1 8.000 7.351 0.239 2 1 3 2 10.000 7.800 0.788 2 1 3 3 7.000 7.041 -0.015 2 1 3 4 4.000 7.593 -1.304 2 1 3 5 1.000 6.868 -2.239 2 1 3 6 1.000 11.631 -3.117 2 1 4 1 12.000 9.446 0.831 2 1 4 2 17.000 10.023 2.204 2 1 4 3 15.000 9.047 1.979 2 1 4 4 13.000 9.757 1.038 2 1 4 5 2.000 8.825 -2.298 2 1 4 6 2.000 14.946 -3.349 2 1 5 1 12.000 7.656 1.570 2 1 5 2 17.000 8.123 3.114 2 1 5 3 19.000 7.333 4.309 2 1 5 4 18.000 7.908 3.589 2 1 5 5 10.000 7.153 1.065 2 1 5 6 4.000 12.113 -2.331 2 1 6 1 31.000 9.065 7.285 2 1 6 2 29.000 9.619 6.249 2 1 6 3 25.000 8.682 5.538 2 1 6 4 24.000 9.363 4.783 2 1 6 5 12.000 8.470 1.213 2 1 6 6 12.000 14.343 -0.619 2 2 1 1 4.000 9.370 -1.754 2 2 1 2 1.000 9.942 -2.836 2 2 1 3 2.000 8.974 -2.328 2 2 1 4 0.000 9.678 -3.111 2 2 1 5 4.000 8.754 -1.607 2 2 1 6 37.000 14.825 5.759 2 2 2 1 0.000 10.399 -3.225 2 2 2 2 4.000 11.033 -2.117 2 2 2 3 0.000 9.959 -3.156 2 2 2 4 1.000 10.740 -2.972 2 2 2 5 8.000 9.715 -0.550 2 2 2 6 25.000 16.452 2.107 2 2 3 1 1.000 7.351 -2.343 2 2 3 2 3.000 7.800 -1.719 2 2 3 3 3.000 7.041 -1.523 2 2 3 4 7.000 7.593 -0.215 2 2 3 5 8.000 6.868 0.432 2 2 3 6 15.000 11.631 0.988 2 2 4 1 4.000 9.446 -1.772 2 2 4 2 4.000 10.023 -1.902 2 2 4 3 8.000 9.047 -0.348 2 2 4 4 17.000 9.757 2.319 2 2 4 5 12.000 8.825 1.069 2 2 4 6 21.000 14.946 1.566 2 2 5 1 3.000 7.656 -1.683 2 2 5 2 12.000 8.123 1.360 2 2 5 3 8.000 7.333 0.246 2 2 5 4 11.000 7.908 1.100 2 2 5 5 20.000 7.153 4.804 2 2 5 6 20.000 12.113 2.266 2 2 6 1 11.000 9.065 0.643 2 2 6 2 8.000 9.619 -0.522 2 2 6 3 12.000 8.682 1.126 2 2 6 4 11.000 9.363 0.535 2 2 6 5 12.000 8.470 1.213 2 2 6 6 33.000 14.343 4.926 *** LOG-LINEAR PARAMETERS *** * TABLE LHXY [or P(LHXY)] * effect beta std err z-value exp(beta) Wald df prob main 2.2491 9.4792 L 1 -0.0007 0.0267 -0.027 0.9993 2 0.0007 1.0007 0.00 1 0.979 H 1 -0.0007 0.0267 -0.027 0.9993 2 0.0007 1.0007 0.00 1 0.979 X 1 0.0609 0.0586 1.039 1.0628 2 0.1651 0.0563 2.933 1.1795 3 -0.1817 0.0646 -2.811 0.8338 4 0.0690 0.0584 1.181 1.0714 5 -0.1411 0.0636 -2.220 0.8684 6 0.0279 1.0282 19.80 5 0.001 Y 1 -0.0732 0.0622 -1.177 0.9294 2 -0.0140 0.0607 -0.230 0.9861 3 -0.1164 0.0633 -1.839 0.8901 4 -0.0409 0.0614 -0.666 0.9600 5 -0.1412 0.0639 -2.208 0.8683 6 0.3856 1.4705 56.57 5 0.000 LH 1 1 -0.0007 0.0267 -0.027 0.9993 1 2 0.0007 1.0007 2 1 0.0007 1.0007 2 2 -0.0007 0.9993 0.00 1 0.979