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 A: 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 [ 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 = 30 Converge criterion = 0.0000008942 X-squared = 138.3034 (0.0225) L-squared = 152.4776 (0.0026) Cressie-Read = 139.9256 (0.0179) Dissimilarity index = 0.1121 Degrees of freedom = 107 Log-likelihood = -6417.22234 Number of parameters = 36 (+1) Sample size = 1399.0 BIC(L-squared) = -622.5783 AIC(L-squared) = -61.5224 BIC(log-likelihood) = 13095.2111 AIC(log-likelihood) = 12906.4447 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 44.000 35.195 1.484 1 1 2 4.000 6.533 -0.991 1 1 3 9.000 8.731 0.091 1 1 4 7.000 4.067 1.455 1 1 5 6.000 6.108 -0.044 1 1 6 7.000 8.929 -0.646 1 2 1 13.000 25.542 -2.482 1 2 2 30.000 22.262 1.640 1 2 3 20.000 20.002 -0.001 1 2 4 8.000 16.463 -2.086 1 2 5 14.000 8.854 1.729 1 2 6 7.000 5.094 0.845 1 3 1 9.000 11.748 -0.802 1 3 2 23.000 18.912 0.940 1 3 3 17.000 14.516 0.652 1 3 4 17.000 14.974 0.524 1 3 5 3.000 5.359 -1.019 1 3 6 0.000 2.130 -1.459 1 4 1 16.000 11.953 1.170 1 4 2 17.000 20.128 -0.697 1 4 3 10.000 15.272 -1.349 1 4 4 20.000 16.016 0.995 1 4 5 2.000 5.563 -1.511 1 4 6 2.000 2.152 -0.103 1 5 1 5.000 5.472 -0.202 1 5 2 4.000 8.456 -1.532 1 5 3 9.000 6.559 0.953 1 5 4 10.000 6.665 1.292 1 5 5 4.000 2.451 0.990 1 5 6 0.000 0.998 -0.999 1 6 1 3.000 3.038 -0.022 1 6 2 3.000 2.731 0.163 1 6 3 0.000 2.435 -1.560 1 6 4 1.000 2.027 -0.721 1 6 5 4.000 1.068 2.838 1 6 6 1.000 0.603 0.511 2 1 1 7.000 11.585 -1.347 2 1 2 4.000 3.137 0.487 2 1 3 5.000 5.024 -0.011 2 1 4 5.000 3.485 0.812 2 1 5 14.000 15.154 -0.296 2 1 6 69.000 71.738 -0.323 2 2 1 5.000 8.550 -1.214 2 2 2 7.000 10.872 -1.174 2 2 3 13.000 11.707 0.378 2 2 4 15.000 14.346 0.173 2 2 5 38.000 22.340 3.313 2 2 6 37.000 41.619 -0.716 2 3 1 6.000 3.414 1.399 2 3 2 7.000 8.018 -0.360 2 3 3 8.000 7.376 0.230 2 3 4 10.000 11.329 -0.395 2 3 5 10.000 11.739 -0.507 2 3 6 15.000 15.107 -0.027 2 4 1 4.000 2.986 0.587 2 4 2 12.000 7.335 1.722 2 4 3 5.000 6.670 -0.646 2 4 4 13.000 10.415 0.801 2 4 5 6.000 10.474 -1.382 2 4 6 14.000 13.119 0.243 2 5 1 2.000 1.042 0.938 2 5 2 3.000 2.350 0.424 2 5 3 1.000 2.184 -0.801 2 5 4 1.000 3.305 -1.268 2 5 5 3.000 3.519 -0.277 2 5 6 5.000 4.641 0.167 2 6 1 0.000 0.419 -0.647 2 6 2 0.000 0.550 -0.741 2 6 3 1.000 0.587 0.539 2 6 4 1.000 0.728 0.319 2 6 5 1.000 1.110 -0.105 2 6 6 3.000 2.030 0.681 3 1 1 8.000 8.516 -0.177 3 1 2 2.000 1.663 0.261 3 1 3 2.000 2.278 -0.184 3 1 4 1.000 1.119 -0.113 3 1 5 0.000 1.941 -1.393 3 1 6 4.000 3.324 0.371 3 2 1 5.000 5.753 -0.314 3 2 2 5.000 5.276 -0.120 3 2 3 5.000 4.858 0.065 3 2 4 5.000 4.219 0.380 3 2 5 5.000 2.619 1.472 3 2 6 3.000 1.765 0.929 3 3 1 8.000 4.836 1.439 3 3 2 10.000 8.191 0.632 3 3 3 7.000 6.442 0.220 3 3 4 4.000 7.012 -1.137 3 3 5 1.000 2.896 -1.114 3 3 6 1.000 1.349 -0.300 3 4 1 12.000 9.383 0.854 3 4 2 17.000 16.626 0.092 3 4 3 15.000 12.927 0.577 3 4 4 13.000 14.305 -0.345 3 4 5 2.000 5.734 -1.559 3 4 6 2.000 2.599 -0.371 3 5 1 12.000 13.649 -0.446 3 5 2 17.000 22.194 -1.103 3 5 3 19.000 17.640 0.324 3 5 4 18.000 18.914 -0.210 3 5 5 10.000 8.027 0.696 3 5 6 4.000 3.831 0.086 3 6 1 31.000 30.003 0.182 3 6 2 29.000 28.390 0.115 3 6 3 25.000 25.929 -0.182 3 6 4 24.000 22.777 0.256 3 6 5 12.000 13.849 -0.497 3 6 6 12.000 9.162 0.938 4 1 1 4.000 3.635 0.191 4 1 2 1.000 1.033 -0.032 4 1 3 2.000 1.692 0.237 4 1 4 0.000 1.234 -1.111 4 1 5 4.000 6.141 -0.864 4 1 6 37.000 33.737 0.562 4 2 1 0.000 2.496 -1.580 4 2 2 4.000 3.330 0.367 4 2 3 0.000 3.669 -1.915 4 2 4 1.000 4.729 -1.715 4 2 5 8.000 8.424 -0.146 4 2 6 25.000 18.212 1.591 4 3 1 1.000 1.828 -0.612 4 3 2 3.000 4.503 -0.708 4 3 3 3.000 4.238 -0.602 4 3 4 7.000 6.847 0.059 4 3 5 8.000 8.117 -0.041 4 3 6 15.000 12.121 0.827 4 4 1 4.000 3.060 0.537 4 4 2 4.000 7.886 -1.384 4 4 3 8.000 7.336 0.245 4 4 4 17.000 12.049 1.426 4 4 5 12.000 13.863 -0.500 4 4 6 21.000 20.149 0.190 4 5 1 3.000 3.416 -0.225 4 5 2 12.000 8.080 1.379 4 5 3 8.000 7.685 0.114 4 5 4 11.000 12.228 -0.351 4 5 5 20.000 14.895 1.323 4 5 6 20.000 22.799 -0.586 4 6 1 11.000 5.481 2.357 4 6 2 8.000 7.543 0.166 4 6 3 12.000 8.244 1.308 4 6 4 11.000 10.748 0.077 4 6 5 12.000 18.756 -1.560 4 6 6 33.000 39.793 -1.077 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 1.8341 6.2595 S 1 0.0386 1.0394 2 -0.1880 0.8287 3 0.0592 1.0610 4 0.0901 1.0943 X 1 -0.1967 0.8214 2 0.2662 1.3051 3 0.0430 1.0439 4 0.3319 1.3936 5 -0.0503 0.9509 6 -0.3941 0.6743 Y 1 -0.1164 0.8901 2 0.0146 1.0147 3 -0.0042 0.9958 4 0.0463 1.0474 5 -0.0093 0.9907 6 0.0691 1.0715 type 2 association (row=X column=Y) association -2.1874 row -0.8545 -0.0293 0.2980 0.3221 0.2763 -0.0126 adj row 1.2637 0.0433 -0.4408 -0.4763 -0.4086 0.0187 column 0.4493 -0.4075 -0.1876 -0.5030 0.0660 0.5827 adj column 0.6646 -0.6027 -0.2774 -0.7439 0.0976 0.8618 type 2 association (row=S column=X) association 3.4208 row -0.3733 -0.6011 0.5983 0.3760 adj row -0.6904 -1.1117 1.1066 0.6954 column -0.3851 -0.4066 -0.2252 -0.0310 0.3169 0.7310 adj column -0.7122 -0.7520 -0.4165 -0.0573 0.5861 1.3519 type 2 association (row=S column=Y) association 2.7021 row -0.5629 0.4327 -0.4286 0.5588 adj row -0.9253 0.7113 -0.7046 0.9185 column -0.4404 -0.3000 -0.2327 -0.0847 0.3105 0.7472 adj column -0.7239 -0.4931 -0.3825 -0.1392 0.5104 1.2283