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-linear models using 4 variables: (HL,LX,HY) * man 4 dim 2 2 6 6 lab L H X Y mod {L H X Y HL HY LX } 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 = 2 Converge criterion = 0.0000000000 X-squared = 353.0452 (0.0000) L-squared = 356.1965 (0.0000) Cressie-Read = 347.0909 (0.0000) Dissimilarity index = 0.1987 Degrees of freedom = 120 Log-likelihood = -6519.08182 Number of parameters = 23 (+1) Sample size = 1399.0 BIC(L-squared) = -513.0250 AIC(L-squared) = 116.1965 BIC(log-likelihood) = 13204.7644 AIC(log-likelihood) = 13084.1636 Eigenvalues information matrix 3078.7782 2464.5496 1399.0527 1384.9098 1224.9774 543.0971 389.1078 327.3057 299.1736 295.2327 281.2992 263.8528 245.8072 233.2206 230.9890 188.4350 159.7300 141.0533 134.3354 130.9762 130.8712 107.5940 101.1402 *** FREQUENCIES *** L H X Y observed estimated std. res. 1 1 1 1 44.000 21.461 4.865 1 1 1 2 4.000 20.815 -3.686 1 1 1 3 9.000 17.841 -2.093 1 1 1 4 7.000 16.549 -2.347 1 1 1 5 6.000 8.145 -0.752 1 1 1 6 7.000 5.559 0.611 1 1 2 1 13.000 24.544 -2.330 1 1 2 2 30.000 23.805 1.270 1 1 2 3 20.000 20.404 -0.089 1 1 2 4 8.000 18.926 -2.511 1 1 2 5 14.000 9.315 1.535 1 1 2 6 7.000 6.358 0.255 1 1 3 1 9.000 14.821 -1.512 1 1 3 2 23.000 14.375 2.275 1 1 3 3 17.000 12.321 1.333 1 1 3 4 17.000 11.429 1.648 1 1 3 5 3.000 5.625 -1.107 1 1 3 6 0.000 3.839 -1.959 1 1 4 1 16.000 14.347 0.436 1 1 4 2 17.000 13.915 0.827 1 1 4 3 10.000 11.927 -0.558 1 1 4 4 20.000 11.063 2.687 1 1 4 5 2.000 5.445 -1.476 1 1 4 6 2.000 3.716 -0.890 1 1 5 1 5.000 5.573 -0.243 1 1 5 2 4.000 5.405 -0.604 1 1 5 3 9.000 4.633 2.029 1 1 5 4 10.000 4.297 2.751 1 1 5 5 4.000 2.115 1.296 1 1 5 6 0.000 1.444 -1.201 1 1 6 1 3.000 2.134 0.593 1 1 6 2 3.000 2.070 0.646 1 1 6 3 0.000 1.774 -1.332 1 1 6 4 1.000 1.646 -0.503 1 1 6 5 4.000 0.810 3.544 1 1 6 6 1.000 0.553 0.601 1 2 1 1 7.000 6.085 0.371 1 2 1 2 4.000 8.416 -1.522 1 2 1 3 5.000 8.545 -1.213 1 2 1 4 5.000 11.911 -2.003 1 2 1 5 14.000 17.608 -0.860 1 2 1 6 69.000 38.064 5.014 1 2 2 1 5.000 6.959 -0.743 1 2 2 2 7.000 9.624 -0.846 1 2 2 3 13.000 9.773 1.032 1 2 2 4 15.000 13.622 0.373 1 2 2 5 38.000 20.137 3.981 1 2 2 6 37.000 43.532 -0.990 1 2 3 1 6.000 4.202 0.877 1 2 3 2 7.000 5.812 0.493 1 2 3 3 8.000 5.901 0.864 1 2 3 4 10.000 8.226 0.619 1 2 3 5 10.000 12.160 -0.619 1 2 3 6 15.000 26.288 -2.202 1 2 4 1 4.000 4.068 -0.034 1 2 4 2 12.000 5.626 2.687 1 2 4 3 5.000 5.712 -0.298 1 2 4 4 13.000 7.963 1.785 1 2 4 5 6.000 11.771 -1.682 1 2 4 6 14.000 25.446 -2.269 1 2 5 1 2.000 1.580 0.334 1 2 5 2 3.000 2.185 0.551 1 2 5 3 1.000 2.219 -0.818 1 2 5 4 1.000 3.093 -1.190 1 2 5 5 3.000 4.572 -0.735 1 2 5 6 5.000 9.884 -1.554 1 2 6 1 0.000 0.605 -0.778 1 2 6 2 0.000 0.837 -0.915 1 2 6 3 1.000 0.850 0.163 1 2 6 4 1.000 1.185 -0.170 1 2 6 5 1.000 1.751 -0.568 1 2 6 6 3.000 3.785 -0.404 2 1 1 1 8.000 7.718 0.101 2 1 1 2 2.000 7.486 -2.005 2 1 1 3 2.000 6.416 -1.743 2 1 1 4 1.000 5.951 -2.030 2 1 1 5 0.000 2.929 -1.711 2 1 1 6 4.000 1.999 1.415 2 1 2 1 5.000 7.837 -1.013 2 1 2 2 5.000 7.601 -0.943 2 1 2 3 5.000 6.515 -0.594 2 1 2 4 5.000 6.043 -0.424 2 1 2 5 5.000 2.974 1.175 2 1 2 6 3.000 2.030 0.681 2 1 3 1 8.000 8.074 -0.026 2 1 3 2 10.000 7.831 0.775 2 1 3 3 7.000 6.712 0.111 2 1 3 4 4.000 6.226 -0.892 2 1 3 5 1.000 3.064 -1.179 2 1 3 6 1.000 2.092 -0.755 2 1 4 1 12.000 15.080 -0.793 2 1 4 2 17.000 14.626 0.621 2 1 4 3 15.000 12.536 0.696 2 1 4 4 13.000 11.628 0.402 2 1 4 5 2.000 5.723 -1.556 2 1 4 6 2.000 3.906 -0.965 2 1 5 1 12.000 18.286 -1.470 2 1 5 2 17.000 17.735 -0.175 2 1 5 3 19.000 15.202 0.974 2 1 5 4 18.000 14.100 1.039 2 1 5 5 10.000 6.940 1.162 2 1 5 6 4.000 4.737 -0.339 2 1 6 1 31.000 26.123 0.954 2 1 6 2 29.000 25.336 0.728 2 1 6 3 25.000 21.717 0.705 2 1 6 4 24.000 20.143 0.859 2 1 6 5 12.000 9.914 0.662 2 1 6 6 12.000 6.767 2.012 2 2 1 1 4.000 2.182 1.231 2 2 1 2 1.000 3.018 -1.162 2 2 1 3 2.000 3.064 -0.608 2 2 1 4 0.000 4.271 -2.067 2 2 1 5 4.000 6.314 -0.921 2 2 1 6 37.000 13.650 6.320 2 2 2 1 0.000 2.216 -1.489 2 2 2 2 4.000 3.064 0.535 2 2 2 3 0.000 3.111 -1.764 2 2 2 4 1.000 4.337 -1.602 2 2 2 5 8.000 6.411 0.627 2 2 2 6 25.000 13.860 2.992 2 2 3 1 1.000 2.283 -0.849 2 2 3 2 3.000 3.157 -0.088 2 2 3 3 3.000 3.206 -0.115 2 2 3 4 7.000 4.469 1.198 2 2 3 5 8.000 6.606 0.542 2 2 3 6 15.000 14.280 0.191 2 2 4 1 4.000 4.264 -0.128 2 2 4 2 4.000 5.896 -0.781 2 2 4 3 8.000 5.987 0.823 2 2 4 4 17.000 8.346 2.996 2 2 4 5 12.000 12.337 -0.096 2 2 4 6 21.000 26.670 -1.098 2 2 5 1 3.000 5.170 -0.954 2 2 5 2 12.000 7.150 1.814 2 2 5 3 8.000 7.260 0.275 2 2 5 4 11.000 10.120 0.277 2 2 5 5 20.000 14.960 1.303 2 2 5 6 20.000 32.340 -2.170 2 2 6 1 11.000 7.386 1.330 2 2 6 2 8.000 10.214 -0.693 2 2 6 3 12.000 10.371 0.506 2 2 6 4 11.000 14.457 -0.909 2 2 6 5 12.000 21.371 -2.027 2 2 6 6 33.000 46.200 -1.942 *** LOG-LINEAR PARAMETERS *** * TABLE LHXY [or P(LHXY)] * effect beta std err z-value exp(beta) Wald df prob main 1.9201 6.8217 L 1 -0.0802 0.0341 -2.351 0.9229 2 0.0802 1.0835 5.53 1 0.019 H 1 0.0454 0.0312 1.457 1.0464 2 -0.0454 0.9556 2.12 1 0.145 X 1 0.1236 0.0682 1.813 1.1316 2 0.1983 0.0670 2.959 1.2194 3 -0.0389 0.0703 -0.554 0.9618 4 0.2571 0.0621 4.142 1.2932 5 -0.1193 0.0761 -1.568 0.8875 6 -0.4208 0.6565 35.80 5 0.000 Y 1 -0.1197 0.0743 -1.611 0.8872 2 0.0271 0.0676 0.401 1.0275 3 -0.0423 0.0686 -0.617 0.9586 4 0.0861 0.0639 1.348 1.0900 5 -0.0729 0.0696 -1.047 0.9297 6 0.1216 1.1293 7.26 5 0.202 LH 1 1 -0.0007 0.0267 -0.027 0.9993 1 2 0.0007 1.0007 2 1 0.0007 1.0007 2 2 -0.0007 0.9993 0.00 1 0.979 LX 1 1 0.5923 0.0682 8.686 1.8081 1 2 0.6517 0.0670 9.721 1.9189 1 3 0.3846 0.0703 5.467 1.4690 1 4 0.0560 0.0621 0.902 1.0576 1 5 -0.5132 0.0761 -6.743 0.5986 1 6 -1.1714 0.3099 2 1 -0.5923 0.5531 2 2 -0.6517 0.5211 2 3 -0.3846 0.6807 2 4 -0.0560 0.9455 2 5 0.5132 1.6706 2 6 1.1714 3.2266 278.87 5 0.000 HY 1 1 0.5855 0.0743 7.880 1.7959 1 2 0.4081 0.0676 6.036 1.5040 1 3 0.3234 0.0686 4.715 1.3818 1 4 0.1197 0.0639 1.873 1.1272 1 5 -0.4302 0.0696 -6.182 0.6504 1 6 -1.0066 0.3655 2 1 -0.5855 0.5568 2 2 -0.4081 0.6649 2 3 -0.3234 0.7237 2 4 -0.1197 0.8872 2 5 0.4302 1.5375 2 6 1.0066 2.7363 286.34 5 0.000