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 * man 3 dim 4 6 6 lab S X Y mod {S X Y XY ass2(S,X,5a) ass2(S,Y,5a) } 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 = 27 Converge criterion = 0.0000009841 X-squared = 101.1903 (0.3128) L-squared = 107.8964 (0.1725) Cressie-Read = 100.2197 (0.3372) Dissimilarity index = 0.0862 Degrees of freedom = 95 Log-likelihood = -6394.93177 Number of parameters = 48 (+1) Sample size = 1399.0 BIC(L-squared) = -580.2373 AIC(L-squared) = -82.1036 BIC(log-likelihood) = 13137.5522 AIC(log-likelihood) = 12885.8635 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 44.000 37.950 0.982 1 1 2 4.000 6.412 -0.952 1 1 3 9.000 9.109 -0.036 1 1 4 7.000 5.258 0.759 1 1 5 6.000 5.180 0.360 1 1 6 7.000 9.317 -0.759 1 2 1 13.000 13.586 -0.159 1 2 2 30.000 26.292 0.723 1 2 3 20.000 18.855 0.264 1 2 4 8.000 11.501 -1.032 1 2 5 14.000 13.749 0.068 1 2 6 7.000 5.604 0.590 1 3 1 9.000 12.847 -1.073 1 3 2 23.000 22.259 0.157 1 3 3 17.000 15.701 0.328 1 3 4 17.000 13.603 0.921 1 3 5 3.000 4.182 -0.578 1 3 6 0.000 2.142 -1.464 1 4 1 16.000 15.907 0.023 1 4 2 17.000 21.336 -0.939 1 4 3 10.000 13.989 -1.067 1 4 4 20.000 18.421 0.368 1 4 5 2.000 3.386 -0.753 1 4 6 2.000 2.146 -0.100 1 5 1 5.000 5.010 -0.005 1 5 2 4.000 7.893 -1.386 1 5 3 9.000 6.932 0.786 1 5 4 10.000 5.903 1.686 1 5 5 4.000 2.845 0.685 1 5 6 0.000 0.784 -0.885 1 6 1 3.000 3.161 -0.090 1 6 2 3.000 2.704 0.180 1 6 3 0.000 2.197 -1.482 1 6 4 1.000 1.697 -0.535 1 6 5 4.000 0.711 3.901 1 6 6 1.000 0.433 0.863 2 1 1 7.000 13.464 -1.762 2 1 2 4.000 2.523 0.930 2 1 3 5.000 5.246 -0.107 2 1 4 5.000 4.858 0.065 2 1 5 14.000 12.508 0.422 2 1 6 69.000 71.165 -0.257 2 2 1 5.000 4.713 0.132 2 2 2 7.000 10.116 -0.980 2 2 3 13.000 10.617 0.731 2 2 4 15.000 10.388 1.431 2 2 5 38.000 32.461 0.972 2 2 6 37.000 41.852 -0.750 2 3 1 6.000 4.103 0.936 2 3 2 7.000 7.885 -0.315 2 3 3 8.000 8.140 -0.049 2 3 4 10.000 11.312 -0.390 2 3 5 10.000 9.091 0.302 2 3 6 15.000 14.732 0.070 2 4 1 4.000 4.585 -0.273 2 4 2 12.000 6.821 1.983 2 4 3 5.000 6.546 -0.604 2 4 4 13.000 13.826 -0.222 2 4 5 6.000 6.642 -0.249 2 4 6 14.000 13.320 0.186 2 5 1 2.000 1.172 0.764 2 5 2 3.000 2.048 0.665 2 5 3 1.000 2.633 -1.006 2 5 4 1.000 3.597 -1.369 2 5 5 3.000 4.531 -0.719 2 5 6 5.000 3.948 0.529 2 6 1 0.000 0.576 -0.759 2 6 2 0.000 0.547 -0.739 2 6 3 1.000 0.650 0.434 2 6 4 1.000 0.805 0.217 2 6 5 1.000 0.882 0.126 2 6 6 3.000 1.697 1.000 3 1 1 8.000 8.016 -0.006 3 1 2 2.000 1.384 0.524 3 1 3 2.000 2.127 -0.087 3 1 4 1.000 1.353 -0.304 3 1 5 0.000 1.626 -1.275 3 1 6 4.000 3.710 0.151 3 2 1 5.000 3.276 0.953 3 2 2 5.000 6.477 -0.580 3 2 3 5.000 5.025 -0.011 3 2 4 5.000 3.379 0.882 3 2 5 5.000 4.927 0.033 3 2 6 3.000 2.547 0.284 3 3 1 8.000 5.041 1.318 3 3 2 10.000 8.923 0.360 3 3 3 7.000 6.810 0.073 3 3 4 4.000 6.504 -0.982 3 3 5 1.000 2.439 -0.921 3 3 6 1.000 1.585 -0.465 3 4 1 12.000 11.423 0.171 3 4 2 17.000 15.653 0.341 3 4 3 15.000 11.103 1.170 3 4 4 13.000 16.120 -0.777 3 4 5 2.000 3.613 -0.849 3 4 6 2.000 2.906 -0.531 3 5 1 12.000 12.292 -0.083 3 5 2 17.000 19.783 -0.626 3 5 3 19.000 18.797 0.047 3 5 4 18.000 17.649 0.084 3 5 5 10.000 10.374 -0.116 3 5 6 4.000 3.625 0.197 3 6 1 31.000 33.818 -0.485 3 6 2 29.000 29.553 -0.102 3 6 3 25.000 25.984 -0.193 3 6 4 24.000 22.128 0.398 3 6 5 12.000 11.304 0.207 3 6 6 12.000 8.725 1.109 4 1 1 4.000 3.570 0.228 4 1 2 1.000 0.682 0.386 4 1 3 2.000 1.518 0.391 4 1 4 0.000 1.530 -1.237 4 1 5 4.000 4.686 -0.317 4 1 6 37.000 32.808 0.732 4 2 1 0.000 1.425 -1.194 4 2 2 4.000 3.116 0.501 4 2 3 0.000 3.502 -1.871 4 2 4 1.000 3.731 -1.414 4 2 5 8.000 13.864 -1.575 4 2 6 25.000 21.997 0.640 4 3 1 1.000 2.009 -0.712 4 3 2 3.000 3.933 -0.471 4 3 3 3.000 4.349 -0.647 4 3 4 7.000 6.581 0.163 4 3 5 8.000 6.288 0.683 4 3 6 15.000 12.541 0.694 4 4 1 4.000 4.084 -0.042 4 4 2 4.000 6.190 -0.880 4 4 3 8.000 6.362 0.649 4 4 4 17.000 14.633 0.619 4 4 5 12.000 8.359 1.259 4 4 6 21.000 20.628 0.082 4 5 1 3.000 3.525 -0.280 4 5 2 12.000 6.275 2.285 4 5 3 8.000 8.639 -0.217 4 5 4 11.000 12.851 -0.516 4 5 5 20.000 19.250 0.171 4 5 6 20.000 20.643 -0.141 4 6 1 11.000 7.446 1.303 4 6 2 8.000 7.197 0.299 4 6 3 12.000 9.168 0.935 4 6 4 11.000 12.369 -0.389 4 6 5 12.000 16.103 -1.023 4 6 6 33.000 38.145 -0.833 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 1.8157 6.1457 S 1 0.0146 1.0147 2 -0.1693 0.8443 3 0.0767 1.0797 4 0.0780 1.0811 X 1 -0.1981 0.8203 2 0.2501 1.2842 3 0.0529 1.0543 4 0.3272 1.3871 5 -0.0276 0.9728 6 -0.4045 0.6673 Y 1 -0.0920 0.9121 2 -0.0263 0.9741 3 0.0194 1.0196 4 0.0795 1.0828 5 -0.0655 0.9366 6 0.0848 1.0885 XY 1 1 0.8719 2.3916 1 2 -0.9101 0.4025 1 3 -0.3773 0.6857 1 4 -0.7050 0.4941 1 5 -0.0017 0.9983 1 6 1.1222 3.0716 2 1 -0.5489 0.5776 2 2 0.1074 1.1134 2 3 -0.0435 0.9574 2 4 -0.3160 0.7290 2 5 0.5808 1.7875 2 6 0.2202 1.2463 3 1 -0.2066 0.8133 3 2 0.3391 1.4037 3 3 0.1717 1.1873 3 4 0.2500 1.2841 3 5 -0.2111 0.8097 3 6 -0.3431 0.7096 4 1 -0.0179 0.9823 4 2 0.2718 1.3123 4 3 0.0313 1.0318 4 4 0.5283 1.6961 4 5 -0.4473 0.6394 4 6 -0.3662 0.6933 5 1 -0.3113 0.7325 5 2 0.1392 1.1494 5 3 0.1910 1.2104 5 4 0.2522 1.2869 5 5 0.2407 1.2721 5 6 -0.5118 0.5994 6 1 0.2128 1.2371 6 2 0.0526 1.0541 6 3 0.0269 1.0272 6 4 -0.0096 0.9905 6 5 -0.1614 0.8509 6 6 -0.1213 0.8857 type 2 association (row=S column=X) association 3.4544 row -0.4085 -0.5765 0.5813 0.4036 adj row -0.7592 -1.0714 1.0804 0.7502 column -0.4072 -0.3684 -0.2260 -0.0493 0.3101 0.7408 adj column -0.7568 -0.6848 -0.4201 -0.0916 0.5763 1.3769 type 2 association (row=S column=Y) association 2.6889 row -0.5933 0.4010 -0.3879 0.5802 adj row -0.9729 0.6576 -0.6361 0.9515 column -0.4072 -0.3684 -0.2260 -0.0493 0.3101 0.7408 adj column -0.6677 -0.6042 -0.3706 -0.0808 0.5085 1.2148