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 (d) * man 3 dim 4 6 6 lab S X Y mod {S X Y ass2(X,Y,5a) 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 = 574 Converge criterion = 0.0000009924 X-squared = 225.2418 (0.0000) L-squared = 220.0194 (0.0000) Cressie-Read = 218.2625 (0.0000) Dissimilarity index = 0.1144 Degrees of freedom = 107 Log-likelihood = -8666.94367 Number of parameters = 36 (+1) Sample size = 2000.0 BIC(L-squared) = -593.2772 AIC(L-squared) = 6.0194 BIC(log-likelihood) = 17607.5198 AIC(log-likelihood) = 17405.8873 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 69.000 36.423 5.398 1 1 2 6.000 17.332 -2.722 1 1 3 1.000 8.040 -2.483 1 1 4 1.000 4.207 -1.564 1 1 5 0.000 0.936 -0.967 1 1 6 0.000 0.068 -0.261 1 2 1 34.000 45.037 -1.645 1 2 2 20.000 17.426 0.617 1 2 3 10.000 8.561 0.492 1 2 4 3.000 7.493 -1.641 1 2 5 1.000 1.405 -0.342 1 2 6 0.000 0.115 -0.339 1 3 1 43.000 52.549 -1.317 1 3 2 24.000 22.202 0.382 1 3 3 13.000 10.644 0.722 1 3 4 9.000 7.486 0.553 1 3 5 1.000 1.510 -0.415 1 3 6 0.000 0.117 -0.343 1 4 1 78.000 68.264 1.178 1 4 2 40.000 45.950 -0.878 1 4 3 20.000 19.356 0.146 1 4 4 6.000 4.278 0.833 1 4 5 0.000 1.267 -1.126 1 4 6 1.000 0.075 3.376 1 5 1 32.000 44.796 -1.912 1 5 2 38.000 30.791 1.299 1 5 3 17.000 12.895 1.143 1 5 4 5.000 2.705 1.395 1 5 5 4.000 0.815 3.527 1 5 6 0.000 0.048 -0.218 1 6 1 5.000 14.349 -2.468 1 6 2 14.000 7.718 2.261 1 6 3 3.000 3.460 -0.247 1 6 4 2.000 1.335 0.575 1 6 5 0.000 0.329 -0.573 1 6 6 0.000 0.022 -0.149 2 1 1 10.000 7.586 0.877 2 1 2 5.000 4.507 0.232 2 1 3 2.000 5.524 -1.499 2 1 4 11.000 16.418 -1.337 2 1 5 16.000 19.571 -0.807 2 1 6 28.000 27.921 0.015 2 2 1 8.000 9.071 -0.356 2 2 2 5.000 4.383 0.295 2 2 3 11.000 5.689 2.227 2 2 4 43.000 28.277 2.769 2 2 5 27.000 28.414 -0.265 2 2 6 38.000 45.735 -1.144 2 3 1 9.000 10.204 -0.377 2 3 2 6.000 5.383 0.266 2 3 3 7.000 6.819 0.069 2 3 4 28.000 27.238 0.146 2 3 5 32.000 29.432 0.473 2 3 6 45.000 45.005 -0.001 2 4 1 8.000 12.130 -1.186 2 4 2 6.000 10.194 -1.314 2 4 3 14.000 11.347 0.788 2 4 4 19.000 14.242 1.261 2 4 5 23.000 22.605 0.083 2 4 6 22.000 26.346 -0.847 2 5 1 4.000 7.180 -1.187 2 5 2 5.000 6.162 -0.468 2 5 3 7.000 6.819 0.069 2 5 4 6.000 8.125 -0.746 2 5 5 18.000 13.121 1.347 2 5 6 18.000 15.107 0.744 2 6 1 0.000 2.032 -1.426 2 6 2 1.000 1.365 -0.312 2 6 3 2.000 1.617 0.301 2 6 4 3.000 3.543 -0.289 2 6 5 5.000 4.674 0.151 2 6 6 8.000 6.208 0.719 3 1 1 4.000 3.719 0.145 3 1 2 1.000 1.740 -0.561 3 1 3 0.000 0.749 -0.865 3 1 4 0.000 0.343 -0.586 3 1 5 0.000 0.067 -0.259 3 1 6 0.000 0.004 -0.062 3 2 1 5.000 6.698 -0.656 3 2 2 3.000 2.548 0.283 3 2 3 2.000 1.162 0.778 3 2 4 1.000 0.890 0.117 3 2 5 0.000 0.147 -0.383 3 2 6 0.000 0.010 -0.098 3 3 1 8.000 11.787 -1.103 3 3 2 6.000 4.896 0.499 3 3 3 3.000 2.178 0.557 3 3 4 1.000 1.341 -0.294 3 3 5 0.000 0.238 -0.488 3 3 6 0.000 0.015 -0.121 3 4 1 36.000 41.524 -0.857 3 4 2 25.000 27.478 -0.473 3 4 3 18.000 10.743 2.214 3 4 4 3.000 2.078 0.640 3 4 5 1.000 0.541 0.624 3 4 6 0.000 0.025 -0.160 3 5 1 83.000 86.764 -0.404 3 5 2 69.000 58.630 1.354 3 5 3 26.000 22.789 0.673 3 5 4 6.000 4.184 0.888 3 5 5 1.000 1.108 -0.103 3 5 6 0.000 0.052 -0.227 3 6 1 127.000 111.712 1.446 3 6 2 50.000 59.073 -1.181 3 6 3 12.000 24.580 -2.537 3 6 4 7.000 8.301 -0.452 3 6 5 2.000 1.796 0.152 3 6 6 0.000 0.096 -0.311 4 1 1 5.000 1.618 2.659 4 1 2 0.000 0.972 -0.986 4 1 3 1.000 1.251 -0.224 4 1 4 4.000 4.053 -0.026 4 1 5 4.000 5.254 -0.547 4 1 6 9.000 8.697 0.103 4 2 1 0.000 2.574 -1.605 4 2 2 1.000 1.258 -0.230 4 2 3 3.000 1.714 0.983 4 2 4 6.000 9.289 -1.079 4 2 5 9.000 10.150 -0.361 4 2 6 27.000 18.955 1.848 4 3 1 2.000 3.957 -0.984 4 3 2 3.000 2.111 0.612 4 3 3 2.000 2.807 -0.482 4 3 4 11.000 12.226 -0.351 4 3 5 27.000 14.366 3.333 4 3 6 20.000 25.488 -1.087 4 4 1 9.000 10.038 -0.327 4 4 2 12.000 8.530 1.188 4 4 3 11.000 9.966 0.327 4 4 4 10.000 13.642 -0.986 4 4 5 23.000 23.544 -0.112 4 4 6 31.000 31.839 -0.149 4 5 1 16.000 14.324 0.443 4 5 2 7.000 12.430 -1.540 4 5 3 5.000 14.439 -2.484 4 5 4 19.000 18.761 0.055 4 5 5 33.000 32.944 0.010 4 5 6 40.000 44.011 -0.605 4 6 1 21.000 11.665 2.733 4 6 2 14.000 7.922 2.160 4 6 3 13.000 9.850 1.004 4 6 4 20.000 23.544 -0.730 4 6 5 21.000 33.766 -2.197 4 6 6 61.000 52.042 1.242 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 1.5398 4.6636 S 1 -0.2928 0.7462 2 0.7440 2.1042 3 -1.0822 0.3388 4 0.6310 1.8796 X 1 -0.7187 0.4874 2 -0.2716 0.7622 3 0.0085 1.0086 4 0.4706 1.6010 5 0.4986 1.6465 6 0.0125 1.0126 Y 1 1.0685 2.9110 2 0.5206 1.6830 3 0.2080 1.2312 4 0.2054 1.2280 5 -0.3992 0.6708 6 -1.6033 0.2012 type 2 association (row=X column=Y) association -1.3876 row -0.1599 -0.5482 -0.3831 0.4910 0.5302 0.0700 adj row 0.1884 0.6458 0.4513 -0.5783 -0.6246 -0.0825 column -0.1600 -0.5440 -0.4373 0.5171 0.2001 0.4240 adj column -0.1885 -0.6408 -0.5151 0.6092 0.2357 0.4995 type 2 association (row=S column=X) association 3.4094 row -0.4334 -0.5310 0.6627 0.3018 adj row -0.8003 -0.9804 1.2236 0.5572 column -0.4560 -0.3554 -0.2454 0.0215 0.3315 0.7037 adj column -0.8419 -0.6562 -0.4531 0.0398 0.6120 1.2994 type 2 association (row=S column=Y) association 7.0081 row -0.4630 0.4757 -0.5351 0.5225 adj row -1.2258 1.2592 -1.4166 1.3832 column -0.4191 -0.3854 -0.2377 0.0263 0.2815 0.7344 adj column -1.1096 -1.0203 -0.6292 0.0697 0.7452 1.9442