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-multiplicative models: graph 3 (c) with restrictions * equal ratings * high man 3 dim 4 6 6 lab S X Y mod {S X Y XY ass2(S,X,5a) ass2(S,Y,5a,2) } des [ 1 2 1 2 ] ass_equ [ 1 2 3 2 ] 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 = 28 Converge criterion = 0.0000005918 X-squared = 106.2802 (0.2439) L-squared = 117.1519 (0.0801) Cressie-Read = 106.9237 (0.2306) Dissimilarity index = 0.0942 Degrees of freedom = 97 Log-likelihood = -6399.55948 Number of parameters = 46 (+1) Sample size = 1399.0 BIC(L-squared) = -585.4689 AIC(L-squared) = -76.8481 BIC(log-likelihood) = 13132.3206 AIC(log-likelihood) = 12891.1190 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 44.000 36.462 1.248 1 1 2 4.000 6.215 -0.888 1 1 3 9.000 9.070 -0.023 1 1 4 7.000 5.327 0.725 1 1 5 6.000 5.399 0.259 1 1 6 7.000 10.648 -1.118 1 2 1 13.000 13.060 -0.017 1 2 2 30.000 25.505 0.890 1 2 3 20.000 18.812 0.274 1 2 4 8.000 11.693 -1.080 1 2 5 14.000 14.434 -0.114 1 2 6 7.000 6.484 0.203 1 3 1 9.000 12.320 -0.946 1 3 2 23.000 21.573 0.307 1 3 3 17.000 15.741 0.317 1 3 4 17.000 14.007 0.800 1 3 5 3.000 4.522 -0.716 1 3 6 0.000 2.608 -1.615 1 4 1 16.000 14.947 0.272 1 4 2 17.000 20.312 -0.735 1 4 3 10.000 13.911 -1.049 1 4 4 20.000 19.058 0.216 1 4 5 2.000 3.773 -0.913 1 4 6 2.000 2.772 -0.464 1 5 1 5.000 4.482 0.244 1 5 2 4.000 7.195 -1.191 1 5 3 9.000 6.743 0.869 1 5 4 10.000 6.138 1.559 1 5 5 4.000 3.350 0.355 1 5 6 0.000 1.123 -1.060 1 6 1 3.000 2.933 0.039 1 6 2 3.000 2.566 0.271 1 6 3 0.000 2.260 -1.503 1 6 4 1.000 1.904 -0.655 1 6 5 4.000 0.938 3.163 1 6 6 1.000 0.716 0.336 2 1 1 7.000 12.426 -1.539 2 1 2 4.000 2.306 1.115 2 1 3 5.000 4.767 0.107 2 1 4 5.000 4.548 0.212 2 1 5 14.000 12.401 0.454 2 1 6 69.000 74.604 -0.649 2 2 1 5.000 4.356 0.309 2 2 2 7.000 9.264 -0.744 2 2 3 13.000 9.675 1.069 2 2 4 15.000 9.770 1.673 2 2 5 38.000 32.449 0.974 2 2 6 37.000 44.460 -1.119 2 3 1 6.000 3.764 1.153 2 3 2 7.000 7.177 -0.066 2 3 3 8.000 7.416 0.215 2 3 4 10.000 10.720 -0.220 2 3 5 10.000 9.312 0.226 2 3 6 15.000 16.380 -0.341 2 4 1 4.000 4.040 -0.020 2 4 2 12.000 5.979 2.462 2 4 3 5.000 5.798 -0.332 2 4 4 13.000 12.905 0.027 2 4 5 6.000 6.875 -0.334 2 4 6 14.000 15.406 -0.358 2 5 1 2.000 0.944 1.087 2 5 2 3.000 1.650 1.051 2 5 3 1.000 2.189 -0.804 2 5 4 1.000 3.238 -1.244 2 5 5 3.000 4.755 -0.805 2 5 6 5.000 4.863 0.062 2 6 1 0.000 0.466 -0.683 2 6 2 0.000 0.444 -0.666 2 6 3 1.000 0.554 0.600 2 6 4 1.000 0.758 0.278 2 6 5 1.000 1.004 -0.004 2 6 6 3.000 2.339 0.432 3 1 1 8.000 9.276 -0.419 3 1 2 2.000 1.581 0.333 3 1 3 2.000 2.308 -0.202 3 1 4 1.000 1.355 -0.305 3 1 5 0.000 1.374 -1.172 3 1 6 4.000 2.709 0.784 3 2 1 5.000 3.701 0.675 3 2 2 5.000 7.227 -0.828 3 2 3 5.000 5.330 -0.143 3 2 4 5.000 3.313 0.927 3 2 5 5.000 4.090 0.450 3 2 6 3.000 1.837 0.858 3 3 1 8.000 5.419 1.109 3 3 2 10.000 9.489 0.166 3 3 3 7.000 6.924 0.029 3 3 4 4.000 6.162 -0.871 3 3 5 1.000 1.989 -0.701 3 3 6 1.000 1.147 -0.137 3 4 1 12.000 12.142 -0.041 3 4 2 17.000 16.501 0.123 3 4 3 15.000 11.301 1.100 3 4 4 13.000 15.482 -0.631 3 4 5 2.000 3.065 -0.608 3 4 6 2.000 2.252 -0.168 3 5 1 12.000 12.726 -0.204 3 5 2 17.000 20.429 -0.759 3 5 3 19.000 19.143 -0.033 3 5 4 18.000 17.427 0.137 3 5 5 10.000 9.512 0.158 3 5 6 4.000 3.190 0.454 3 6 1 31.000 34.104 -0.531 3 6 2 29.000 29.844 -0.155 3 6 3 25.000 26.278 -0.249 3 6 4 24.000 22.143 0.395 3 6 5 12.000 10.903 0.332 3 6 6 12.000 8.324 1.274 4 1 1 4.000 4.836 -0.380 4 1 2 1.000 0.898 0.108 4 1 3 2.000 1.855 0.106 4 1 4 0.000 1.770 -1.330 4 1 5 4.000 4.827 -0.376 4 1 6 37.000 29.038 1.477 4 2 1 0.000 1.883 -1.372 4 2 2 4.000 4.004 -0.002 4 2 3 0.000 4.182 -2.045 4 2 4 1.000 4.223 -1.569 4 2 5 8.000 14.027 -1.609 4 2 6 25.000 19.219 1.319 4 3 1 1.000 2.497 -0.947 4 3 2 3.000 4.761 -0.807 4 3 3 3.000 4.919 -0.865 4 3 4 7.000 7.111 -0.042 4 3 5 8.000 6.177 0.734 4 3 6 15.000 10.865 1.254 4 4 1 4.000 4.870 -0.394 4 4 2 4.000 7.207 -1.195 4 4 3 8.000 6.990 0.382 4 4 4 17.000 15.556 0.366 4 4 5 12.000 8.287 1.290 4 4 6 21.000 18.570 0.564 4 5 1 3.000 3.847 -0.432 4 5 2 12.000 6.725 2.034 4 5 3 8.000 8.924 -0.309 4 5 4 11.000 13.198 -0.605 4 5 5 20.000 19.382 0.140 4 5 6 20.000 19.824 0.040 4 6 1 11.000 7.498 1.279 4 6 2 8.000 7.145 0.320 4 6 3 12.000 8.909 1.036 4 6 4 11.000 12.195 -0.342 4 6 5 12.000 16.155 -1.034 4 6 6 33.000 37.622 -0.753 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 1.8254 6.2052 S 1 0.0575 1.0592 2 -0.2254 0.7982 3 0.0305 1.0310 4 0.1374 1.1473 X 1 -0.1856 0.8306 2 0.2548 1.2902 3 0.0455 1.0465 4 0.3199 1.3769 5 -0.0412 0.9597 6 -0.3933 0.6748 Y 1 -0.0873 0.9164 2 -0.0269 0.9735 3 0.0117 1.0118 4 0.0752 1.0781 5 -0.0700 0.9324 6 0.0972 1.1021 XY 1 1 0.9274 2.5280 1 2 -0.8597 0.4233 1 3 -0.3462 0.7074 1 4 -0.6993 0.4969 1 5 -0.0459 0.9551 1 6 1.0237 2.7835 2 1 -0.4973 0.6082 2 2 0.1542 1.1667 2 3 -0.0148 0.9853 2 4 -0.3111 0.7326 2 5 0.5395 1.7151 2 6 0.1296 1.1383 3 1 -0.1732 0.8410 3 2 0.3692 1.4466 3 3 0.1895 1.2086 3 4 0.2519 1.2865 3 5 -0.2387 0.7877 3 6 -0.3987 0.6712 4 1 -0.0128 0.9873 4 2 0.2761 1.3180 4 3 0.0330 1.0335 4 4 0.5269 1.6936 4 5 -0.4526 0.6360 4 6 -0.3705 0.6904 5 1 -0.3636 0.6952 5 2 0.0919 1.0963 5 3 0.1623 1.1762 5 4 0.2475 1.2808 5 5 0.2821 1.3259 5 6 -0.4202 0.6569 6 1 0.1194 1.1268 6 2 -0.0318 0.9687 6 3 -0.0237 0.9766 6 4 -0.0158 0.9843 6 5 -0.0843 0.9192 6 6 0.0362 1.0369 type 2 association (row=S column=X) association 3.3916 row -0.3899 -0.5876 0.6006 0.3768 adj row -0.7180 -1.0821 1.1061 0.6940 column -0.3994 -0.3674 -0.2364 -0.0538 0.3187 0.7384 adj column -0.7356 -0.6765 -0.4354 -0.0991 0.5869 1.3598 type 2 association (row=S column=Y) association 2.6572 row -0.5000 0.5000 -0.5000 0.5000 adj row -0.8150 0.8150 -0.8150 0.8150 column -0.3994 -0.3674 -0.2364 -0.0538 0.3187 0.7384 adj column -0.6511 -0.5988 -0.3854 -0.0877 0.5194 1.2036