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 *** * Generated probabilities from 2PL with theta normal(0,1); * * a2= 1.7 b2= -1.05; * a3= .7 b3= -.35; * a4= 1.2 b4= .35; * a5= 1.7 b5= 1.05; * lat 1 man 4 dim 9 2 2 2 2 lab X A B C D mod X {wei(X)} A|X {A spe(A,1a,X,c,-1)} B|X {B spe(B,1a,X,c,-1)} C|X {C spe(C,1a,X,c,-1)} D|X {D spe(D,1a,X,c,-1)} sta wei(X) [0.000022345844 0.002789141321 0.049916406765 0.244097502895 0.406349206349 0.244097502895 0.049916406765 0.002789141321 0.000022345844] des [ -4.512745863399 -3.205429002856 -2.076847978677 -1.023255663789 0.000000000000 1.023255663789 2.076847978677 3.205429002856 4.512745863399 -4.512745863399 -3.205429002856 -2.076847978677 -1.023255663789 0.000000000000 1.023255663789 2.076847978677 3.205429002856 4.512745863399 -4.512745863399 -3.205429002856 -2.076847978677 -1.023255663789 0.000000000000 1.023255663789 2.076847978677 3.205429002856 4.512745863399 -4.512745863399 -3.205429002856 -2.076847978677 -1.023255663789 0.000000000000 1.023255663789 2.076847978677 3.205429002856 4.512745863399 ] nco dat gen4normal.dat *** STATISTICS *** Number of iterations = 145 Converge criterion = 0.0000009090 Seed random values = 4973 X-squared = 0.8552 (0.9968) L-squared = 0.8553 (0.9968) Cressie-Read = 0.8552 (0.9968) Dissimilarity index = 0.0004 Degrees of freedom = 7 Log-likelihood = -2511247.44644 Number of parameters = 8 (+1) Sample size = 1000000.2 BIC(L-squared) = -95.8533 AIC(L-squared) = -13.1447 BIC(log-likelihood) = 5022605.4170 AIC(log-likelihood) = 5022510.8929 Eigenvalues information matrix 8.76E+0005 7.50E+0005 6.26E+0005 3.33E+0005 3.07E+0005 1.71E+0005 68879.4013 41448.2193 *** FREQUENCIES *** A B C D observed estimated std. res. 1 1 1 1 137147.060 137197.809 -0.137 1 1 1 2 102958.650 102842.362 0.363 1 1 2 1 66209.509 66147.344 0.242 1 1 2 2 120662.420 120823.879 -0.465 1 2 1 1 47507.868 47483.749 0.111 1 2 1 2 60340.355 60425.086 -0.345 1 2 2 1 33473.271 33485.533 -0.067 1 2 2 2 100307.180 100202.283 0.331 2 1 1 1 12607.949 12588.033 0.178 2 1 1 2 32785.287 32811.175 -0.143 2 1 2 1 14775.889 14803.557 -0.227 2 1 2 2 91022.155 90954.812 0.223 2 2 1 1 7389.064 7400.399 -0.132 2 2 1 2 31631.731 31618.163 0.076 2 2 2 1 12283.259 12284.100 -0.008 2 2 2 2 128898.530 128931.894 -0.093 *** PSEUDO R-SQUARED MEASURES *** * P(A|X) * baseline fitted R-squared entropy 0.6352 0.4585 0.2781 qualitative variance 0.2216 0.1497 0.3244 classification error 0.3314 0.2053 0.3804 -2/N*log-likelihood 1.2703 0.9170 0.2781/0.2611 likelihood^(-2/N) 3.5620 2.5017 0.2977/0.4138 * P(B|X) * baseline fitted R-squared entropy 0.6809 0.6298 0.0750 qualitative variance 0.2439 0.2200 0.0980 classification error 0.4218 0.3506 0.1689 -2/N*log-likelihood 1.3618 1.2596 0.0750/0.0927 likelihood^(-2/N) 3.9030 3.5240 0.0971/0.1306 * P(C|X) * baseline fitted R-squared entropy 0.6840 0.5632 0.1765 qualitative variance 0.2454 0.1913 0.2206 classification error 0.4324 0.2897 0.3299 -2/N*log-likelihood 1.3679 1.1265 0.1765/0.1945 likelihood^(-2/N) 3.9273 3.0848 0.2145/0.2878 * P(D|X) * baseline fitted R-squared entropy 0.6352 0.4587 0.2778 qualitative variance 0.2216 0.1498 0.3240 classification error 0.3314 0.2055 0.3799 -2/N*log-likelihood 1.2703 0.9175 0.2778/0.2608 likelihood^(-2/N) 3.5620 2.5030 0.2973/0.4134 *** LOG-LINEAR PARAMETERS *** * TABLE XA [or P(A|X)] * effect beta std err z-value exp(beta) Wald df prob A 1 0.5251 0.0020 256.857 1.6906 2 -0.5251 0.5915 65975.35 1 0.000 spe(A,1a) [X 1] 1 0.8506 0.0041 205.657 2.3411 42294.70 1 0.000 * TABLE XB [or P(B|X)] * effect beta std err z-value exp(beta) Wald df prob B 1 0.1750 0.0011 154.407 1.1912 2 -0.1750 0.8395 23841.64 1 0.000 spe(B,1a) [X 1] 1 0.3497 0.0017 200.950 1.4187 40380.95 1 0.000 * TABLE XC [or P(C|X)] * effect beta std err z-value exp(beta) Wald df prob C 1 -0.1749 0.0013 -132.236 0.8395 2 0.1749 1.1911 17486.39 1 0.000 spe(C,1a) [X 1] 1 0.5991 0.0025 236.142 1.8206 55762.89 1 0.000 * TABLE XD [or P(D|X)] * effect beta std err z-value exp(beta) Wald df prob D 1 -0.5248 0.0020 -257.509 0.5917 2 0.5248 1.6900 66310.75 1 0.000 spe(D,1a) [X 1] 1 0.8496 0.0041 206.789 2.3388 42761.64 1 0.000 *** LATENT CLASS OUTPUT *** X 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 0.0000 0.0028 0.0499 0.2441 0.4063 0.2441 0.0499 0.0028 A 1 0.0013 0.0121 0.0771 0.3339 0.7408 0.9422 0.9899 0.9985 A 2 0.9987 0.9879 0.9229 0.6661 0.2592 0.0578 0.0101 0.0015 B 1 0.0570 0.1310 0.2492 0.4096 0.5866 0.7438 0.8585 0.9303 B 2 0.9430 0.8690 0.7508 0.5904 0.4134 0.2562 0.1415 0.0697 C 1 0.0031 0.0149 0.0553 0.1714 0.4134 0.7061 0.8946 0.9704 C 2 0.9969 0.9851 0.9447 0.8286 0.5866 0.2939 0.1054 0.0296 D 1 0.0002 0.0015 0.0102 0.0580 0.2593 0.6658 0.9227 0.9878 D 2 0.9998 0.9985 0.9898 0.9420 0.7407 0.3342 0.0773 0.0122 X 9 0.0000 A 1 0.9998 A 2 0.0002 B 1 0.9709 B 2 0.0291 C 1 0.9937 C 2 0.0063 D 1 0.9987 D 2 0.0013 E = 0.4279, lambda = 0.2793