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 *** * Olzak & Wickens (1983) The interpretation of detection data * through direct multivariate frequency analysis. * Psych Bulletin, 93, 574-585. * * Subject B: Log-multiplicative model, graph 3 (c) * man 3 dim 4 6 6 lab S X Y mod {S X Y XY ass2(S,X,5a) ass2(S,Y,5a) } nco dat [ 69 6 1 1 0 0 34 20 10 3 1 0 43 24 13 9 1 0 78 40 20 6 0 1 32 38 17 5 4 0 5 14 3 2 0 0 10 5 2 11 16 28 8 5 11 43 27 38 9 6 7 28 32 45 8 6 14 19 23 22 4 5 7 6 18 18 0 1 2 3 5 8 4 1 0 0 0 0 5 3 2 1 0 0 8 6 3 1 0 0 36 25 18 3 1 0 83 69 26 6 1 0 127 50 12 7 2 0 5 0 1 4 4 9 0 1 3 6 9 27 2 3 2 11 27 20 9 12 11 10 23 31 16 7 5 19 33 40 21 14 13 20 21 61 ] *** STATISTICS *** Number of iterations = 502 Converge criterion = 0.0000009959 X-squared = 130.8899 (0.0039) L-squared = 130.0915 (0.0045) Cressie-Read = 127.0345 (0.0075) Dissimilarity index = 0.0808 Degrees of freedom = 91 Log-likelihood = -8621.97973 Number of parameters = 52 (+1) Sample size = 2000.0 BIC(L-squared) = -561.5906 AIC(L-squared) = -51.9085 BIC(log-likelihood) = 17639.2064 AIC(log-likelihood) = 17347.9595 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 69.000 64.495 0.561 1 1 2 6.000 8.354 -0.815 1 1 3 1.000 2.037 -0.726 1 1 4 1.000 2.552 -0.972 1 1 5 0.000 0.664 -0.815 1 1 6 0.000 0.073 -0.270 1 2 1 34.000 33.128 0.152 1 2 2 20.000 19.448 0.125 1 2 3 10.000 12.838 -0.792 1 2 4 3.000 8.270 -1.832 1 2 5 1.000 1.201 -0.183 1 2 6 0.000 0.124 -0.352 1 3 1 43.000 41.219 0.277 1 3 2 24.000 24.712 -0.143 1 3 3 13.000 11.747 0.366 1 3 4 9.000 7.336 0.615 1 3 5 1.000 1.865 -0.634 1 3 6 0.000 0.118 -0.344 1 4 1 78.000 68.093 1.201 1 4 2 40.000 41.337 -0.208 1 4 3 20.000 23.784 -0.776 1 4 4 6.000 4.690 0.605 1 4 5 0.000 1.201 -1.096 1 4 6 1.000 0.079 3.276 1 5 1 32.000 40.034 -1.270 1 5 2 38.000 34.144 0.660 1 5 3 17.000 12.508 1.270 1 5 4 5.000 2.869 1.258 1 5 5 4.000 0.934 3.173 1 5 6 0.000 0.054 -0.233 1 6 1 5.000 17.148 -2.934 1 6 2 14.000 8.678 1.807 1 6 3 3.000 2.795 0.122 1 6 4 2.000 1.210 0.718 1 6 5 0.000 0.233 -0.483 1 6 6 0.000 0.032 -0.179 2 1 1 10.000 13.800 -1.023 2 1 2 5.000 2.287 1.793 2 1 3 2.000 1.441 0.466 2 1 4 11.000 10.671 0.101 2 1 5 16.000 15.364 0.162 2 1 6 28.000 28.732 -0.137 2 2 1 8.000 6.734 0.488 2 2 2 5.000 5.059 -0.026 2 2 3 11.000 8.628 0.807 2 2 4 43.000 32.847 1.772 2 2 5 27.000 26.400 0.117 2 2 6 38.000 46.605 -1.260 2 3 1 9.000 7.955 0.371 2 3 2 6.000 6.103 -0.042 2 3 3 7.000 7.495 -0.181 2 3 4 28.000 27.661 0.064 2 3 5 32.000 38.945 -1.113 2 3 6 45.000 42.093 0.448 2 4 1 8.000 11.618 -1.061 2 4 2 6.000 9.025 -1.007 2 4 3 14.000 13.416 0.159 2 4 4 19.000 15.635 0.851 2 4 5 23.000 22.168 0.177 2 4 6 22.000 24.918 -0.585 2 5 1 4.000 5.890 -0.779 2 5 2 5.000 6.428 -0.563 2 5 3 7.000 6.083 0.372 2 5 4 6.000 8.246 -0.782 2 5 5 18.000 14.861 0.814 2 5 6 18.000 14.773 0.840 2 6 1 0.000 2.131 -1.460 2 6 2 1.000 1.380 -0.323 2 6 3 2.000 1.148 0.795 2 6 4 3.000 2.937 0.037 2 6 5 5.000 3.134 1.054 2 6 6 8.000 7.384 0.227 3 1 1 4.000 6.781 -1.068 3 1 2 1.000 0.869 0.141 3 1 3 0.000 0.203 -0.450 3 1 4 0.000 0.234 -0.484 3 1 5 0.000 0.056 -0.238 3 1 6 0.000 0.005 -0.074 3 2 1 5.000 5.177 -0.078 3 2 2 3.000 3.005 -0.003 3 2 3 2.000 1.900 0.073 3 2 4 1.000 1.129 -0.121 3 2 5 0.000 0.152 -0.389 3 2 6 0.000 0.014 -0.117 3 3 1 8.000 9.628 -0.525 3 3 2 6.000 5.708 0.122 3 3 3 3.000 2.599 0.249 3 3 4 1.000 1.497 -0.406 3 3 5 0.000 0.352 -0.593 3 3 6 0.000 0.020 -0.140 3 4 1 36.000 41.249 -0.817 3 4 2 25.000 24.762 0.048 3 4 3 18.000 13.646 1.179 3 4 4 3.000 2.482 0.329 3 4 5 1.000 0.588 0.537 3 4 6 0.000 0.034 -0.184 3 5 1 83.000 76.303 0.767 3 5 2 69.000 64.351 0.580 3 5 3 26.000 22.578 0.720 3 5 4 6.000 4.777 0.560 3 5 5 1.000 1.438 -0.366 3 5 6 0.000 0.074 -0.271 3 6 1 127.000 120.544 0.588 3 6 2 50.000 60.325 -1.329 3 6 3 12.000 18.611 -1.533 3 6 4 7.000 7.430 -0.158 3 6 5 2.000 1.324 0.587 3 6 6 0.000 0.161 -0.401 4 1 1 5.000 2.924 1.214 4 1 2 0.000 0.489 -0.700 4 1 3 1.000 0.320 1.202 4 1 4 4.000 2.542 0.914 4 1 5 4.000 3.916 0.043 4 1 6 9.000 8.190 0.283 4 2 1 0.000 1.961 -1.400 4 2 2 1.000 1.488 -0.400 4 2 3 3.000 2.634 0.226 4 2 4 6.000 10.755 -1.450 4 2 5 9.000 9.248 -0.081 4 2 6 27.000 18.258 2.046 4 3 1 2.000 3.199 -0.670 4 3 2 3.000 2.478 0.332 4 3 3 2.000 3.159 -0.652 4 3 4 11.000 12.506 -0.426 4 3 5 27.000 18.837 1.881 4 3 6 20.000 22.770 -0.580 4 4 1 9.000 10.040 -0.328 4 4 2 12.000 7.876 1.470 4 4 3 11.000 12.154 -0.331 4 4 4 10.000 15.192 -1.332 4 4 5 23.000 23.043 -0.009 4 4 6 31.000 28.969 0.377 4 5 1 16.000 12.773 0.903 4 5 2 7.000 14.077 -1.886 4 5 3 5.000 13.831 -2.375 4 5 4 19.000 20.108 -0.247 4 5 5 33.000 38.767 -0.926 4 5 6 40.000 43.100 -0.472 4 6 1 21.000 13.177 2.155 4 6 2 14.000 8.617 1.834 4 6 3 13.000 7.445 2.036 4 6 4 20.000 20.423 -0.094 4 6 5 21.000 23.309 -0.478 4 6 6 61.000 61.423 -0.054 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 1.5122 4.5369 S 1 -0.3052 0.7370 2 0.7065 2.0268 3 -1.0093 0.3645 4 0.6080 1.8367 X 1 -1.0357 0.3550 2 -0.1623 0.8502 3 0.0996 1.1047 4 0.5397 1.7155 5 0.5684 1.7654 6 -0.0097 0.9903 Y 1 1.1018 3.0096 2 0.4868 1.6272 3 0.0833 1.0868 4 0.1992 1.2204 5 -0.4106 0.6633 6 -1.4605 0.2321 XY 1 1 0.8662 2.3780 1 2 -0.4396 0.6443 1 3 -0.9745 0.3774 1 4 0.0211 1.0213 1 5 0.1371 1.1469 1 6 0.3897 1.4766 2 1 -0.5204 0.5943 2 2 -0.3152 0.7297 2 3 0.1462 1.1574 2 4 0.4761 1.6098 2 5 0.0092 1.0092 2 6 0.2041 1.2265 3 1 -0.4086 0.6646 3 2 -0.1824 0.8333 3 3 -0.0493 0.9519 3 4 0.2496 1.2835 3 5 0.3432 1.4095 3 6 0.0476 1.0487 4 1 0.0211 1.0213 4 2 0.2599 1.2968 4 3 0.5839 1.7930 4 4 -0.2699 0.7634 4 5 -0.1693 0.8443 4 6 -0.4257 0.6533 5 1 -0.0962 0.9083 5 2 0.4825 1.6202 5 3 0.3550 1.4262 5 4 -0.3477 0.7063 5 5 -0.0072 0.9928 5 6 -0.3865 0.6794 6 1 0.1379 1.1479 6 2 0.1947 1.2150 6 3 -0.0614 0.9405 6 4 -0.1291 0.8788 6 5 -0.3129 0.7313 6 6 0.1708 1.1862 type 2 association (row=S column=X) association 3.3838 row -0.4129 -0.5512 0.6567 0.3074 adj row -0.7595 -1.0139 1.2079 0.5655 column -0.4626 -0.3531 -0.2421 0.0213 0.3380 0.6986 adj column -0.8510 -0.6496 -0.4453 0.0391 0.6217 1.2851 type 2 association (row=S column=Y) association 6.8257 row -0.4778 0.4807 -0.5214 0.5185 adj row -1.2483 1.2558 -1.3621 1.3546 column -0.4234 -0.3857 -0.2406 0.0309 0.2925 0.7263 adj column -1.1062 -1.0077 -0.6287 0.0808 0.7642 1.8976