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 RASCH with theta exp(1) * b2= -1.05; * b3= -.35; * b4= .35; * b5= 1.05; * b6= 1.75; * b7= .50; * b8= -.50; * b9= -.70; man 8 dim 2 2 2 2 2 2 2 2 lab A B C D E F G H mod { A B C D E F G H ass2(A,B,-,7a) ass2(A,C,-,7a) ass2(A,D,-,7a) ass2(A,E,-,7a) ass2(A,F,-,7a) ass2(A,G,-,7a) ass2(A,H,-,7a) ass2(B,C,-,7a) ass2(B,D,-,7a) ass2(B,E,-,7a) ass2(B,F,-,7a) ass2(B,G,-,7a) ass2(B,H,-,7a) ass2(C,D,-,7a) ass2(C,E,-,7a) ass2(C,F,-,7a) ass2(C,G,-,7a) ass2(C,H,-,7a) ass2(D,E,-,7a) ass2(D,F,-,7a) ass2(D,G,-,7a) ass2(D,H,-,7a) ass2(E,F,-,7a) ass2(E,G,-,7a) ass2(E,H,-,7a) ass2(F,G,-,7a) ass2(F,H,-,7a) ass2(G,H,-,7a) } } ass_equ [ 1 2 13 1 3 13 1 4 13 1 5 13 1 6 13 1 7 13 1 8 13 2 3 13 2 4 13 2 5 13 2 6 13 2 7 13 2 8 13 3 4 13 3 5 13 3 6 13 3 7 13 3 8 13 4 5 13 4 6 13 4 7 13 4 8 13 5 6 13 5 7 13 5 8 13 6 7 13 6 8 13 7 8 13 ] ass_res [ 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 0 0 3 ] ass_phi [ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ] nco dat gen8rasch_exp1.dat *** STATISTICS *** Number of iterations = 15 Converge criterion = 0.0000008466 X-squared = 23653.0557 (0.0000) L-squared = 24125.3211 (0.0000) Cressie-Read = 23752.2767 (0.0000) Dissimilarity index = 0.0665 Degrees of freedom = 246 Log-likelihood = -4403121.13733 Number of parameters = 9 (+1) Sample size = 1000000.0 BIC(L-squared) = 20726.7055 AIC(L-squared) = 23633.3211 BIC(log-likelihood) = 8806366.6143 AIC(log-likelihood) = 8806260.2747 WARNING: no information is provided on identification of parameters *** FREQUENCIES *** A B C D E F G H observed estimated std. res. 1 1 1 1 1 1 1 1 197.728 978.717 -24.964 1 1 1 1 1 1 1 2 118.708 256.718 -8.614 1 1 1 1 1 1 2 1 144.991 312.447 -9.474 1 1 1 1 1 1 2 2 90.495 111.390 -1.980 1 1 1 1 1 2 1 1 394.126 854.359 -15.746 1 1 1 1 1 2 1 2 245.992 304.587 -3.357 1 1 1 1 1 2 2 1 300.455 370.708 -3.649 1 1 1 1 1 2 2 2 198.304 179.628 1.393 1 1 1 1 2 1 1 1 1375.634 3064.799 -30.512 1 1 1 1 2 1 1 2 858.596 1092.629 -7.080 1 1 1 1 2 1 2 1 1048.692 1329.821 -7.709 1 1 1 1 2 1 2 2 692.149 644.370 1.882 1 1 1 1 2 2 1 1 2850.641 3636.275 -13.028 1 1 1 1 2 2 1 2 1881.457 1761.971 2.847 1 1 1 1 2 2 2 1 2298.016 2144.466 3.316 1 1 1 1 2 2 2 2 1650.617 1412.319 6.341 1 1 1 2 1 1 1 1 683.120 1498.720 -21.068 1 1 1 2 1 1 1 2 426.366 534.307 -4.670 1 1 1 2 1 1 2 1 520.765 650.297 -5.079 1 1 1 2 1 1 2 2 343.711 315.104 1.612 1 1 1 2 1 2 1 1 1415.586 1778.178 -8.599 1 1 1 2 1 2 1 2 934.304 861.623 2.476 1 1 1 2 1 2 2 1 1141.161 1048.667 2.856 1 1 1 2 1 2 2 2 819.672 690.639 4.910 1 1 1 2 2 1 1 1 4940.882 6378.766 -18.003 1 1 1 2 2 1 1 2 3261.040 3090.856 3.061 1 1 1 2 2 1 2 1 3983.044 3761.830 3.607 1 1 1 2 2 1 2 2 2860.937 2477.494 7.704 1 1 1 2 2 2 1 1 10827.036 10286.386 5.331 1 1 1 2 2 2 1 2 7776.833 6774.485 12.178 1 1 1 2 2 2 2 1 9498.646 8245.114 13.805 1 1 1 2 2 2 2 2 7813.727 7380.427 5.044 1 1 2 1 1 1 1 1 339.227 733.384 -14.555 1 1 2 1 1 1 1 2 211.727 261.458 -3.076 1 1 2 1 1 1 2 1 258.604 318.216 -3.342 1 1 2 1 1 1 2 2 170.682 154.193 1.328 1 1 2 1 1 2 1 1 702.959 870.134 -5.667 1 1 2 1 1 2 1 2 463.962 421.627 2.062 1 1 2 1 1 2 2 1 566.684 513.155 2.363 1 1 2 1 1 2 2 2 407.037 337.957 3.758 1 1 2 1 2 1 1 1 2453.569 3121.386 -11.953 1 1 2 1 2 1 1 2 1619.385 1512.480 2.749 1 1 2 1 2 1 2 1 1977.921 1840.814 3.196 1 1 2 1 2 1 2 2 1420.699 1212.337 5.984 1 1 2 1 2 2 1 1 5376.547 5033.540 4.835 1 1 2 1 2 2 1 2 3861.861 3315.027 9.498 1 1 2 1 2 2 2 1 4716.888 4034.664 10.740 1 1 2 1 2 2 2 2 3880.182 3611.538 4.470 1 1 2 2 1 1 1 1 1218.406 1526.391 -7.883 1 1 2 2 1 1 1 2 804.163 739.619 2.373 1 1 2 2 1 1 2 1 982.207 900.178 2.734 1 1 2 2 1 1 2 2 705.498 592.846 4.627 1 1 2 2 1 2 1 1 2669.914 2461.455 4.202 1 1 2 2 1 2 1 2 1917.744 1621.084 7.368 1 1 2 2 1 2 2 1 2342.337 1972.994 8.315 1 1 2 2 1 2 2 2 1926.841 1766.081 3.825 1 1 2 2 2 1 1 1 9318.916 8829.853 5.205 1 1 2 2 2 1 1 2 6693.583 5815.231 11.518 1 1 2 2 2 1 2 1 8175.560 7077.622 13.051 1 1 2 2 2 1 2 2 6725.337 6335.373 4.899 1 1 2 2 2 2 1 1 22223.477 19353.119 20.633 1 1 2 2 2 2 1 2 18281.362 17323.506 7.278 1 1 2 2 2 2 2 1 22328.906 21084.153 8.572 1 1 2 2 2 2 2 2 23118.943 25651.476 -15.812 1 2 1 1 1 1 1 1 168.455 362.503 -10.192 1 2 1 1 1 1 1 2 105.141 129.236 -2.120 1 2 1 1 1 1 2 1 128.419 157.291 -2.302 1 2 1 1 1 1 2 2 84.758 76.216 0.978 1 2 1 1 1 2 1 1 349.079 430.097 -3.907 1 2 1 1 1 2 1 2 230.396 208.405 1.523 1 2 1 1 1 2 2 1 281.407 253.647 1.743 1 2 1 1 1 2 2 2 202.129 167.049 2.714 1 2 1 1 2 1 1 1 1218.406 1542.867 -8.260 1 2 1 1 2 1 1 2 804.163 747.602 2.069 1 2 1 1 2 1 2 1 982.207 909.894 2.397 1 2 1 1 2 1 2 2 705.498 599.245 4.341 1 2 1 1 2 2 1 1 2669.914 2488.023 3.647 1 2 1 1 2 2 1 2 1917.744 1638.581 6.896 1 2 1 1 2 2 2 1 2342.337 1994.290 7.794 1 2 1 1 2 2 2 2 1926.841 1785.144 3.354 1 2 1 2 1 1 1 1 605.043 754.478 -5.440 1 2 1 2 1 1 1 2 399.335 365.585 1.765 1 2 1 2 1 1 2 1 487.749 444.948 2.029 1 2 1 2 1 1 2 2 350.340 293.037 3.347 1 2 1 2 1 2 1 1 1325.840 1216.670 3.130 1 2 1 2 1 2 1 2 952.323 801.284 5.336 1 2 1 2 1 2 2 1 1163.170 975.229 6.018 1 2 1 2 1 2 2 2 956.841 872.954 2.839 1 2 1 2 2 1 1 1 4627.637 4364.499 3.983 1 2 1 2 2 1 1 2 3323.935 2874.405 8.385 1 2 1 2 2 1 2 1 4059.863 3498.390 9.493 1 2 1 2 2 1 2 2 3339.704 3131.505 3.721 1 2 1 2 2 2 1 1 11035.852 9566.033 15.028 1 2 1 2 2 2 1 2 9078.256 8562.818 5.570 1 2 1 2 2 2 2 1 11088.206 10421.664 6.529 1 2 1 2 2 2 2 2 11480.527 12679.242 -10.646 1 2 2 1 1 1 1 1 300.455 369.196 -3.578 1 2 2 1 1 1 1 2 198.304 178.896 1.451 1 2 2 1 1 1 2 1 242.209 217.731 1.659 1 2 2 1 1 1 2 2 173.974 143.395 2.554 1 2 2 1 1 2 1 1 658.393 595.365 2.583 1 2 2 1 1 2 1 2 472.910 392.100 4.081 1 2 2 1 1 2 2 1 577.613 477.219 4.596 1 2 2 1 1 2 2 2 475.153 427.172 2.322 1 2 2 1 2 1 1 1 2298.016 2135.724 3.512 1 2 2 1 2 1 1 2 1650.617 1406.561 6.507 1 2 2 1 2 1 2 1 2016.068 1711.902 7.351 1 2 2 1 2 1 2 2 1658.448 1532.371 3.221 1 2 2 1 2 2 1 1 5480.242 4681.043 11.681 1 2 2 1 2 2 1 2 4508.128 4190.130 4.913 1 2 2 1 2 2 2 1 5506.240 5099.738 5.692 1 2 2 1 2 2 2 2 5701.061 6204.460 -6.391 1 2 2 2 1 1 1 1 1141.161 1044.392 2.994 1 2 2 2 1 1 1 2 819.672 687.824 5.027 1 2 2 2 1 1 2 1 1001.150 837.139 5.669 1 2 2 2 1 1 2 2 823.561 749.346 2.711 1 2 2 2 1 2 1 1 2721.408 2289.080 9.036 1 2 2 2 1 2 1 2 2238.670 2049.018 4.190 1 2 2 2 1 2 2 1 2734.318 2493.827 4.816 1 2 2 2 1 2 2 2 2831.063 3034.048 -3.685 1 2 2 2 2 1 1 1 9498.646 8211.502 14.204 1 2 2 2 2 1 1 2 7813.727 7350.340 5.405 1 2 2 2 2 1 2 1 9543.708 8945.977 6.320 1 2 2 2 2 1 2 2 9881.381 10883.886 -9.609 1 2 2 2 2 2 1 1 25942.487 24461.969 9.466 1 2 2 2 2 2 1 2 26860.379 29761.005 -16.814 1 2 2 2 2 2 2 1 32807.341 36221.628 -17.940 1 2 2 2 2 2 2 2 51140.991 59895.682 -35.772 2 1 1 1 1 1 1 1 83.652 182.955 -7.342 2 1 1 1 1 1 1 2 52.211 65.225 -1.611 2 1 1 1 1 1 2 1 63.771 79.385 -1.752 2 1 1 1 1 1 2 2 42.090 38.466 0.584 2 1 1 1 1 2 1 1 173.348 217.070 -2.968 2 1 1 1 1 2 1 2 114.412 105.182 0.900 2 1 1 1 1 2 2 1 139.743 128.015 1.036 2 1 1 1 1 2 2 2 100.374 84.309 1.750 2 1 1 1 2 1 1 1 605.043 778.683 -6.223 2 1 1 1 2 1 1 2 399.335 377.314 1.134 2 1 1 1 2 1 2 1 487.749 459.223 1.331 2 1 1 1 2 1 2 2 350.340 302.438 2.754 2 1 1 1 2 2 1 1 1325.840 1255.703 1.979 2 1 1 1 2 2 1 2 952.323 826.990 4.358 2 1 1 1 2 2 2 1 1163.170 1006.516 4.938 2 1 1 1 2 2 2 2 956.841 900.960 1.862 2 1 1 2 1 1 1 1 300.455 380.785 -4.117 2 1 1 2 1 1 1 2 198.304 184.511 1.015 2 1 1 2 1 1 2 1 242.209 224.565 1.177 2 1 1 2 1 1 2 2 173.974 147.896 2.144 2 1 1 2 1 2 1 1 658.393 614.052 1.789 2 1 1 2 1 2 1 2 472.910 404.407 3.406 2 1 1 2 1 2 2 1 577.613 492.197 3.850 2 1 1 2 1 2 2 2 475.153 440.579 1.647 2 1 1 2 2 1 1 1 2298.016 2202.759 2.030 2 1 1 2 2 1 1 2 1650.617 1450.710 5.249 2 1 1 2 2 1 2 1 2016.068 1765.635 5.960 2 1 1 2 2 1 2 2 1658.448 1580.468 1.962 2 1 1 2 2 2 1 1 5480.242 4827.969 9.387 2 1 1 2 2 2 1 2 4508.128 4321.647 2.837 2 1 1 2 2 2 2 1 5506.240 5259.805 3.398 2 1 1 2 2 2 2 2 5701.061 6399.202 -8.727 2 1 2 1 1 1 1 1 149.202 186.333 -2.720 2 1 2 1 1 1 1 2 98.475 90.288 0.862 2 1 2 1 1 1 2 1 120.278 109.889 0.991 2 1 2 1 1 1 2 2 86.393 72.371 1.648 2 1 2 1 1 2 1 1 326.948 300.480 1.527 2 1 2 1 1 2 1 2 234.840 197.893 2.626 2 1 2 1 1 2 2 1 286.834 240.852 2.963 2 1 2 1 1 2 2 2 235.954 215.593 1.387 2 1 2 1 2 1 1 1 1141.161 1077.898 1.927 2 1 2 1 2 1 1 2 819.672 709.890 4.120 2 1 2 1 2 1 2 1 1001.150 863.996 4.666 2 1 2 1 2 1 2 2 823.561 773.386 1.804 2 1 2 1 2 2 1 1 2721.408 2362.518 7.384 2 1 2 1 2 2 1 2 2238.670 2114.755 2.695 2 1 2 1 2 2 2 1 2734.318 2573.833 3.163 2 1 2 1 2 2 2 2 2831.063 3131.386 -5.367 2 1 2 2 1 1 1 1 566.684 527.104 1.724 2 1 2 2 1 1 1 2 407.037 347.144 3.215 2 1 2 2 1 1 2 1 497.156 422.503 3.632 2 1 2 2 1 1 2 2 408.968 378.194 1.582 2 1 2 2 1 2 1 1 1351.411 1155.297 5.770 2 1 2 2 1 2 1 2 1111.691 1034.138 2.412 2 1 2 2 1 2 2 1 1357.822 1258.632 2.796 2 1 2 2 1 2 2 2 1405.864 1531.281 -3.205 2 1 2 2 2 1 1 1 4716.888 4144.338 8.894 2 1 2 2 2 1 1 2 3880.182 3709.710 2.799 2 1 2 2 2 1 2 1 4739.265 4515.027 3.337 2 1 2 2 2 1 2 2 4906.949 5493.088 -7.908 2 1 2 2 2 2 1 1 12882.658 12345.935 4.830 2 1 2 2 2 2 1 2 13338.470 15020.354 -13.723 2 1 2 2 2 2 2 1 16291.643 18281.025 -14.714 2 1 2 2 2 2 2 2 25395.864 30229.299 -27.800 2 2 1 1 1 1 1 1 74.091 92.102 -1.877 2 2 1 1 1 1 1 2 48.901 44.629 0.640 2 2 1 1 1 1 2 1 59.728 54.317 0.734 2 2 1 1 1 1 2 2 42.901 35.772 1.192 2 2 1 1 1 2 1 1 162.358 148.524 1.135 2 2 1 1 1 2 1 2 116.618 97.816 1.901 2 2 1 1 1 2 2 1 142.438 119.050 2.143 2 2 1 1 1 2 2 2 117.171 106.565 1.027 2 2 1 1 2 1 1 1 566.684 532.793 1.468 2 2 1 1 2 1 1 2 407.037 350.891 2.997 2 2 1 1 2 1 2 1 497.156 427.064 3.392 2 2 1 1 2 1 2 2 408.968 382.276 1.365 2 2 1 1 2 2 1 1 1351.411 1167.767 5.374 2 2 1 1 2 2 1 2 1111.691 1045.300 2.053 2 2 1 1 2 2 2 1 1357.822 1272.217 2.400 2 2 1 1 2 2 2 2 1405.864 1547.810 -3.608 2 2 1 2 1 1 1 1 281.407 260.542 1.293 2 2 1 2 1 1 1 2 202.129 171.589 2.331 2 2 1 2 1 1 2 1 246.881 208.839 2.632 2 2 1 2 1 1 2 2 203.088 186.937 1.181 2 2 1 2 1 2 1 1 671.091 571.051 4.186 2 2 1 2 1 2 1 2 552.049 511.163 1.808 2 2 1 2 1 2 2 1 674.275 622.128 2.091 2 2 1 2 1 2 2 2 698.132 756.896 -2.136 2 2 1 2 2 1 1 1 2342.337 2048.500 6.492 2 2 1 2 2 1 1 2 1926.841 1833.669 2.176 2 2 1 2 2 1 2 1 2353.449 2231.728 2.577 2 2 1 2 2 1 2 2 2436.719 2715.173 -5.344 2 2 1 2 2 2 1 1 6397.339 6102.459 3.775 2 2 1 2 2 2 1 2 6623.688 7424.395 -9.293 2 2 1 2 2 2 2 1 8090.191 9036.109 -9.951 2 2 1 2 2 2 2 2 12611.213 14942.009 -19.068 2 2 2 1 1 1 1 1 139.743 127.493 1.085 2 2 2 1 1 1 1 2 100.374 83.966 1.791 2 2 2 1 1 1 2 1 122.597 102.193 2.018 2 2 2 1 1 1 2 2 100.850 91.476 0.980 2 2 2 1 1 2 1 1 333.254 279.438 3.219 2 2 2 1 1 2 1 2 274.140 250.132 1.518 2 2 2 1 1 2 2 1 334.835 304.432 1.742 2 2 2 1 1 2 2 2 346.682 370.379 -1.231 2 2 2 1 2 1 1 1 1163.170 1002.413 5.077 2 2 2 1 2 1 1 2 956.841 897.288 1.988 2 2 2 1 2 1 2 1 1168.688 1092.074 2.318 2 2 2 1 2 1 2 2 1210.039 1328.643 -3.254 2 2 2 1 2 2 1 1 3176.824 2986.178 3.489 2 2 2 1 2 2 1 2 3289.226 3633.054 -5.704 2 2 2 1 2 2 2 1 4017.470 4421.730 -6.079 2 2 2 1 2 2 2 2 6262.543 7311.723 -12.270 2 2 2 2 1 1 1 1 577.613 490.191 3.949 2 2 2 2 1 1 1 2 475.153 438.783 1.736 2 2 2 2 1 1 2 1 580.353 534.036 2.004 2 2 2 2 1 1 2 2 600.887 649.721 -1.916 2 2 2 2 1 2 1 1 1577.564 1460.273 3.069 2 2 2 2 1 2 1 2 1633.381 1776.602 -3.398 2 2 2 2 1 2 2 1 1995.016 2162.273 -3.597 2 2 2 2 1 2 2 2 3109.887 3575.511 -7.787 2 2 2 2 2 1 1 1 5506.240 5238.363 3.701 2 2 2 2 2 1 1 2 5701.061 6373.115 -8.418 2 2 2 2 2 1 2 1 6963.292 7756.614 -9.008 2 2 2 2 2 1 2 2 10854.572 12826.250 -17.409 2 2 2 2 2 2 1 1 18928.189 21209.761 -15.666 2 2 2 2 2 2 1 2 29505.785 35072.225 -29.723 2 2 2 2 2 2 2 1 36038.447 42685.826 -32.174 2 2 2 2 2 2 2 2 123173.480 95936.220 87.937 *** PSEUDO R-SQUARED MEASURES *** * P() * baseline fitted R-squared entropy 0.0000 0.0000 0.0000 qualitative variance 0.0000 0.0000 0.0000 classification error 0.0000 0.0000 0.0000 -2/N*log-likelihood 0.0000 -0.0000 0.0000/0.0000 likelihood^(-2/N) 1.0000 1.0000 0.0000/0.0000 *** LOG-LINEAR PARAMETERS *** * TABLE ABCDEFGH [or P(ABCDEFGH)] * effect beta exp(beta) main 7.0308 1.13E+0003 A 1 0.3015 1.3519 2 -0.3015 0.7397 B 1 -0.0404 0.9604 2 0.0404 1.0412 C 1 -0.3927 0.6752 2 0.3927 1.4810 D 1 -0.7501 0.4723 2 0.7501 2.1172 E 1 -1.1078 0.3303 2 1.1078 3.0276 F 1 -0.4691 0.6256 2 0.4691 1.5985 G 1 0.0339 1.0345 2 -0.0339 0.9667 H 1 0.1321 1.1412 2 -0.1321 0.8762 type 2 association (row=A column=B slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=A column=C slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=A column=D slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=A column=E slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=A column=F slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=A column=G slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=A column=H slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=B column=C slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=B column=D slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=B column=E slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=B column=F slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=B column=G slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=B column=H slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=C column=D slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=C column=E slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=C column=F slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=C column=G slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=C column=H slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=D column=E slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=D column=F slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=D column=G slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=D column=H slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=E column=F slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=E column=G slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=E column=H slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=F column=G slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=F column=H slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 type 2 association (row=G column=H slab=) association 1.0000 row -0.7071 0.7071 adj row -0.7071 0.7071 column -0.7071 0.7071 adj column -0.7071 0.7071 slab 0.1534 adj slab 0.1534 * TABLE [or P()] * effect beta exp(beta)