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 * * LMA2 including treatment x item interactions * * * 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 5 1 4 5 2 3 5 2 4 5 3 4 5 ] ass_res [ 0 2 3 0 2 3 0 2 3 2 2 3 2 2 3 2 2 3 ] ass_phi [ 1 1 1 1 1 1 ] nco dlk dat anes_items_ctl.txt *** STATISTICS *** Number of iterations = 224 Converge criterion = 0.0000009389 X-squared = 841.1318 (0.0000) L-squared = 418.2208 (0.0000) Cressie-Read = 528.8153 (0.0000) Dissimilarity index = 0.1843 Degrees of freedom = 256 Log-likelihood = -4920.47592 Number of parameters = 31 (+1) Sample size = 1176.0 BIC(L-squared) = -1391.6670 AIC(L-squared) = -93.7792 BIC(log-likelihood) = 10060.1179 AIC(log-likelihood) = 9902.9518 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 13.704 -1.811 1 1 1 1 2 2.000 5.301 -1.434 1 1 1 1 3 0.000 0.641 -0.801 1 1 1 2 1 1.000 3.601 -1.371 1 1 1 2 2 2.000 1.859 0.103 1 1 1 2 3 0.000 0.375 -0.612 1 1 1 3 1 0.000 0.461 -0.679 1 1 1 3 2 0.000 0.503 -0.709 1 1 1 3 3 2.000 0.382 2.616 1 1 2 1 1 6.000 4.832 0.532 1 1 2 1 2 2.000 2.558 -0.349 1 1 2 1 3 0.000 0.539 -0.734 1 1 2 2 1 3.000 1.630 1.073 1 1 2 2 2 1.000 1.151 -0.141 1 1 2 2 3 0.000 0.405 -0.636 1 1 2 3 1 1.000 0.399 0.952 1 1 2 3 2 0.000 0.596 -0.772 1 1 2 3 3 2.000 0.789 1.363 1 1 3 1 1 12.000 7.524 1.632 1 1 3 1 2 7.000 4.863 0.969 1 1 3 1 3 2.000 1.461 0.446 1 1 3 2 1 6.000 2.975 1.754 1 1 3 2 2 5.000 2.566 1.519 1 1 3 2 3 1.000 1.285 -0.252 1 1 3 3 1 1.000 1.100 -0.096 1 1 3 3 2 0.000 2.007 -1.417 1 1 3 3 3 4.000 3.787 0.110 1 1 4 1 1 1.000 0.169 2.020 1 1 4 1 2 0.000 0.116 -0.341 1 1 4 1 3 0.000 0.039 -0.198 1 1 4 2 1 0.000 0.070 -0.265 1 1 4 2 2 0.000 0.065 -0.254 1 1 4 2 3 0.000 0.036 -0.190 1 1 4 3 1 0.000 0.030 -0.172 1 1 4 3 2 0.000 0.057 -0.240 1 1 4 3 3 0.000 0.121 -0.348 1 2 1 1 1 30.000 40.164 -1.604 1 2 1 1 2 7.000 14.129 -1.897 1 2 1 1 3 0.000 1.445 -1.202 1 2 1 2 1 9.000 9.785 -0.251 1 2 1 2 2 2.000 4.593 -1.210 1 2 1 2 3 0.000 0.783 -0.885 1 2 1 3 1 1.000 1.029 -0.029 1 2 1 3 2 0.000 1.022 -1.011 1 2 1 3 3 2.000 0.656 1.659 1 2 2 1 1 15.000 13.042 0.542 1 2 2 1 2 7.000 6.278 0.288 1 2 2 1 3 1.000 1.119 -0.112 1 2 2 2 1 4.000 4.079 -0.039 1 2 2 2 2 4.000 2.620 0.852 1 2 2 2 3 0.000 0.779 -0.882 1 2 2 3 1 1.000 0.820 0.198 1 2 2 3 2 0.000 1.114 -1.056 1 2 2 3 3 1.000 1.248 -0.222 1 2 3 1 1 20.000 19.274 0.165 1 2 3 1 2 24.000 11.329 3.764 1 2 3 1 3 0.000 2.876 -1.696 1 2 3 2 1 10.000 7.066 1.104 1 2 3 2 2 15.000 5.543 4.017 1 2 3 2 3 1.000 2.346 -0.879 1 2 3 3 1 2.000 2.148 -0.101 1 2 3 3 2 1.000 3.562 -1.357 1 2 3 3 3 9.000 5.679 1.393 1 2 4 1 1 0.000 0.426 -0.653 1 2 4 1 2 0.000 0.267 -0.517 1 2 4 1 3 0.000 0.076 -0.275 1 2 4 2 1 0.000 0.164 -0.405 1 2 4 2 2 0.000 0.137 -0.370 1 2 4 2 3 0.000 0.065 -0.255 1 2 4 3 1 0.000 0.057 -0.238 1 2 4 3 2 0.000 0.100 -0.317 1 2 4 3 3 0.000 0.179 -0.423 1 3 1 1 1 184.000 149.321 2.838 1 3 1 1 2 34.000 35.687 -0.282 1 3 1 1 3 1.000 1.840 -0.619 1 3 1 2 1 26.000 26.740 -0.143 1 3 1 2 2 13.000 8.528 1.531 1 3 1 2 3 0.000 0.733 -0.856 1 3 1 3 1 0.000 1.266 -1.125 1 3 1 3 2 0.000 0.854 -0.924 1 3 1 3 3 3.000 0.276 5.181 1 3 2 1 1 29.000 34.700 -0.968 1 3 2 1 2 13.000 11.349 0.490 1 3 2 1 3 2.000 1.020 0.971 1 3 2 2 1 5.000 7.977 -1.054 1 3 2 2 2 5.000 3.481 0.814 1 3 2 2 3 0.000 0.522 -0.722 1 3 2 3 1 1.000 0.722 0.327 1 3 2 3 2 0.000 0.666 -0.816 1 3 2 3 3 2.000 0.376 2.648 1 3 3 1 1 31.000 41.442 -1.622 1 3 3 1 2 11.000 16.549 -1.364 1 3 3 1 3 0.000 2.118 -1.455 1 3 3 2 1 5.000 11.169 -1.846 1 3 3 2 2 3.000 5.952 -1.210 1 3 3 2 3 0.000 1.270 -1.127 1 3 3 3 1 0.000 1.527 -1.236 1 3 3 3 2 0.000 1.721 -1.312 1 3 3 3 3 1.000 1.383 -0.326 1 3 4 1 1 0.000 0.858 -0.926 1 3 4 1 2 0.000 0.365 -0.604 1 3 4 1 3 0.000 0.052 -0.228 1 3 4 2 1 0.000 0.243 -0.493 1 3 4 2 2 0.000 0.138 -0.371 1 3 4 2 3 0.000 0.033 -0.181 1 3 4 3 1 0.000 0.038 -0.194 1 3 4 3 2 0.000 0.045 -0.213 1 3 4 3 3 2.000 0.041 9.709 1 4 1 1 1 0.000 0.147 -0.383 1 4 1 1 2 0.000 0.065 -0.255 1 4 1 1 3 0.000 0.010 -0.100 1 4 1 2 1 0.000 0.043 -0.207 1 4 1 2 2 0.000 0.025 -0.159 1 4 1 2 3 0.000 0.006 -0.080 1 4 1 3 1 0.000 0.007 -0.085 1 4 1 3 2 0.000 0.009 -0.095 1 4 1 3 3 0.000 0.009 -0.093 1 4 2 1 1 0.000 0.058 -0.241 1 4 2 1 2 0.000 0.035 -0.187 1 4 2 1 3 0.000 0.009 -0.097 1 4 2 2 1 0.000 0.022 -0.148 1 4 2 2 2 0.000 0.018 -0.133 1 4 2 2 3 0.000 0.008 -0.089 1 4 2 3 1 0.000 0.007 -0.084 1 4 2 3 2 0.000 0.012 -0.110 1 4 2 3 3 0.000 0.020 -0.142 1 4 3 1 1 0.000 0.097 -0.312 1 4 3 1 2 0.000 0.072 -0.268 1 4 3 1 3 0.000 0.027 -0.165 1 4 3 2 1 0.000 0.043 -0.207 1 4 3 2 2 0.000 0.042 -0.205 1 4 3 2 3 0.000 0.027 -0.164 1 4 3 3 1 0.000 0.021 -0.144 1 4 3 3 2 0.000 0.044 -0.209 1 4 3 3 3 0.000 0.104 -0.323 1 4 4 1 1 0.000 0.002 -0.047 1 4 4 1 2 0.000 0.002 -0.042 1 4 4 1 3 0.000 0.001 -0.027 1 4 4 2 1 0.000 0.001 -0.032 1 4 4 2 2 0.000 0.001 -0.033 1 4 4 2 3 0.000 0.001 -0.028 1 4 4 3 1 0.000 0.001 -0.024 1 4 4 3 2 0.000 0.001 -0.036 1 4 4 3 3 1.000 0.003 17.076 2 1 1 1 1 4.000 5.507 -0.642 2 1 1 1 2 0.000 1.102 -1.050 2 1 1 1 3 3.000 1.203 1.638 2 1 1 2 1 0.000 0.842 -0.918 2 1 1 2 2 0.000 0.225 -0.474 2 1 1 2 3 1.000 0.409 0.924 2 1 1 3 1 0.000 0.271 -0.520 2 1 1 3 2 0.000 0.153 -0.391 2 1 1 3 3 3.000 1.048 1.906 2 1 2 1 1 3.000 1.485 1.243 2 1 2 1 2 0.000 0.407 -0.638 2 1 2 1 3 0.000 0.774 -0.880 2 1 2 2 1 1.000 0.291 1.313 2 1 2 2 2 0.000 0.107 -0.326 2 1 2 2 3 0.000 0.338 -0.581 2 1 2 3 1 0.000 0.179 -0.423 2 1 2 3 2 0.000 0.138 -0.372 2 1 2 3 3 2.000 1.655 0.268 2 1 3 1 1 5.000 1.789 2.401 2 1 3 1 2 0.000 0.598 -0.774 2 1 3 1 3 2.000 1.621 0.298 2 1 3 2 1 3.000 0.412 4.035 2 1 3 2 2 2.000 0.184 4.238 2 1 3 2 3 1.000 0.830 0.187 2 1 3 3 1 2.000 0.382 2.616 2 1 3 3 2 0.000 0.361 -0.601 2 1 3 3 3 4.000 6.141 -0.864 2 1 4 1 1 0.000 1.089 -1.044 2 1 4 1 2 0.000 0.388 -0.623 2 1 4 1 3 0.000 1.174 -1.083 2 1 4 2 1 0.000 0.263 -0.513 2 1 4 2 2 0.000 0.125 -0.354 2 1 4 2 3 0.000 0.632 -0.795 2 1 4 3 1 0.000 0.278 -0.528 2 1 4 3 2 0.000 0.280 -0.529 2 1 4 3 3 2.000 5.319 -1.439 2 2 1 1 1 23.000 32.232 -1.626 2 2 1 1 2 4.000 5.867 -0.771 2 2 1 1 3 5.000 5.411 -0.177 2 2 1 2 1 2.000 4.569 -1.202 2 2 1 2 2 3.000 1.110 1.794 2 2 1 2 3 1.000 1.706 -0.541 2 2 1 3 1 2.000 1.207 0.722 2 2 1 3 2 1.000 0.620 0.482 2 2 1 3 3 7.000 3.592 1.798 2 2 2 1 1 12.000 8.004 1.412 2 2 2 1 2 3.000 1.994 0.713 2 2 2 1 3 3.000 3.205 -0.115 2 2 2 2 1 4.000 1.456 2.108 2 2 2 2 2 2.000 0.484 2.179 2 2 2 2 3 0.000 1.298 -1.139 2 2 2 3 1 0.000 0.736 -0.858 2 2 2 3 2 1.000 0.517 0.671 2 2 2 3 3 4.000 5.223 -0.535 2 2 3 1 1 14.000 9.151 1.603 2 2 3 1 2 7.000 2.783 2.528 2 2 3 1 3 9.000 6.373 1.041 2 2 3 2 1 5.000 1.952 2.182 2 2 3 2 2 1.000 0.792 0.233 2 2 3 2 3 3.000 3.024 -0.014 2 2 3 3 1 4.000 1.490 2.056 2 2 3 3 2 0.000 1.279 -1.131 2 2 3 3 3 19.000 18.392 0.142 2 2 4 1 1 5.000 5.481 -0.206 2 2 4 1 2 3.000 1.775 0.920 2 2 4 1 3 4.000 4.540 -0.253 2 2 4 2 1 1.000 1.229 -0.206 2 2 4 2 2 0.000 0.531 -0.729 2 2 4 2 3 1.000 2.264 -0.840 2 2 4 3 1 0.000 1.067 -1.033 2 2 4 3 2 0.000 0.975 -0.988 2 2 4 3 3 5.000 15.671 -2.696 2 3 1 1 1 146.000 142.396 0.302 2 3 1 1 2 18.000 17.610 0.093 2 3 1 1 3 13.000 8.189 1.681 2 3 1 2 1 14.000 14.836 -0.217 2 3 1 2 2 3.000 2.449 0.352 2 3 1 2 3 1.000 1.898 -0.652 2 3 1 3 1 2.000 1.764 0.178 2 3 1 3 2 0.000 0.615 -0.785 2 3 1 3 3 9.000 1.798 5.371 2 3 2 1 1 21.000 25.306 -0.856 2 3 2 1 2 3.000 4.283 -0.620 2 3 2 1 3 1.000 3.471 -1.326 2 3 2 2 1 4.000 3.385 0.334 2 3 2 2 2 2.000 0.764 1.413 2 3 2 2 3 0.000 1.033 -1.016 2 3 2 3 1 0.000 0.769 -0.877 2 3 2 3 2 1.000 0.367 1.044 2 3 2 3 3 5.000 1.871 2.288 2 3 3 1 1 12.000 23.380 -2.354 2 3 3 1 2 5.000 4.831 0.077 2 3 3 1 3 5.000 5.578 -0.245 2 3 3 2 1 3.000 3.666 -0.348 2 3 3 2 2 0.000 1.011 -1.005 2 3 3 2 3 1.000 1.946 -0.678 2 3 3 3 1 1.000 1.259 -0.231 2 3 3 3 2 1.000 0.734 0.310 2 3 3 3 3 6.000 5.324 0.293 2 3 4 1 1 17.000 13.101 1.077 2 3 4 1 2 0.000 2.882 -1.698 2 3 4 1 3 4.000 3.717 0.147 2 3 4 2 1 1.000 2.159 -0.789 2 3 4 2 2 1.000 0.634 0.460 2 3 4 2 3 0.000 1.363 -1.167 2 3 4 3 1 0.000 0.844 -0.919 2 3 4 3 2 0.000 0.524 -0.724 2 3 4 3 3 10.000 4.243 2.795 2 4 1 1 1 2.000 5.134 -1.383 2 4 1 1 2 0.000 1.175 -1.084 2 4 1 1 3 2.000 1.624 0.295 2 4 1 2 1 1.000 0.873 0.136 2 4 1 2 2 0.000 0.267 -0.516 2 4 1 2 3 1.000 0.615 0.492 2 4 1 3 1 0.000 0.370 -0.608 2 4 1 3 2 0.000 0.239 -0.489 2 4 1 3 3 0.000 2.075 -1.441 2 4 2 1 1 3.000 1.554 1.160 2 4 2 1 2 0.000 0.487 -0.698 2 4 2 1 3 0.000 1.173 -1.083 2 4 2 2 1 0.000 0.339 -0.582 2 4 2 2 2 1.000 0.142 2.280 2 4 2 2 3 0.000 0.570 -0.755 2 4 2 3 1 0.000 0.275 -0.524 2 4 2 3 2 0.000 0.243 -0.493 2 4 2 3 3 2.000 3.678 -0.875 2 4 3 1 1 3.000 2.015 0.694 2 4 3 1 2 0.000 0.770 -0.878 2 4 3 1 3 4.000 2.645 0.833 2 4 3 2 1 0.000 0.516 -0.718 2 4 3 2 2 0.000 0.263 -0.513 2 4 3 2 3 0.000 1.506 -1.227 2 4 3 3 1 0.000 0.631 -0.795 2 4 3 3 2 0.000 0.681 -0.825 2 4 3 3 3 7.000 14.690 -2.006 2 4 4 1 1 8.000 1.256 6.019 2 4 4 1 2 0.000 0.511 -0.715 2 4 4 1 3 3.000 1.960 0.743 2 4 4 2 1 0.000 0.338 -0.581 2 4 4 2 2 2.000 0.183 4.241 2 4 4 2 3 1.000 1.173 -0.160 2 4 4 3 1 0.000 0.471 -0.686 2 4 4 3 2 1.000 0.540 0.625 2 4 4 3 3 23.000 13.020 2.766 *** LOG-LINEAR PARAMETERS *** * TABLE TABCD [or P(TABCD)] * effect beta exp(beta) main -0.3657 0.6937 T 1 -0.6645 0.5145 2 0.6645 1.9435 A 1 -0.1031 0.9021 2 0.9907 2.6931 3 1.0586 2.8823 4 -1.9462 0.1428 B 1 0.5000 1.6487 2 0.0044 1.0045 3 0.7526 2.1224 4 -1.2570 0.2845 C 1 0.7549 2.1274 2 -0.2986 0.7419 3 -0.4563 0.6336 D 1 0.3329 1.3950 2 -0.2643 0.7677 3 -0.0686 0.9337 TA 1 1 0.7528 2.1229 1 2 0.4070 1.5023 1 3 0.3207 1.3781 1 4 -1.4805 0.2275 2 1 -0.7528 0.4710 2 2 -0.4070 0.6656 2 3 -0.3207 0.7256 2 4 1.4805 4.3953 TB 1 1 0.2476 1.2809 1 2 0.3817 1.4647 1 3 0.5100 1.6653 1 4 -1.1393 0.3201 2 1 -0.2476 0.7807 2 2 -0.3817 0.6827 2 3 -0.5100 0.6005 2 4 1.1393 3.1245 TC 1 1 -0.0270 0.9733 1 2 0.2438 1.2760 1 3 -0.2167 0.8051 2 1 0.0270 1.0274 2 2 -0.2438 0.7837 2 3 0.2167 1.2420 TD 1 1 0.1470 1.1583 1 2 0.4764 1.6102 1 3 -0.6233 0.5361 2 1 -0.1470 0.8633 2 2 -0.4764 0.6210 2 3 0.6233 1.8652 type 2 association (row=A column=B slab=-) association 1.0000 row 0.2418 0.0343 -0.8097 0.5336 adj row 0.2418 0.0343 -0.8097 0.5336 column -0.7658 -0.0809 0.3551 0.4917 adj column -0.7658 -0.0809 0.3551 0.4917 slab 0.5788 adj slab 0.5788 type 2 association (row=A column=C slab=-) association 1.0000 row 0.2418 0.0343 -0.8097 0.5336 adj row 0.2418 0.0343 -0.8097 0.5336 column -0.9650 -0.3350 1.3000 adj column -0.9650 -0.3350 1.3000 slab 0.5788 adj slab 0.5788 type 2 association (row=A column=D slab=-) association 1.0000 row 0.2418 0.0343 -0.8097 0.5336 adj row 0.2418 0.0343 -0.8097 0.5336 column -0.9947 -0.2035 1.1982 adj column -0.9947 -0.2035 1.1982 slab 0.5788 adj slab 0.5788 type 2 association (row=B column=C slab=-) association 1.0000 row -0.7658 -0.0809 0.3551 0.4917 adj row -0.7658 -0.0809 0.3551 0.4917 column -0.9650 -0.3350 1.3000 adj column -0.9650 -0.3350 1.3000 slab 0.5788 adj slab 0.5788 type 2 association (row=B column=D slab=-) association 1.0000 row -0.7658 -0.0809 0.3551 0.4917 adj row -0.7658 -0.0809 0.3551 0.4917 column -0.9947 -0.2035 1.1982 adj column -0.9947 -0.2035 1.1982 slab 0.5788 adj slab 0.5788 type 2 association (row=C column=D slab=-) association 1.0000 row -0.9650 -0.3350 1.3000 adj row -0.9650 -0.3350 1.3000 column -0.9947 -0.2035 1.1982 adj column -0.9947 -0.2035 1.1982 slab 0.5788 adj slab 0.5788