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: treatement x item1 x item2 x item3 x item4 * T A B C D * * Hybrid 2 dimensional w/ TA TB TC TD * * man 5 dim 2 4 4 3 3 lab T A B C D mod {T A B C D TA TB TC TD ass2(A,B,-,7a) ass2(A,C,-,7a) ass2(A,D,-,7a) ass2(B,C,-,7a) ass2(B,D,-,7a) ass2(C,D,-,7a) } ass_equ [ 1 2 5 1 3 6 1 4 6 2 3 6 2 4 6 3 4 7 ] ass_res [ 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 ] ass_phi [ 1 1 1 1 1 1 ] nco dlk dat anes_items_ctl.txt *** STATISTICS *** Number of iterations = 108 Converge criterion = 0.0000009297 X-squared = 598.0012 (0.0000) L-squared = 313.1186 (0.0095) Cressie-Read = 386.8485 (0.0000) Dissimilarity index = 0.1491 Degrees of freedom = 257 Log-likelihood = -4867.92482 Number of parameters = 30 (+1) Sample size = 1176.0 BIC(L-squared) = -1503.8391 AIC(L-squared) = -200.8814 BIC(log-likelihood) = 9947.9459 AIC(log-likelihood) = 9795.8496 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** T A B C D observed estimated std. res. 1 1 1 1 1 7.000 9.451 -0.797 1 1 1 1 2 2.000 3.362 -0.743 1 1 1 1 3 0.000 0.308 -0.555 1 1 1 2 1 1.000 2.316 -0.865 1 1 1 2 2 2.000 1.142 0.803 1 1 1 2 3 0.000 0.195 -0.442 1 1 1 3 1 0.000 0.195 -0.441 1 1 1 3 2 0.000 0.298 -0.546 1 1 1 3 3 2.000 0.448 2.320 1 1 2 1 1 6.000 6.320 -0.127 1 1 2 1 2 2.000 2.709 -0.431 1 1 2 1 3 0.000 0.354 -0.595 1 1 2 2 1 3.000 1.742 0.953 1 1 2 2 2 1.000 1.035 -0.035 1 1 2 2 3 0.000 0.253 -0.503 1 1 2 3 1 1.000 0.220 1.660 1 1 2 3 2 0.000 0.407 -0.638 1 1 2 3 3 2.000 0.874 1.205 1 1 3 1 1 12.000 13.839 -0.494 1 1 3 1 2 7.000 6.714 0.110 1 1 3 1 3 2.000 1.113 0.840 1 1 3 2 1 6.000 4.125 0.923 1 1 3 2 2 5.000 2.775 1.336 1 1 3 2 3 1.000 0.861 0.150 1 1 3 3 1 1.000 0.685 0.381 1 1 3 3 2 0.000 1.430 -1.196 1 1 3 3 3 4.000 3.893 0.054 1 1 4 1 1 1.000 0.343 1.120 1 1 4 1 2 0.000 0.174 -0.417 1 1 4 1 3 0.000 0.031 -0.176 1 1 4 2 1 0.000 0.105 -0.324 1 1 4 2 2 0.000 0.074 -0.271 1 1 4 2 3 0.000 0.025 -0.157 1 1 4 3 1 0.000 0.019 -0.138 1 1 4 3 2 0.000 0.041 -0.204 1 1 4 3 3 0.000 0.122 -0.349 1 2 1 1 1 30.000 31.013 -0.182 1 2 1 1 2 7.000 10.503 -1.081 1 2 1 1 3 0.000 0.875 -0.935 1 2 1 2 1 9.000 7.367 0.602 1 2 1 2 2 2.000 3.459 -0.784 1 2 1 2 3 0.000 0.539 -0.734 1 2 1 3 1 1.000 0.556 0.595 1 2 1 3 2 0.000 0.811 -0.901 1 2 1 3 3 2.000 1.108 0.847 1 2 2 1 1 15.000 16.383 -0.342 1 2 2 1 2 7.000 6.686 0.121 1 2 2 1 3 1.000 0.796 0.229 1 2 2 2 1 4.000 4.378 -0.181 1 2 2 2 2 4.000 2.477 0.967 1 2 2 2 3 0.000 0.552 -0.743 1 2 2 3 1 1.000 0.498 0.712 1 2 2 3 2 0.000 0.874 -0.935 1 2 2 3 3 1.000 1.708 -0.542 1 2 3 1 1 20.000 30.676 -1.928 1 2 3 1 2 24.000 14.169 2.612 1 2 3 1 3 0.000 2.139 -1.462 1 2 3 2 1 10.000 8.865 0.381 1 2 3 2 2 15.000 5.677 3.913 1 2 3 2 3 1.000 1.603 -0.476 1 2 3 3 1 2.000 1.321 0.590 1 2 3 3 2 1.000 2.628 -1.004 1 2 3 3 3 9.000 6.509 0.976 1 2 4 1 1 0.000 0.723 -0.851 1 2 4 1 2 0.000 0.348 -0.590 1 2 4 1 3 0.000 0.057 -0.238 1 2 4 2 1 0.000 0.214 -0.463 1 2 4 2 2 0.000 0.143 -0.378 1 2 4 2 3 0.000 0.044 -0.209 1 2 4 3 1 0.000 0.035 -0.187 1 2 4 3 2 0.000 0.072 -0.269 1 2 4 3 3 0.000 0.194 -0.440 1 3 1 1 1 184.000 154.993 2.330 1 3 1 1 2 34.000 43.002 -1.373 1 3 1 1 3 1.000 2.444 -0.924 1 3 1 2 1 26.000 32.457 -1.133 1 3 1 2 2 13.000 12.485 0.146 1 3 1 2 3 0.000 1.327 -1.152 1 3 1 3 1 0.000 1.583 -1.258 1 3 1 3 2 0.000 1.891 -1.375 1 3 1 3 3 3.000 1.763 0.931 1 3 2 1 1 29.000 31.453 -0.437 1 3 2 1 2 13.000 10.516 0.766 1 3 2 1 3 2.000 0.854 1.239 1 3 2 2 1 5.000 7.410 -0.885 1 3 2 2 2 5.000 3.435 0.844 1 3 2 2 3 0.000 0.522 -0.723 1 3 2 3 1 1.000 0.544 0.618 1 3 2 3 2 0.000 0.783 -0.885 1 3 2 3 3 2.000 1.044 0.936 1 3 3 1 1 31.000 31.207 -0.037 1 3 3 1 2 11.000 11.809 -0.235 1 3 3 1 3 0.000 1.216 -1.103 1 3 3 2 1 5.000 7.951 -1.047 1 3 3 2 2 3.000 4.171 -0.573 1 3 3 2 3 0.000 0.804 -0.896 1 3 3 3 1 0.000 0.766 -0.875 1 3 3 3 2 0.000 1.247 -1.117 1 3 3 3 3 1.000 2.108 -0.763 1 3 4 1 1 0.000 0.598 -0.773 1 3 4 1 2 0.000 0.236 -0.485 1 3 4 1 3 0.000 0.026 -0.162 1 3 4 2 1 0.000 0.156 -0.395 1 3 4 2 2 0.000 0.085 -0.292 1 3 4 2 3 0.000 0.018 -0.133 1 3 4 3 1 0.000 0.016 -0.128 1 3 4 3 2 0.000 0.028 -0.167 1 3 4 3 3 2.000 0.051 8.634 1 4 1 1 1 0.000 0.052 -0.229 1 4 1 1 2 0.000 0.021 -0.146 1 4 1 1 3 0.000 0.003 -0.050 1 4 1 2 1 0.000 0.014 -0.118 1 4 1 2 2 0.000 0.008 -0.089 1 4 1 2 3 0.000 0.002 -0.042 1 4 1 3 1 0.000 0.002 -0.040 1 4 1 3 2 0.000 0.003 -0.052 1 4 1 3 3 0.000 0.005 -0.073 1 4 2 1 1 0.000 0.066 -0.257 1 4 2 1 2 0.000 0.032 -0.180 1 4 2 1 3 0.000 0.005 -0.074 1 4 2 2 1 0.000 0.020 -0.141 1 4 2 2 2 0.000 0.013 -0.116 1 4 2 2 3 0.000 0.004 -0.065 1 4 2 3 1 0.000 0.003 -0.058 1 4 2 3 2 0.000 0.007 -0.084 1 4 2 3 3 0.000 0.019 -0.139 1 4 3 1 1 0.000 0.219 -0.468 1 4 3 1 2 0.000 0.121 -0.348 1 4 3 1 3 0.000 0.026 -0.161 1 4 3 2 1 0.000 0.071 -0.267 1 4 3 2 2 0.000 0.055 -0.233 1 4 3 2 3 0.000 0.022 -0.147 1 4 3 3 1 0.000 0.016 -0.125 1 4 3 3 2 0.000 0.037 -0.194 1 4 3 3 3 0.000 0.131 -0.362 1 4 4 1 1 0.000 0.006 -0.079 1 4 4 1 2 0.000 0.004 -0.060 1 4 4 1 3 0.000 0.001 -0.029 1 4 4 2 1 0.000 0.002 -0.046 1 4 4 2 2 0.000 0.002 -0.041 1 4 4 2 3 0.000 0.001 -0.027 1 4 4 3 1 0.000 0.001 -0.022 1 4 4 3 2 0.000 0.001 -0.035 1 4 4 3 3 1.000 0.005 14.505 2 1 1 1 1 4.000 4.200 -0.098 2 1 1 1 2 0.000 0.776 -0.881 2 1 1 1 3 3.000 0.681 2.809 2 1 1 2 1 0.000 0.599 -0.774 2 1 1 2 2 0.000 0.153 -0.392 2 1 1 2 3 1.000 0.252 1.490 2 1 1 3 1 0.000 0.091 -0.301 2 1 1 3 2 0.000 0.072 -0.269 2 1 1 3 3 3.000 1.042 1.919 2 1 2 1 1 3.000 2.199 0.541 2 1 2 1 2 0.000 0.489 -0.700 2 1 2 1 3 0.000 0.614 -0.784 2 1 2 2 1 1.000 0.353 1.089 2 1 2 2 2 0.000 0.109 -0.330 2 1 2 2 3 0.000 0.256 -0.506 2 1 2 3 1 0.000 0.081 -0.284 2 1 2 3 2 0.000 0.077 -0.278 2 1 2 3 3 2.000 1.591 0.325 2 1 3 1 1 5.000 3.793 0.620 2 1 3 1 2 0.000 0.956 -0.978 2 1 3 1 3 2.000 1.521 0.388 2 1 3 2 1 3.000 0.658 2.886 2 1 3 2 2 2.000 0.230 3.691 2 1 3 2 3 1.000 0.685 0.381 2 1 3 3 1 2.000 0.197 4.060 2 1 3 3 2 0.000 0.214 -0.462 2 1 3 3 3 4.000 5.585 -0.671 2 1 4 1 1 0.000 2.539 -1.593 2 1 4 1 2 0.000 0.666 -0.816 2 1 4 1 3 0.000 1.145 -1.070 2 1 4 2 1 0.000 0.452 -0.672 2 1 4 2 2 0.000 0.164 -0.405 2 1 4 2 3 0.000 0.529 -0.727 2 1 4 3 1 0.000 0.148 -0.385 2 1 4 3 2 0.000 0.167 -0.409 2 1 4 3 3 2.000 4.716 -1.251 2 2 1 1 1 23.000 26.136 -0.613 2 2 1 1 2 4.000 4.596 -0.278 2 2 1 1 3 5.000 3.674 0.692 2 2 1 2 1 2.000 3.615 -0.849 2 2 1 2 2 3.000 0.881 2.257 2 2 1 2 3 1.000 1.318 -0.277 2 2 1 3 1 2.000 0.492 2.149 2 2 1 3 2 1.000 0.373 1.027 2 2 1 3 3 7.000 4.889 0.955 2 2 2 1 1 12.000 10.808 0.363 2 2 2 1 2 3.000 2.290 0.469 2 2 2 1 3 3.000 2.617 0.237 2 2 2 2 1 4.000 1.682 1.788 2 2 2 2 2 2.000 0.494 2.142 2 2 2 2 3 0.000 1.056 -1.028 2 2 2 3 1 0.000 0.345 -0.587 2 2 2 3 2 1.000 0.315 1.222 2 2 2 3 3 4.000 5.898 -0.782 2 2 3 1 1 14.000 15.945 -0.487 2 2 3 1 2 7.000 3.824 1.624 2 2 3 1 3 9.000 5.540 1.470 2 2 3 2 1 5.000 2.683 1.414 2 2 3 2 2 1.000 0.892 0.114 2 2 3 2 3 3.000 2.418 0.375 2 2 3 3 1 4.000 0.721 3.860 2 2 3 3 2 0.000 0.745 -0.863 2 2 3 3 3 19.000 17.710 0.307 2 2 4 1 1 5.000 10.140 -1.614 2 2 4 1 2 3.000 2.532 0.294 2 2 4 1 3 4.000 3.965 0.018 2 2 4 2 1 1.000 1.751 -0.567 2 2 4 2 2 0.000 0.606 -0.779 2 2 4 2 3 1.000 1.775 -0.582 2 2 4 3 1 0.000 0.514 -0.717 2 2 4 3 2 0.000 0.553 -0.744 2 2 4 3 3 5.000 14.207 -2.443 2 3 1 1 1 146.000 144.673 0.110 2 3 1 1 2 18.000 20.843 -0.623 2 3 1 1 3 13.000 11.369 0.484 2 3 1 2 1 14.000 17.641 -0.867 2 3 1 2 2 3.000 3.524 -0.279 2 3 1 2 3 1.000 3.595 -1.369 2 3 1 3 1 2.000 1.552 0.360 2 3 1 3 2 0.000 0.963 -0.981 2 3 1 3 3 9.000 8.616 0.131 2 3 2 1 1 21.000 22.981 -0.413 2 3 2 1 2 3.000 3.990 -0.495 2 3 2 1 3 1.000 3.111 -1.197 2 3 2 2 1 4.000 3.153 0.477 2 3 2 2 2 2.000 0.759 1.425 2 3 2 2 3 0.000 1.107 -1.052 2 3 2 3 1 0.000 0.417 -0.646 2 3 2 3 2 1.000 0.312 1.232 2 3 2 3 3 5.000 3.993 0.504 2 3 3 1 1 12.000 17.966 -1.407 2 3 3 1 2 5.000 3.530 0.782 2 3 3 1 3 5.000 3.490 0.808 2 3 3 2 1 3.000 2.665 0.205 2 3 3 2 2 0.000 0.726 -0.852 2 3 3 2 3 1.000 1.343 -0.296 2 3 3 3 1 1.000 0.463 0.789 2 3 3 3 2 1.000 0.392 0.972 2 3 3 3 3 6.000 6.353 -0.140 2 3 4 1 1 17.000 9.285 2.532 2 3 4 1 2 0.000 1.900 -1.378 2 3 4 1 3 4.000 2.029 1.383 2 3 4 2 1 1.000 1.413 -0.348 2 3 4 2 2 1.000 0.401 0.946 2 3 4 2 3 0.000 0.801 -0.895 2 3 4 3 1 0.000 0.268 -0.518 2 3 4 3 2 0.000 0.236 -0.486 2 3 4 3 3 10.000 4.142 2.879 2 4 1 1 1 2.000 1.881 0.087 2 4 1 1 2 0.000 0.396 -0.629 2 4 1 1 3 2.000 0.447 2.321 2 4 1 2 1 1.000 0.292 1.312 2 4 1 2 2 0.000 0.085 -0.292 2 4 1 2 3 1.000 0.180 1.934 2 4 1 3 1 0.000 0.059 -0.243 2 4 1 3 2 0.000 0.053 -0.231 2 4 1 3 3 0.000 0.991 -0.996 2 4 2 1 1 3.000 1.849 0.846 2 4 2 1 2 0.000 0.469 -0.685 2 4 2 1 3 0.000 0.758 -0.871 2 4 2 2 1 0.000 0.323 -0.568 2 4 2 2 2 1.000 0.113 2.631 2 4 2 2 3 0.000 0.343 -0.586 2 4 2 3 1 0.000 0.098 -0.313 2 4 2 3 2 0.000 0.107 -0.328 2 4 2 3 3 2.000 2.844 -0.500 2 4 3 1 1 3.000 4.847 -0.839 2 4 3 1 2 0.000 1.393 -1.180 2 4 3 1 3 4.000 2.851 0.680 2 4 3 2 1 0.000 0.914 -0.956 2 4 3 2 2 0.000 0.364 -0.603 2 4 3 2 3 0.000 1.395 -1.181 2 4 3 3 1 0.000 0.365 -0.604 2 4 3 3 2 0.000 0.452 -0.672 2 4 3 3 3 7.000 15.174 -2.098 2 4 4 1 1 8.000 3.720 2.219 2 4 4 1 2 0.000 1.113 -1.055 2 4 4 1 3 3.000 2.462 0.343 2 4 4 2 1 0.000 0.720 -0.848 2 4 4 2 2 2.000 0.299 3.114 2 4 4 2 3 1.000 1.235 -0.212 2 4 4 3 1 0.000 0.314 -0.560 2 4 4 3 2 1.000 0.405 0.936 2 4 4 3 3 23.000 14.688 2.169 *** LOG-LINEAR PARAMETERS *** * TABLE TABCD [or P(TABCD)] * effect beta exp(beta) main -0.5166 0.5966 T 1 -0.6529 0.5205 2 0.6529 1.9211 A 1 -0.0908 0.9132 2 1.0289 2.7980 3 1.1120 3.0405 4 -2.0501 0.1287 B 1 0.2795 1.3224 2 0.0135 1.0136 3 0.8499 2.3393 4 -1.1428 0.3189 C 1 0.8712 2.3897 2 -0.2715 0.7623 3 -0.5997 0.5490 D 1 0.3279 1.3881 2 -0.2675 0.7653 3 -0.0604 0.9414 TA 1 1 0.7215 2.0575 1 2 0.4015 1.4941 1 3 0.3504 1.4196 1 4 -1.4734 0.2291 2 1 -0.7215 0.4860 2 2 -0.4015 0.6693 2 3 -0.3504 0.7044 2 4 1.4734 4.3640 TB 1 1 0.2604 1.2975 1 2 0.3829 1.4665 1 3 0.5020 1.6521 1 4 -1.1453 0.3181 2 1 -0.2604 0.7707 2 2 -0.3829 0.6819 2 3 -0.5020 0.6053 2 4 1.1453 3.1434 TC 1 1 -0.0819 0.9213 1 2 0.1885 1.2074 1 3 -0.1065 0.8990 2 1 0.0819 1.0854 2 2 -0.1885 0.8282 2 3 0.1065 1.1124 TD 1 1 0.1585 1.1717 1 2 0.4861 1.6260 1 3 -0.6446 0.5249 2 1 -0.1585 0.8534 2 2 -0.4861 0.6150 2 3 0.6446 1.9052 type 2 association (row=A column=B slab=-) association 1.0000 row 0.1523 -0.0277 -0.7581 0.6335 adj row 0.1523 -0.0277 -0.7581 0.6335 column -0.7763 -0.0930 0.3605 0.5087 adj column -0.7763 -0.0930 0.3605 0.5087 slab 1.9171 adj slab 1.9171 type 2 association (row=A column=C slab=-) association 1.0000 row 0.1523 -0.0277 -0.7581 0.6335 adj row 0.1523 -0.0277 -0.7581 0.6335 column -0.5496 -0.2481 0.7977 adj column -0.5496 -0.2481 0.7977 slab 0.5721 adj slab 0.5721 type 2 association (row=A column=D slab=-) association 1.0000 row 0.1523 -0.0277 -0.7581 0.6335 adj row 0.1523 -0.0277 -0.7581 0.6335 column -0.6229 -0.1458 0.7686 adj column -0.6229 -0.1458 0.7686 slab 0.5721 adj slab 0.5721 type 2 association (row=B column=C slab=-) association 1.0000 row -0.7763 -0.0930 0.3605 0.5087 adj row -0.7763 -0.0930 0.3605 0.5087 column -0.5496 -0.2481 0.7977 adj column -0.5496 -0.2481 0.7977 slab 0.5721 adj slab 0.5721 type 2 association (row=B column=D slab=-) association 1.0000 row -0.7763 -0.0930 0.3605 0.5087 adj row -0.7763 -0.0930 0.3605 0.5087 column -0.6229 -0.1458 0.7686 adj column -0.6229 -0.1458 0.7686 slab 0.5721 adj slab 0.5721 type 2 association (row=C column=D slab=-) association 1.0000 row -0.5496 -0.2481 0.7977 adj row -0.5496 -0.2481 0.7977 column -0.6229 -0.1458 0.7686 adj column -0.6229 -0.1458 0.7686 slab 2.2710 adj slab 2.2710