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 *** * ANES data: item1 x item2 x item3 x item4 * * man 4 dim 4 4 3 3 lab A B C D model {A B C D } dat[ 11 2 3 1 2 1 0 0 5 9 2 0 4 1 0 1 0 4 17 7 4 9 7 2 3 0 8 1 0 0 0 0 0 0 0 2 53 11 5 11 5 1 3 1 9 27 10 4 8 6 0 1 1 5 34 31 9 15 16 4 6 1 28 5 3 4 1 0 1 0 0 5 330 52 14 40 16 1 2 0 12 50 16 3 9 7 0 1 1 7 43 16 5 8 3 1 1 1 7 17 0 4 1 1 0 0 0 12 2 0 2 1 0 1 0 0 0 3 0 0 0 1 0 0 0 2 3 0 4 0 0 0 0 0 7 8 0 3 0 2 1 0 1 24 ] *** STATISTICS *** Number of iterations = 2 Converge criterion = 0.0000000000 X-squared = 5792.4860 (0.0000) L-squared = 1189.0066 (0.0000) Cressie-Read = 2509.8813 (0.0000) Dissimilarity index = 0.3501 Degrees of freedom = 133 Log-likelihood = -4663.28369 Number of parameters = 10 (+1) Sample size = 1176.0 BIC(L-squared) = 248.7133 AIC(L-squared) = 923.0066 BIC(log-likelihood) = 9397.2661 AIC(log-likelihood) = 9346.5674 Eigenvalues information matrix 830.5082 675.5788 502.4499 462.2926 394.0385 324.9668 324.7933 283.5405 216.2268 137.7516 *** FREQUENCIES *** A B C D observed estimated std. res. 1 1 1 1 11.000 23.780 -2.621 1 1 1 2 2.000 7.176 -1.932 1 1 1 3 3.000 6.886 -1.481 1 1 2 1 1.000 5.406 -1.895 1 1 2 2 2.000 1.631 0.289 1 1 2 3 1.000 1.565 -0.452 1 1 3 1 0.000 4.629 -2.152 1 1 3 2 0.000 1.397 -1.182 1 1 3 3 5.000 1.341 3.161 1 2 1 1 9.000 7.289 0.634 1 2 1 2 2.000 2.200 -0.135 1 2 1 3 0.000 2.111 -1.453 1 2 2 1 4.000 1.657 1.820 1 2 2 2 1.000 0.500 0.707 1 2 2 3 0.000 0.480 -0.693 1 2 3 1 1.000 1.419 -0.352 1 2 3 2 0.000 0.428 -0.654 1 2 3 3 4.000 0.411 5.599 1 3 1 1 17.000 11.950 1.461 1 3 1 2 7.000 3.606 1.787 1 3 1 3 4.000 3.460 0.290 1 3 2 1 9.000 2.716 3.812 1 3 2 2 7.000 0.820 6.826 1 3 2 3 2.000 0.787 1.368 1 3 3 1 3.000 2.326 0.442 1 3 3 2 0.000 0.702 -0.838 1 3 3 3 8.000 0.674 8.926 1 4 1 1 1.000 3.824 -1.444 1 4 1 2 0.000 1.154 -1.074 1 4 1 3 0.000 1.107 -1.052 1 4 2 1 0.000 0.869 -0.932 1 4 2 2 0.000 0.262 -0.512 1 4 2 3 0.000 0.252 -0.502 1 4 3 1 0.000 0.744 -0.863 1 4 3 2 0.000 0.225 -0.474 1 4 3 3 2.000 0.216 3.843 2 1 1 1 53.000 72.685 -2.309 2 1 1 2 11.000 21.933 -2.335 2 1 1 3 5.000 21.048 -3.498 2 1 2 1 11.000 16.523 -1.359 2 1 2 2 5.000 4.986 0.006 2 1 2 3 1.000 4.785 -1.730 2 1 3 1 3.000 14.150 -2.964 2 1 3 2 1.000 4.270 -1.582 2 1 3 3 9.000 4.098 2.422 2 2 1 1 27.000 22.280 1.000 2 2 1 2 10.000 6.723 1.264 2 2 1 3 4.000 6.452 -0.965 2 2 2 1 8.000 5.065 1.304 2 2 2 2 6.000 1.528 3.617 2 2 2 3 0.000 1.467 -1.211 2 2 3 1 1.000 4.338 -1.603 2 2 3 2 1.000 1.309 -0.270 2 2 3 3 5.000 1.256 3.341 2 3 1 1 34.000 36.525 -0.418 2 3 1 2 31.000 11.022 6.018 2 3 1 3 9.000 10.577 -0.485 2 3 2 1 15.000 8.303 2.324 2 3 2 2 16.000 2.506 8.525 2 3 2 3 4.000 2.404 1.029 2 3 3 1 6.000 7.111 -0.417 2 3 3 2 1.000 2.146 -0.782 2 3 3 3 28.000 2.059 18.078 2 4 1 1 5.000 11.688 -1.956 2 4 1 2 3.000 3.527 -0.281 2 4 1 3 4.000 3.385 0.334 2 4 2 1 1.000 2.657 -1.017 2 4 2 2 0.000 0.802 -0.895 2 4 2 3 1.000 0.769 0.263 2 4 3 1 0.000 2.275 -1.508 2 4 3 2 0.000 0.687 -0.829 2 4 3 3 5.000 0.659 5.348 3 1 1 1 330.000 152.774 14.338 3 1 1 2 52.000 46.101 0.869 3 1 1 3 14.000 44.240 -4.547 3 1 2 1 40.000 34.730 0.894 3 1 2 2 16.000 10.480 1.705 3 1 2 3 1.000 10.057 -2.856 3 1 3 1 2.000 29.742 -5.087 3 1 3 2 0.000 8.975 -2.996 3 1 3 3 12.000 8.613 1.154 3 2 1 1 50.000 46.830 0.463 3 2 1 2 16.000 14.131 0.497 3 2 1 3 3.000 13.561 -2.868 3 2 2 1 9.000 10.646 -0.504 3 2 2 2 7.000 3.212 2.113 3 2 2 3 0.000 3.083 -1.756 3 2 3 1 1.000 9.117 -2.688 3 2 3 2 1.000 2.751 -1.056 3 2 3 3 7.000 2.640 2.683 3 3 1 1 43.000 76.771 -3.854 3 3 1 2 16.000 23.166 -1.489 3 3 1 3 5.000 22.231 -3.655 3 3 2 1 8.000 17.452 -2.263 3 3 2 2 3.000 5.266 -0.988 3 3 2 3 1.000 5.054 -1.803 3 3 3 1 1.000 14.946 -3.607 3 3 3 2 1.000 4.510 -1.653 3 3 3 3 7.000 4.328 1.284 3 4 1 1 17.000 24.567 -1.527 3 4 1 2 0.000 7.413 -2.723 3 4 1 3 4.000 7.114 -1.168 3 4 2 1 1.000 5.585 -1.940 3 4 2 2 1.000 1.685 -0.528 3 4 2 3 0.000 1.617 -1.272 3 4 3 1 0.000 4.783 -2.187 3 4 3 2 0.000 1.443 -1.201 3 4 3 3 12.000 1.385 9.020 4 1 1 1 2.000 14.582 -3.295 4 1 1 2 0.000 4.400 -2.098 4 1 1 3 2.000 4.223 -1.082 4 1 2 1 1.000 3.315 -1.271 4 1 2 2 0.000 1.000 -1.000 4 1 2 3 1.000 0.960 0.041 4 1 3 1 0.000 2.839 -1.685 4 1 3 2 0.000 0.857 -0.926 4 1 3 3 0.000 0.822 -0.907 4 2 1 1 3.000 4.470 -0.695 4 2 1 2 0.000 1.349 -1.161 4 2 1 3 0.000 1.294 -1.138 4 2 2 1 0.000 1.016 -1.008 4 2 2 2 1.000 0.307 1.252 4 2 2 3 0.000 0.294 -0.542 4 2 3 1 0.000 0.870 -0.933 4 2 3 2 0.000 0.263 -0.512 4 2 3 3 2.000 0.252 3.482 4 3 1 1 3.000 7.328 -1.599 4 3 1 2 0.000 2.211 -1.487 4 3 1 3 4.000 2.122 1.289 4 3 2 1 0.000 1.666 -1.291 4 3 2 2 0.000 0.503 -0.709 4 3 2 3 0.000 0.482 -0.695 4 3 3 1 0.000 1.427 -1.194 4 3 3 2 0.000 0.430 -0.656 4 3 3 3 7.000 0.413 10.248 4 4 1 1 8.000 2.345 3.693 4 4 1 2 0.000 0.708 -0.841 4 4 1 3 3.000 0.679 2.817 4 4 2 1 0.000 0.533 -0.730 4 4 2 2 2.000 0.161 4.586 4 4 2 3 1.000 0.154 2.152 4 4 3 1 0.000 0.456 -0.676 4 4 3 2 1.000 0.138 2.323 4 4 3 3 24.000 0.132 65.646 *** LOG-LINEAR PARAMETERS *** * TABLE ABCD [or P(ABCD)] * effect beta std err z-value exp(beta) Wald df prob main 1.0147 2.7584 A 1 -0.6221 0.0809 -7.685 0.5368 2 0.4952 0.0581 8.519 1.6408 3 1.2380 0.0507 24.420 3.4488 4 -1.1111 0.3292 607.67 3 0.000 B 1 0.9245 0.0463 19.971 2.5207 2 -0.2579 0.0635 -4.059 0.7727 3 0.2364 0.0545 4.336 1.2667 4 -0.9030 0.4053 414.17 3 0.000 C 1 1.0393 0.0426 24.369 2.8271 2 -0.4421 0.0565 -7.830 0.6427 3 -0.5971 0.5504 593.88 2 0.000 D 1 0.8125 0.0402 20.193 2.2535 2 -0.3856 0.0516 -7.474 0.6800 3 -0.4268 0.6526 407.76 2 0.000 *** (CONDITIONAL) PROBABILITIES *** * P(ABCD) * 1 1 1 1 0.0202 1 1 1 2 0.0061 1 1 1 3 0.0059 1 1 2 1 0.0046 1 1 2 2 0.0014 1 1 2 3 0.0013 1 1 3 1 0.0039 1 1 3 2 0.0012 1 1 3 3 0.0011 1 2 1 1 0.0062 1 2 1 2 0.0019 1 2 1 3 0.0018 1 2 2 1 0.0014 1 2 2 2 0.0004 1 2 2 3 0.0004 1 2 3 1 0.0012 1 2 3 2 0.0004 1 2 3 3 0.0003 1 3 1 1 0.0102 1 3 1 2 0.0031 1 3 1 3 0.0029 1 3 2 1 0.0023 1 3 2 2 0.0007 1 3 2 3 0.0007 1 3 3 1 0.0020 1 3 3 2 0.0006 1 3 3 3 0.0006 1 4 1 1 0.0033 1 4 1 2 0.0010 1 4 1 3 0.0009 1 4 2 1 0.0007 1 4 2 2 0.0002 1 4 2 3 0.0002 1 4 3 1 0.0006 1 4 3 2 0.0002 1 4 3 3 0.0002 2 1 1 1 0.0618 2 1 1 2 0.0187 2 1 1 3 0.0179 2 1 2 1 0.0141 2 1 2 2 0.0042 2 1 2 3 0.0041 2 1 3 1 0.0120 2 1 3 2 0.0036 2 1 3 3 0.0035 2 2 1 1 0.0189 2 2 1 2 0.0057 2 2 1 3 0.0055 2 2 2 1 0.0043 2 2 2 2 0.0013 2 2 2 3 0.0012 2 2 3 1 0.0037 2 2 3 2 0.0011 2 2 3 3 0.0011 2 3 1 1 0.0311 2 3 1 2 0.0094 2 3 1 3 0.0090 2 3 2 1 0.0071 2 3 2 2 0.0021 2 3 2 3 0.0020 2 3 3 1 0.0060 2 3 3 2 0.0018 2 3 3 3 0.0018 2 4 1 1 0.0099 2 4 1 2 0.0030 2 4 1 3 0.0029 2 4 2 1 0.0023 2 4 2 2 0.0007 2 4 2 3 0.0007 2 4 3 1 0.0019 2 4 3 2 0.0006 2 4 3 3 0.0006 3 1 1 1 0.1299 3 1 1 2 0.0392 3 1 1 3 0.0376 3 1 2 1 0.0295 3 1 2 2 0.0089 3 1 2 3 0.0086 3 1 3 1 0.0253 3 1 3 2 0.0076 3 1 3 3 0.0073 3 2 1 1 0.0398 3 2 1 2 0.0120 3 2 1 3 0.0115 3 2 2 1 0.0091 3 2 2 2 0.0027 3 2 2 3 0.0026 3 2 3 1 0.0078 3 2 3 2 0.0023 3 2 3 3 0.0022 3 3 1 1 0.0653 3 3 1 2 0.0197 3 3 1 3 0.0189 3 3 2 1 0.0148 3 3 2 2 0.0045 3 3 2 3 0.0043 3 3 3 1 0.0127 3 3 3 2 0.0038 3 3 3 3 0.0037 3 4 1 1 0.0209 3 4 1 2 0.0063 3 4 1 3 0.0060 3 4 2 1 0.0047 3 4 2 2 0.0014 3 4 2 3 0.0014 3 4 3 1 0.0041 3 4 3 2 0.0012 3 4 3 3 0.0012 4 1 1 1 0.0124 4 1 1 2 0.0037 4 1 1 3 0.0036 4 1 2 1 0.0028 4 1 2 2 0.0009 4 1 2 3 0.0008 4 1 3 1 0.0024 4 1 3 2 0.0007 4 1 3 3 0.0007 4 2 1 1 0.0038 4 2 1 2 0.0011 4 2 1 3 0.0011 4 2 2 1 0.0009 4 2 2 2 0.0003 4 2 2 3 0.0003 4 2 3 1 0.0007 4 2 3 2 0.0002 4 2 3 3 0.0002 4 3 1 1 0.0062 4 3 1 2 0.0019 4 3 1 3 0.0018 4 3 2 1 0.0014 4 3 2 2 0.0004 4 3 2 3 0.0004 4 3 3 1 0.0012 4 3 3 2 0.0004 4 3 3 3 0.0004 4 4 1 1 0.0020 4 4 1 2 0.0006 4 4 1 3 0.0006 4 4 2 1 0.0005 4 4 2 2 0.0001 4 4 2 3 0.0001 4 4 3 1 0.0004 4 4 3 2 0.0001 4 4 3 3 0.0001