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,3) ass2(A,C,-,7a) ass2(A,D,-,7a) ass2(B,C,-,7a) ass2(B,D,-,7a) ass2(C,D,-,7a) } des[ 1 1 2 3 ] 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 = 0.0000009105 X-squared = 590.1765 (0.0000) L-squared = 316.7933 (0.0073) Cressie-Read = 387.4933 (0.0000) Dissimilarity index = 0.1501 Degrees of freedom = 258 Log-likelihood = -4869.76221 Number of parameters = 29 (+1) Sample size = 1176.0 BIC(L-squared) = -1507.2342 AIC(L-squared) = -199.2067 BIC(log-likelihood) = 9944.5508 AIC(log-likelihood) = 9797.5244 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 11.825 -1.403 1 1 1 1 2 2.000 4.054 -1.020 1 1 1 1 3 0.000 0.350 -0.592 1 1 1 2 1 1.000 2.831 -1.088 1 1 1 2 2 2.000 1.348 0.561 1 1 1 2 3 0.000 0.217 -0.465 1 1 1 3 1 0.000 0.220 -0.469 1 1 1 3 2 0.000 0.330 -0.575 1 1 1 3 3 2.000 0.464 2.256 1 1 2 1 1 6.000 6.602 -0.234 1 1 2 1 2 2.000 2.727 -0.440 1 1 2 1 3 0.000 0.335 -0.579 1 1 2 2 1 3.000 1.775 0.919 1 1 2 2 2 1.000 1.019 -0.019 1 1 2 2 3 0.000 0.233 -0.482 1 1 2 3 1 1.000 0.207 1.745 1 1 2 3 2 0.000 0.374 -0.612 1 1 2 3 3 2.000 0.747 1.450 1 1 3 1 1 12.000 12.932 -0.259 1 1 3 1 2 7.000 6.051 0.386 1 1 3 1 3 2.000 0.940 1.094 1 1 3 2 1 6.000 3.757 1.157 1 1 3 2 2 5.000 2.442 1.637 1 1 3 2 3 1.000 0.706 0.351 1 1 3 3 1 1.000 0.573 0.564 1 1 3 3 2 0.000 1.175 -1.084 1 1 3 3 3 4.000 2.968 0.599 1 1 4 1 1 1.000 0.310 1.238 1 1 4 1 2 0.000 0.151 -0.389 1 1 4 1 3 0.000 0.025 -0.159 1 1 4 2 1 0.000 0.092 -0.304 1 1 4 2 2 0.000 0.063 -0.250 1 1 4 2 3 0.000 0.020 -0.140 1 1 4 3 1 0.000 0.015 -0.124 1 1 4 3 2 0.000 0.033 -0.182 1 1 4 3 3 0.000 0.090 -0.300 1 2 1 1 1 30.000 28.866 0.211 1 2 1 1 2 7.000 9.896 -0.921 1 2 1 1 3 0.000 0.854 -0.924 1 2 1 2 1 9.000 6.912 0.794 1 2 1 2 2 2.000 3.292 -0.712 1 2 1 2 3 0.000 0.529 -0.727 1 2 1 3 1 1.000 0.537 0.632 1 2 1 3 2 0.000 0.806 -0.898 1 2 1 3 3 2.000 1.132 0.816 1 2 2 1 1 15.000 16.116 -0.278 1 2 2 1 2 7.000 6.658 0.133 1 2 2 1 3 1.000 0.817 0.202 1 2 2 2 1 4.000 4.333 -0.160 1 2 2 2 2 4.000 2.487 0.959 1 2 2 2 3 0.000 0.568 -0.754 1 2 2 3 1 1.000 0.505 0.698 1 2 2 3 2 0.000 0.913 -0.955 1 2 2 3 3 1.000 1.824 -0.610 1 2 3 1 1 20.000 31.570 -2.059 1 2 3 1 2 24.000 14.771 2.401 1 2 3 1 3 0.000 2.294 -1.514 1 2 3 2 1 10.000 9.172 0.273 1 2 3 2 2 15.000 5.961 3.702 1 2 3 2 3 1.000 1.722 -0.550 1 2 3 3 1 2.000 1.399 0.508 1 2 3 3 2 1.000 2.867 -1.103 1 2 3 3 3 9.000 7.246 0.652 1 2 4 1 1 0.000 0.757 -0.870 1 2 4 1 2 0.000 0.369 -0.608 1 2 4 1 3 0.000 0.062 -0.249 1 2 4 2 1 0.000 0.226 -0.475 1 2 4 2 2 0.000 0.153 -0.391 1 2 4 2 3 0.000 0.048 -0.219 1 2 4 3 1 0.000 0.038 -0.194 1 2 4 3 2 0.000 0.081 -0.284 1 2 4 3 3 0.000 0.220 -0.469 1 3 1 1 1 184.000 154.576 2.367 1 3 1 1 2 34.000 42.829 -1.349 1 3 1 1 3 1.000 2.473 -0.936 1 3 1 2 1 26.000 32.420 -1.128 1 3 1 2 2 13.000 12.479 0.147 1 3 1 2 3 0.000 1.340 -1.158 1 3 1 3 1 0.000 1.585 -1.259 1 3 1 3 2 0.000 1.923 -1.387 1 3 1 3 3 3.000 1.807 0.887 1 3 2 1 1 29.000 31.458 -0.438 1 3 2 1 2 13.000 10.503 0.770 1 3 2 1 3 2.000 0.862 1.225 1 3 2 2 1 5.000 7.409 -0.885 1 3 2 2 2 5.000 3.437 0.843 1 3 2 2 3 0.000 0.525 -0.725 1 3 2 3 1 1.000 0.543 0.620 1 3 2 3 2 0.000 0.794 -0.891 1 3 2 3 3 2.000 1.061 0.911 1 3 3 1 1 31.000 31.430 -0.077 1 3 3 1 2 11.000 11.884 -0.256 1 3 3 1 3 0.000 1.234 -1.111 1 3 3 2 1 5.000 7.998 -1.060 1 3 3 2 2 3.000 4.201 -0.586 1 3 3 2 3 0.000 0.812 -0.901 1 3 3 3 1 0.000 0.768 -0.877 1 3 3 3 2 0.000 1.272 -1.128 1 3 3 3 3 1.000 2.150 -0.785 1 3 4 1 1 0.000 0.601 -0.775 1 3 4 1 2 0.000 0.237 -0.487 1 3 4 1 3 0.000 0.027 -0.163 1 3 4 2 1 0.000 0.157 -0.396 1 3 4 2 2 0.000 0.086 -0.293 1 3 4 2 3 0.000 0.018 -0.134 1 3 4 3 1 0.000 0.017 -0.129 1 3 4 3 2 0.000 0.029 -0.169 1 3 4 3 3 2.000 0.052 8.528 1 4 1 1 1 0.000 0.051 -0.225 1 4 1 1 2 0.000 0.021 -0.144 1 4 1 1 3 0.000 0.002 -0.050 1 4 1 2 1 0.000 0.014 -0.116 1 4 1 2 2 0.000 0.008 -0.088 1 4 1 2 3 0.000 0.002 -0.041 1 4 1 3 1 0.000 0.002 -0.039 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 0.064 -0.254 1 4 2 1 2 0.000 0.032 -0.178 1 4 2 1 3 0.000 0.005 -0.073 1 4 2 2 1 0.000 0.019 -0.139 1 4 2 2 2 0.000 0.013 -0.115 1 4 2 2 3 0.000 0.004 -0.065 1 4 2 3 1 0.000 0.003 -0.057 1 4 2 3 2 0.000 0.007 -0.084 1 4 2 3 3 0.000 0.020 -0.140 1 4 3 1 1 0.000 0.219 -0.468 1 4 3 1 2 0.000 0.122 -0.349 1 4 3 1 3 0.000 0.026 -0.162 1 4 3 2 1 0.000 0.071 -0.266 1 4 3 2 2 0.000 0.055 -0.234 1 4 3 2 3 0.000 0.022 -0.148 1 4 3 3 1 0.000 0.016 -0.126 1 4 3 3 2 0.000 0.038 -0.196 1 4 3 3 3 0.000 0.135 -0.367 1 4 4 1 1 0.000 0.006 -0.079 1 4 4 1 2 0.000 0.004 -0.061 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 -0.023 1 4 4 3 2 0.000 0.001 -0.036 1 4 4 3 3 1.000 0.005 14.170 2 1 1 1 1 4.000 5.800 -0.748 2 1 1 1 2 0.000 1.030 -1.015 2 1 1 1 3 3.000 0.844 2.348 2 1 1 2 1 0.000 0.809 -0.899 2 1 1 2 2 0.000 0.200 -0.447 2 1 1 2 3 1.000 0.304 1.262 2 1 1 3 1 0.000 0.112 -0.335 2 1 1 3 2 0.000 0.087 -0.295 2 1 1 3 3 3.000 1.160 1.708 2 1 2 1 1 3.000 2.525 0.299 2 1 2 1 2 0.000 0.540 -0.735 2 1 2 1 3 0.000 0.629 -0.793 2 1 2 2 1 1.000 0.395 0.962 2 1 2 2 2 0.000 0.118 -0.343 2 1 2 2 3 0.000 0.255 -0.505 2 1 2 3 1 0.000 0.082 -0.286 2 1 2 3 2 0.000 0.077 -0.277 2 1 2 3 3 2.000 1.457 0.449 2 1 3 1 1 5.000 3.856 0.583 2 1 3 1 2 0.000 0.935 -0.967 2 1 3 1 3 2.000 1.377 0.531 2 1 3 2 1 3.000 0.652 2.907 2 1 3 2 2 2.000 0.220 3.799 2 1 3 2 3 1.000 0.602 0.513 2 1 3 3 1 2.000 0.177 4.328 2 1 3 3 2 0.000 0.188 -0.434 2 1 3 3 3 4.000 4.514 -0.242 2 1 4 1 1 0.000 2.472 -1.572 2 1 4 1 2 0.000 0.625 -0.790 2 1 4 1 3 0.000 0.996 -0.998 2 1 4 2 1 0.000 0.429 -0.655 2 1 4 2 2 0.000 0.151 -0.388 2 1 4 2 3 0.000 0.447 -0.668 2 1 4 3 1 0.000 0.128 -0.357 2 1 4 3 2 0.000 0.141 -0.376 2 1 4 3 3 2.000 3.669 -0.871 2 2 1 1 1 23.000 24.117 -0.227 2 2 1 1 2 4.000 4.284 -0.137 2 2 1 1 3 5.000 3.508 0.797 2 2 1 2 1 2.000 3.362 -0.743 2 2 1 2 2 3.000 0.830 2.383 2 2 1 2 3 1.000 1.264 -0.235 2 2 1 3 1 2.000 0.465 2.250 2 2 1 3 2 1.000 0.362 1.060 2 2 1 3 3 7.000 4.824 0.991 2 2 2 1 1 12.000 10.499 0.463 2 2 2 1 2 3.000 2.247 0.502 2 2 2 1 3 3.000 2.617 0.237 2 2 2 2 1 4.000 1.644 1.838 2 2 2 2 2 2.000 0.489 2.162 2 2 2 2 3 0.000 1.059 -1.029 2 2 2 3 1 0.000 0.341 -0.584 2 2 2 3 2 1.000 0.320 1.203 2 2 2 3 3 4.000 6.060 -0.837 2 2 3 1 1 14.000 16.032 -0.507 2 2 3 1 2 7.000 3.886 1.580 2 2 3 1 3 9.000 5.724 1.369 2 2 3 2 1 5.000 2.712 1.390 2 2 3 2 2 1.000 0.913 0.091 2 2 3 2 3 3.000 2.503 0.314 2 2 3 3 1 4.000 0.737 3.799 2 2 3 3 2 0.000 0.783 -0.885 2 2 3 3 3 19.000 18.767 0.054 2 2 4 1 1 5.000 10.276 -1.646 2 2 4 1 2 3.000 2.597 0.250 2 2 4 1 3 4.000 4.140 -0.069 2 2 4 2 1 1.000 1.784 -0.587 2 2 4 2 2 0.000 0.626 -0.791 2 2 4 2 3 1.000 1.858 -0.629 2 2 4 3 1 0.000 0.531 -0.729 2 2 4 3 2 0.000 0.588 -0.767 2 2 4 3 3 5.000 15.253 -2.625 2 3 1 1 1 146.000 145.079 0.076 2 3 1 1 2 18.000 20.826 -0.619 2 3 1 1 3 13.000 11.405 0.472 2 3 1 2 1 14.000 17.716 -0.883 2 3 1 2 2 3.000 3.533 -0.284 2 3 1 2 3 1.000 3.600 -1.370 2 3 1 3 1 2.000 1.544 0.367 2 3 1 3 2 0.000 0.971 -0.985 2 3 1 3 3 9.000 8.651 0.119 2 3 2 1 1 21.000 23.020 -0.421 2 3 2 1 2 3.000 3.982 -0.492 2 3 2 1 3 1.000 3.102 -1.193 2 3 2 2 1 4.000 3.157 0.475 2 3 2 2 2 2.000 0.759 1.425 2 3 2 2 3 0.000 1.099 -1.049 2 3 2 3 1 0.000 0.413 -0.642 2 3 2 3 2 1.000 0.313 1.230 2 3 2 3 3 5.000 3.961 0.522 2 3 3 1 1 12.000 17.929 -1.400 2 3 3 1 2 5.000 3.512 0.794 2 3 3 1 3 5.000 3.460 0.828 2 3 3 2 1 3.000 2.656 0.211 2 3 3 2 2 0.000 0.723 -0.850 2 3 3 2 3 1.000 1.325 -0.283 2 3 3 3 1 1.000 0.455 0.808 2 3 3 3 2 1.000 0.390 0.976 2 3 3 3 3 6.000 6.257 -0.103 2 3 4 1 1 17.000 9.167 2.587 2 3 4 1 2 0.000 1.873 -1.368 2 3 4 1 3 4.000 1.996 1.418 2 3 4 2 1 1.000 1.394 -0.334 2 3 4 2 2 1.000 0.396 0.961 2 3 4 2 3 0.000 0.785 -0.886 2 3 4 3 1 0.000 0.261 -0.511 2 3 4 3 2 0.000 0.234 -0.484 2 3 4 3 3 10.000 4.057 2.951 2 4 1 1 1 2.000 1.839 0.118 2 4 1 1 2 0.000 0.389 -0.623 2 4 1 1 3 2.000 0.442 2.344 2 4 1 2 1 1.000 0.286 1.336 2 4 1 2 2 0.000 0.084 -0.290 2 4 1 2 3 1.000 0.177 1.953 2 4 1 3 1 0.000 0.058 -0.240 2 4 1 3 2 0.000 0.053 -0.231 2 4 1 3 3 0.000 0.988 -0.994 2 4 2 1 1 3.000 1.825 0.870 2 4 2 1 2 0.000 0.465 -0.682 2 4 2 1 3 0.000 0.751 -0.867 2 4 2 2 1 0.000 0.318 -0.564 2 4 2 2 2 1.000 0.113 2.645 2 4 2 2 3 0.000 0.339 -0.582 2 4 2 3 1 0.000 0.096 -0.310 2 4 2 3 2 0.000 0.107 -0.328 2 4 2 3 3 2.000 2.828 -0.492 2 4 3 1 1 3.000 4.827 -0.831 2 4 3 1 2 0.000 1.392 -1.180 2 4 3 1 3 4.000 2.847 0.683 2 4 3 2 1 0.000 0.910 -0.954 2 4 3 2 2 0.000 0.364 -0.604 2 4 3 2 3 0.000 1.387 -1.178 2 4 3 3 1 0.000 0.361 -0.601 2 4 3 3 2 0.000 0.456 -0.675 2 4 3 3 3 7.000 15.171 -2.098 2 4 4 1 1 8.000 3.721 2.218 2 4 4 1 2 0.000 1.119 -1.058 2 4 4 1 3 3.000 2.477 0.333 2 4 4 2 1 0.000 0.720 -0.848 2 4 4 2 2 2.000 0.301 3.099 2 4 4 2 3 1.000 1.238 -0.214 2 4 4 3 1 0.000 0.313 -0.559 2 4 4 3 2 1.000 0.412 0.917 2 4 4 3 3 23.000 14.829 2.122 *** LOG-LINEAR PARAMETERS *** * TABLE TABCD [or P(TABCD)] * effect beta exp(beta) main -0.5183 0.5955 T 1 -0.6565 0.5187 2 0.6565 1.9279 A 1 -0.1116 0.8944 2 1.0472 2.8495 3 1.1147 3.0487 4 -2.0503 0.1287 B 1 0.3138 1.3686 2 0.0142 1.0143 3 0.8341 2.3027 4 -1.1621 0.3128 C 1 0.8777 2.4054 2 -0.2700 0.7634 3 -0.6078 0.5446 D 1 0.3315 1.3930 2 -0.2632 0.7686 3 -0.0683 0.9340 TA 1 1 0.6857 1.9851 1 2 0.4194 1.5211 1 3 0.3612 1.4351 1 4 -1.4663 0.2308 2 1 -0.6857 0.5038 2 2 -0.4194 0.6574 2 3 -0.3612 0.6968 2 4 1.4663 4.3333 TB 1 1 0.2551 1.2906 1 2 0.3795 1.4616 1 3 0.5041 1.6554 1 4 -1.1387 0.3202 2 1 -0.2551 0.7748 2 2 -0.3795 0.6842 2 3 -0.5041 0.6041 2 4 1.1387 3.1227 TC 1 1 -0.0840 0.9195 1 2 0.1865 1.2050 1 3 -0.1025 0.9026 2 1 0.0840 1.0876 2 2 -0.1865 0.8299 2 3 0.1025 1.1080 TD 1 1 0.1558 1.1686 1 2 0.4846 1.6235 1 3 -0.6403 0.5271 2 1 -0.1558 0.8558 2 2 -0.4846 0.6160 2 3 0.6403 1.8971 type 2 association (row=A column=B slab=-) association 1.0000 row 0.0357 0.0357 -0.7410 0.6696 adj row 0.0357 0.0357 -0.7410 0.6696 column -0.7751 -0.0949 0.3589 0.5112 adj column -0.7751 -0.0949 0.3589 0.5112 slab 1.9102 adj slab 1.9102 type 2 association (row=A column=C slab=-) association 1.0000 row 0.0357 0.0357 -0.7410 0.6696 adj row 0.0357 0.0357 -0.7410 0.6696 column -0.5489 -0.2491 0.7979 adj column -0.5489 -0.2491 0.7979 slab 0.5688 adj slab 0.5688 type 2 association (row=A column=D slab=-) association 1.0000 row 0.0357 0.0357 -0.7410 0.6696 adj row 0.0357 0.0357 -0.7410 0.6696 column -0.6248 -0.1428 0.7676 adj column -0.6248 -0.1428 0.7676 slab 0.5688 adj slab 0.5688 type 2 association (row=B column=C slab=-) association 1.0000 row -0.7751 -0.0949 0.3589 0.5112 adj row -0.7751 -0.0949 0.3589 0.5112 column -0.5489 -0.2491 0.7979 adj column -0.5489 -0.2491 0.7979 slab 0.5688 adj slab 0.5688 type 2 association (row=B column=D slab=-) association 1.0000 row -0.7751 -0.0949 0.3589 0.5112 adj row -0.7751 -0.0949 0.3589 0.5112 column -0.6248 -0.1428 0.7676 adj column -0.6248 -0.1428 0.7676 slab 0.5688 adj slab 0.5688 type 2 association (row=C column=D slab=-) association 1.0000 row -0.5489 -0.2491 0.7979 adj row -0.5489 -0.2491 0.7979 column -0.6248 -0.1428 0.7676 adj column -0.6248 -0.1428 0.7676 slab 2.2751 adj slab 2.2751