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) * 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[ 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 = 0.0000009381 X-squared = 96.4654 (0.3276) L-squared = 104.0300 (0.1655) Cressie-Read = 96.0043 (0.3396) Dissimilarity index = 0.0844 Degrees of freedom = 91 Log-likelihood = -6392.99857 Number of parameters = 52 (+1) Sample size = 1399.0 BIC(L-squared) = -555.1296 AIC(L-squared) = -77.9700 BIC(log-likelihood) = 13162.6598 AIC(log-likelihood) = 12889.9971 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** S X Y observed estimated std. res. 1 1 1 44.000 38.883 0.821 1 1 2 4.000 6.033 -0.828 1 1 3 9.000 9.184 -0.061 1 1 4 7.000 5.570 0.606 1 1 5 6.000 5.189 0.356 1 1 6 7.000 8.744 -0.590 1 2 1 13.000 14.275 -0.338 1 2 2 30.000 25.374 0.918 1 2 3 20.000 19.500 0.113 1 2 4 8.000 12.499 -1.273 1 2 5 14.000 14.141 -0.037 1 2 6 7.000 5.419 0.679 1 3 1 9.000 13.182 -1.152 1 3 2 23.000 20.919 0.455 1 3 3 17.000 15.817 0.298 1 3 4 17.000 14.384 0.690 1 3 5 3.000 4.169 -0.572 1 3 6 0.000 1.996 -1.413 1 4 1 16.000 15.892 0.027 1 4 2 17.000 19.422 -0.550 1 4 3 10.000 13.670 -0.993 1 4 4 20.000 18.877 0.259 1 4 5 2.000 3.255 -0.696 1 4 6 2.000 1.921 0.057 1 5 1 5.000 5.134 -0.059 1 5 2 4.000 7.307 -1.223 1 5 3 9.000 6.920 0.791 1 5 4 10.000 6.181 1.536 1 5 5 4.000 2.786 0.727 1 5 6 0.000 0.715 -0.845 1 6 1 3.000 3.502 -0.268 1 6 2 3.000 2.701 0.182 1 6 3 0.000 2.365 -1.538 1 6 4 1.000 1.909 -0.658 1 6 5 4.000 0.746 3.767 1 6 6 1.000 0.422 0.890 2 1 1 7.000 12.475 -1.550 2 1 2 4.000 2.773 0.737 2 1 3 5.000 5.085 -0.038 2 1 4 5.000 4.517 0.227 2 1 5 14.000 12.272 0.493 2 1 6 69.000 70.069 -0.128 2 2 1 5.000 4.608 0.183 2 2 2 7.000 11.736 -1.382 2 2 3 13.000 10.863 0.648 2 2 4 15.000 10.198 1.504 2 2 5 38.000 33.644 0.751 2 2 6 37.000 43.689 -1.012 2 3 1 6.000 3.859 1.090 2 3 2 7.000 8.776 -0.600 2 3 3 8.000 7.992 0.003 2 3 4 10.000 10.645 -0.198 2 3 5 10.000 8.997 0.335 2 3 6 15.000 14.600 0.105 2 4 1 4.000 4.199 -0.097 2 4 2 12.000 7.352 1.714 2 4 3 5.000 6.233 -0.494 2 4 4 13.000 12.606 0.111 2 4 5 6.000 6.339 -0.135 2 4 6 14.000 12.679 0.371 2 5 1 2.000 1.134 0.813 2 5 2 3.000 2.313 0.451 2 5 3 1.000 2.639 -1.009 2 5 4 1.000 3.452 -1.320 2 5 5 3.000 4.538 -0.722 2 5 6 5.000 3.944 0.532 2 6 1 0.000 0.626 -0.791 2 6 2 0.000 0.692 -0.832 2 6 3 1.000 0.730 0.316 2 6 4 1.000 0.863 0.148 2 6 5 1.000 0.983 0.017 2 6 6 3.000 1.884 0.813 3 1 1 8.000 8.239 -0.083 3 1 2 2.000 1.385 0.522 3 1 3 2.000 2.199 -0.134 3 1 4 1.000 1.452 -0.375 3 1 5 0.000 1.719 -1.311 3 1 6 4.000 3.806 0.100 3 2 1 5.000 2.907 1.227 3 2 2 5.000 5.600 -0.254 3 2 3 5.000 4.487 0.242 3 2 4 5.000 3.132 1.055 3 2 5 5.000 4.502 0.235 3 2 6 3.000 2.267 0.487 3 3 1 8.000 5.074 1.299 3 3 2 10.000 8.727 0.431 3 3 3 7.000 6.878 0.046 3 3 4 4.000 6.813 -1.078 3 3 5 1.000 2.509 -0.952 3 3 6 1.000 1.579 -0.460 3 4 1 12.000 11.964 0.011 3 4 2 17.000 15.847 0.290 3 4 3 15.000 11.627 0.989 3 4 4 13.000 17.487 -1.073 3 4 5 2.000 3.831 -0.935 3 4 6 2.000 2.971 -0.563 3 5 1 12.000 12.406 -0.115 3 5 2 17.000 19.137 -0.488 3 5 3 19.000 18.894 0.024 3 5 4 18.000 18.380 -0.089 3 5 5 10.000 10.525 -0.162 3 5 6 4.000 3.547 0.240 3 6 1 31.000 33.789 -0.480 3 6 2 29.000 28.247 0.142 3 6 3 25.000 25.784 -0.154 3 6 4 24.000 22.668 0.280 3 6 5 12.000 11.256 0.222 3 6 6 12.000 8.362 1.258 4 1 1 4.000 3.404 0.323 4 1 2 1.000 0.808 0.214 4 1 3 2.000 1.533 0.377 4 1 4 0.000 1.460 -1.208 4 1 5 4.000 4.820 -0.374 4 1 6 37.000 34.381 0.447 4 2 1 0.000 1.210 -1.100 4 2 2 4.000 3.290 0.392 4 2 3 0.000 3.150 -1.775 4 2 4 1.000 3.170 -1.219 4 2 5 8.000 12.713 -1.322 4 2 6 25.000 20.625 0.963 4 3 1 1.000 1.885 -0.645 4 3 2 3.000 4.578 -0.737 4 3 3 3.000 4.313 -0.632 4 3 4 7.000 6.158 0.339 4 3 5 8.000 6.326 0.665 4 3 6 15.000 12.825 0.607 4 4 1 4.000 3.946 0.027 4 4 2 4.000 7.379 -1.244 4 4 3 8.000 6.471 0.601 4 4 4 17.000 14.031 0.793 4 4 5 12.000 8.575 1.169 4 4 6 21.000 21.428 -0.093 4 5 1 3.000 3.326 -0.179 4 5 2 12.000 7.243 1.768 4 5 3 8.000 8.547 -0.187 4 5 4 11.000 11.987 -0.285 4 5 5 20.000 19.151 0.194 4 5 6 20.000 20.794 -0.174 4 6 1 11.000 7.083 1.472 4 6 2 8.000 8.360 -0.124 4 6 3 12.000 9.121 0.953 4 6 4 11.000 11.560 -0.165 4 6 5 12.000 16.015 -1.003 4 6 6 33.000 38.332 -0.861 *** LOG-LINEAR PARAMETERS *** * TABLE SXY [or P(SXY)] * effect beta exp(beta) main 1.8175 6.1567 S 1 0.0127 1.0127 2 -0.1557 0.8558 3 0.0700 1.0725 4 0.0730 1.0758 X 1 -0.1907 0.8264 2 0.2127 1.2370 3 0.0514 1.0527 4 0.3235 1.3819 5 -0.0315 0.9690 6 -0.3654 0.6939 Y 1 -0.1113 0.8946 2 0.0163 1.0165 3 0.0180 1.0182 4 0.0735 1.0763 5 -0.0627 0.9392 6 0.0661 1.0684 XY 1 1 0.8640 2.3726 1 2 -0.9106 0.4023 1 3 -0.3802 0.6837 1 4 -0.7062 0.4935 1 5 0.0030 1.0030 1 6 1.1300 3.0957 2 1 -0.5580 0.5724 2 2 0.1059 1.1118 2 3 -0.0472 0.9539 2 4 -0.3179 0.7277 2 5 0.5855 1.7959 2 6 0.2316 1.2606 3 1 -0.2107 0.8100 3 2 0.3398 1.4047 3 3 0.1704 1.1858 3 4 0.2495 1.2834 3 5 -0.2090 0.8114 3 6 -0.3400 0.7118 4 1 -0.0159 0.9842 4 2 0.2734 1.3145 4 3 0.0324 1.0329 4 4 0.5291 1.6975 4 5 -0.4486 0.6385 4 6 -0.3705 0.6904 5 1 -0.3043 0.7376 5 2 0.1374 1.1473 5 3 0.1932 1.2131 5 4 0.2543 1.2895 5 5 0.2374 1.2679 5 6 -0.5180 0.5957 6 1 0.2249 1.2522 6 2 0.0540 1.0555 6 3 0.0314 1.0319 6 4 -0.0088 0.9912 6 5 -0.1683 0.8451 6 6 -0.1332 0.8753 type 2 association (row=S column=X) association 3.4107 row -0.4166 -0.5696 0.5818 0.4044 adj row -0.7694 -1.0519 1.0745 0.7468 column -0.3954 -0.4070 -0.2200 -0.0231 0.3194 0.7260 adj column -0.7301 -0.7516 -0.4064 -0.0426 0.5900 1.3408 type 2 association (row=S column=Y) association 2.7126 row -0.6013 0.3983 -0.3777 0.5806 adj row -0.9903 0.6561 -0.6220 0.9563 column -0.4332 -0.3006 -0.2320 -0.0912 0.3034 0.7535 adj column -0.7135 -0.4950 -0.3820 -0.1503 0.4998 1.2410