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 *** *data generated from rasch theta exp1 * Rasch MMLE man 4 lat 1 dim 9 2 2 2 2 lab X A B C D mod X {wei(X)} A|X {A} B|X {B} C|X {C} D|X {D} all {spe(XA,XB,XC,XD,1b)} nco data gen4rasch_exp1.dat *** STATISTICS *** Number of iterations = 59 Converge criterion = 0.0000009751 Seed random values = 2935 X-squared = 1920.0022 (0.0000) L-squared = 1940.6381 (0.0000) Cressie-Read = 1926.6171 (0.0000) Dissimilarity index = 0.0189 Degrees of freedom = 10 Log-likelihood = -2296335.80378 Number of parameters = 5 (+1) Sample size = 1000000.0 BIC(L-squared) = 1802.4830 AIC(L-squared) = 1920.6381 BIC(log-likelihood) = 4592740.6851 AIC(log-likelihood) = 4592681.6076 Eigenvalues information matrix 1.84E+0006 8.38E+0005 7.46E+0005 5.77E+0005 3.34E+0005 *** FREQUENCIES *** A B C D observed estimated std. res. 1 1 1 1 14346.602 16947.051 -19.976 1 1 1 2 57246.831 57420.200 -0.724 1 1 2 1 28427.935 28217.364 1.254 1 1 2 2 129433.190 123658.587 16.421 1 2 1 1 14116.895 13924.114 1.634 1 2 1 2 64274.622 61020.451 13.174 1 2 2 1 31917.833 29986.595 11.152 1 2 2 2 187799.660 195967.987 -18.452 2 1 1 1 7010.242 6980.313 0.358 2 1 1 2 31917.833 30590.229 7.591 2 1 2 1 15849.927 15032.613 6.666 2 1 2 2 93258.553 98240.925 -15.896 2 2 1 1 7870.841 7417.979 5.258 2 2 1 2 46310.827 48477.875 -9.842 2 2 2 1 22997.276 23822.938 -5.349 2 2 2 2 247220.930 242294.776 10.008 *** PSEUDO R-SQUARED MEASURES *** * P(A|X) * baseline fitted R-squared entropy 0.6916 0.6308 0.0879 qualitative variance 0.2492 0.2200 0.1174 classification error 0.4724 0.3477 0.2640 -2/N*log-likelihood 1.3833 1.2586 0.0901/0.1109 likelihood^(-2/N) 3.9879 3.5203 0.1172/0.1565 * P(B|X) * baseline fitted R-squared entropy 0.6628 0.6056 0.0863 qualitative variance 0.2350 0.2089 0.1109 classification error 0.3775 0.3297 0.1265 -2/N*log-likelihood 1.3256 1.2107 0.0867/0.1031 likelihood^(-2/N) 3.7646 3.3558 0.1086/0.1479 * P(C|X) * baseline fitted R-squared entropy 0.5546 0.5102 0.0800 qualitative variance 0.1840 0.1679 0.0875 classification error 0.2431 0.2431 -0.0000 -2/N*log-likelihood 1.1092 1.0220 0.0786/0.0802 likelihood^(-2/N) 3.0321 2.7788 0.0835/0.1247 * P(D|X) * baseline fitted R-squared entropy 0.4095 0.3804 0.0712 qualitative variance 0.1222 0.1150 0.0587 classification error 0.1425 0.1425 -0.0000 -2/N*log-likelihood 0.8191 0.7629 0.0686/0.0532 likelihood^(-2/N) 2.2684 2.1445 0.0546/0.0977 *** LOG-LINEAR PARAMETERS *** * TABLE XA [or P(A|X)] * effect beta std err z-value exp(beta) Wald df prob A 1 0.0616 0.0011 54.284 1.0635 2 -0.0616 0.9403 2946.72 1 0.000 * TABLE XB [or P(B|X)] * effect beta std err z-value exp(beta) Wald df prob B 1 -0.2837 0.0012 -240.737 0.7530 2 0.2837 1.3280 57954.27 1 0.000 * TABLE XC [or P(C|X)] * effect beta std err z-value exp(beta) Wald df prob C 1 -0.6368 0.0013 -475.130 0.5290 2 0.6368 1.8905 225748.11 1 0.000 * TABLE XD [or P(D|X)] * effect beta std err z-value exp(beta) Wald df prob D 1 -0.9921 0.0016 -614.194 0.3708 2 0.9921 2.6968 377234.56 1 0.000 * ALL TABLES * effect beta std err z-value exp(beta) Wald df prob spe(XA,.,XD,1b) 1 -0.2863 0.0009 -335.993 0.7510 112891.08 1 0.000 *** LATENT CLASS OUTPUT *** X 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 0.1111 0.1111 0.1111 0.1111 0.1111 0.1111 0.1111 0.1111 A 1 0.2646 0.3239 0.3895 0.4593 0.5307 0.6010 0.6673 0.7275 A 2 0.7354 0.6761 0.6105 0.5407 0.4693 0.3990 0.3327 0.2725 B 1 0.1528 0.1937 0.2423 0.2987 0.3618 0.4302 0.5013 0.5724 B 2 0.8472 0.8063 0.7577 0.7013 0.6382 0.5698 0.4987 0.4276 C 1 0.0817 0.1060 0.1363 0.1736 0.2186 0.2714 0.3316 0.3978 C 2 0.9183 0.8940 0.8637 0.8264 0.7814 0.7286 0.6684 0.6022 D 1 0.0419 0.0550 0.0720 0.0936 0.1209 0.1547 0.1960 0.2450 D 2 0.9581 0.9450 0.9280 0.9064 0.8791 0.8453 0.8040 0.7550 X 9 0.1111 A 1 0.7805 A 2 0.2195 B 1 0.6406 B 2 0.3594 C 1 0.4679 C 2 0.5321 D 1 0.3018 D 2 0.6982 E = 0.8019, lambda = 0.0979