nag_correg_lmm_init_combine (g02jgc) Example Program Data U 90 12 12 :: weight,n,var,lvnames 2 3 2 3 2 3 1 4 5 2 3 3 :: levels F01 F02 F03 F04 F05 F06 F07 F08 F09 F10 F11 F12 :: vnames F01+F02 :: fixed 3 :: nrndm -1+F03+F04 / SUBJECT = F10.F11.F12 :: rndm1 -1+F05+F06 / SUBJECT = F11.F12 :: rndm2 -1+F07+F08+F09 / SUBJECT = F12 :: rndm3 ## Data 4 :: number of blocks of data ## 1st block of data 10 :: n 1.0 3.0 2.0 1.0 2.0 2.0 -0.3160 4.0 2.0 1.0 1.0 1.0 3.1100 1.0 1.0 1.0 3.0 1.0 2.0 -1.3377 1.0 4.0 1.0 1.0 1.0 2.8226 1.0 3.0 1.0 3.0 1.0 3.0 -0.7610 4.0 2.0 1.0 1.0 1.0 7.4543 2.0 3.0 2.0 1.0 1.0 3.0 -2.2976 4.0 2.0 1.0 1.0 1.0 4.4313 2.0 2.0 1.0 3.0 2.0 3.0 -0.4263 2.0 1.0 1.0 1.0 1.0 6.1543 2.0 1.0 2.0 3.0 1.0 3.0 1.4067 3.0 3.0 2.0 1.0 1.0 -0.1783 2.0 3.0 2.0 1.0 2.0 1.0 -1.4669 1.0 2.0 2.0 1.0 1.0 4.6748 1.0 1.0 1.0 3.0 2.0 3.0 0.4717 2.0 4.0 2.0 1.0 1.0 7.0667 1.0 3.0 2.0 3.0 2.0 1.0 0.4436 1.0 3.0 2.0 1.0 1.0 1.4262 1.0 1.0 1.0 2.0 2.0 3.0 -0.5950 3.0 4.0 2.0 1.0 1.0 7.7290 :: dat, y ## 2nd block of data 31 :: n 1.0 3.0 1.0 3.0 1.0 1.0 -1.7981 4.0 2.0 1.0 2.0 1.0 -2.1806 2.0 3.0 1.0 2.0 1.0 1.0 0.2397 1.0 4.0 1.0 2.0 1.0 6.8419 1.0 2.0 2.0 1.0 2.0 3.0 0.4742 1.0 1.0 1.0 2.0 1.0 1.2590 2.0 2.0 2.0 2.0 2.0 3.0 0.6888 3.0 1.0 1.0 2.0 1.0 8.8405 2.0 1.0 2.0 3.0 1.0 3.0 -1.0616 3.0 5.0 1.0 2.0 1.0 6.1657 1.0 2.0 2.0 2.0 2.0 1.0 -0.5356 1.0 3.0 2.0 2.0 1.0 -4.5605 1.0 3.0 2.0 2.0 1.0 1.0 -1.2963 2.0 5.0 2.0 2.0 1.0 -1.2367 1.0 2.0 2.0 1.0 2.0 2.0 -1.5389 3.0 2.0 2.0 2.0 1.0 -12.2932 2.0 3.0 1.0 1.0 2.0 2.0 -0.6408 2.0 1.0 2.0 2.0 1.0 -2.3374 1.0 2.0 2.0 2.0 1.0 1.0 0.6574 1.0 1.0 2.0 2.0 1.0 0.0716 2.0 1.0 1.0 1.0 1.0 3.0 0.9259 1.0 2.0 1.0 3.0 1.0 0.1895 2.0 2.0 2.0 1.0 2.0 2.0 1.5080 3.0 1.0 1.0 3.0 1.0 1.5608 2.0 3.0 1.0 1.0 1.0 3.0 2.5821 2.0 3.0 1.0 3.0 1.0 -0.8529 1.0 2.0 2.0 1.0 2.0 3.0 0.4102 1.0 4.0 1.0 3.0 1.0 -4.1169 2.0 1.0 2.0 3.0 2.0 2.0 0.7839 2.0 5.0 1.0 3.0 1.0 3.9977 1.0 2.0 2.0 3.0 2.0 1.0 -1.8812 4.0 2.0 2.0 3.0 1.0 -8.1277 1.0 2.0 1.0 3.0 2.0 3.0 0.7770 4.0 1.0 2.0 3.0 1.0 -4.9656 2.0 2.0 1.0 2.0 1.0 3.0 0.2590 3.0 1.0 2.0 3.0 1.0 -0.6428 2.0 3.0 2.0 2.0 2.0 3.0 -0.9250 3.0 3.0 2.0 3.0 1.0 -5.5152 2.0 2.0 1.0 3.0 2.0 3.0 -0.4831 1.0 5.0 2.0 3.0 1.0 -5.5657 2.0 2.0 1.0 3.0 1.0 3.0 0.5046 3.0 3.0 1.0 1.0 2.0 14.8177 2.0 1.0 1.0 2.0 2.0 1.0 -0.6903 2.0 1.0 1.0 1.0 2.0 16.9783 1.0 3.0 2.0 2.0 2.0 1.0 1.6166 2.0 5.0 1.0 1.0 2.0 13.8966 2.0 2.0 2.0 2.0 1.0 3.0 0.2778 2.0 3.0 1.0 1.0 2.0 14.8166 2.0 3.0 2.0 2.0 1.0 2.0 1.9586 4.0 2.0 1.0 1.0 2.0 19.3640 1.0 3.0 1.0 1.0 1.0 3.0 1.0506 2.0 5.0 2.0 1.0 2.0 9.5299 2.0 1.0 1.0 3.0 2.0 3.0 0.4871 1.0 1.0 2.0 1.0 2.0 12.0102 2.0 1.0 2.0 3.0 2.0 1.0 2.0891 4.0 4.0 2.0 1.0 2.0 6.1551 1.0 2.0 1.0 1.0 2.0 2.0 1.4338 4.0 3.0 2.0 1.0 2.0 -1.7048 1.0 1.0 2.0 3.0 1.0 2.0 -1.1196 3.0 4.0 2.0 1.0 2.0 2.7640 1.0 3.0 1.0 1.0 2.0 1.0 0.3367 3.0 2.0 1.0 2.0 2.0 2.8065 :: dat, y ## 3rd block of data 40 :: n 2.0 2.0 1.0 3.0 1.0 1.0 0.1092 2.0 2.0 1.0 2.0 2.0 0.0974 1.0 1.0 1.0 2.0 2.0 2.0 0.4007 4.0 1.0 1.0 2.0 2.0 -7.8080 2.0 3.0 1.0 1.0 1.0 2.0 0.1460 3.0 5.0 1.0 2.0 2.0 -18.0450 2.0 1.0 2.0 3.0 1.0 3.0 -0.3877 3.0 4.0 1.0 2.0 2.0 -2.8199 1.0 1.0 1.0 2.0 2.0 1.0 0.6957 4.0 3.0 2.0 2.0 2.0 8.9893 2.0 1.0 1.0 1.0 2.0 1.0 -0.4664 3.0 3.0 2.0 2.0 2.0 3.7978 1.0 1.0 1.0 1.0 2.0 3.0 0.2067 2.0 4.0 2.0 2.0 2.0 -6.3493 2.0 1.0 2.0 1.0 1.0 2.0 0.4112 1.0 4.0 2.0 2.0 2.0 8.1411 2.0 2.0 1.0 1.0 1.0 2.0 -1.3734 3.0 3.0 2.0 2.0 2.0 -7.5483 2.0 1.0 2.0 3.0 1.0 3.0 0.7065 1.0 3.0 1.0 3.0 2.0 -0.4600 1.0 2.0 2.0 2.0 1.0 2.0 1.3628 4.0 2.0 1.0 3.0 2.0 -3.2135 2.0 1.0 2.0 2.0 2.0 3.0 -0.5052 4.0 5.0 1.0 3.0 2.0 -6.6562 2.0 1.0 1.0 1.0 2.0 1.0 -1.3457 2.0 5.0 1.0 3.0 2.0 5.1267 1.0 1.0 2.0 1.0 2.0 3.0 -1.8022 3.0 4.0 1.0 3.0 2.0 3.5592 2.0 3.0 1.0 2.0 1.0 1.0 0.0116 2.0 4.0 2.0 3.0 2.0 -4.4420 2.0 2.0 1.0 3.0 2.0 3.0 -0.9075 1.0 3.0 2.0 3.0 2.0 -8.5965 2.0 2.0 2.0 2.0 2.0 3.0 -1.4707 1.0 1.0 2.0 3.0 2.0 -6.3187 2.0 2.0 1.0 1.0 2.0 1.0 -1.2938 2.0 3.0 2.0 3.0 2.0 -7.8953 1.0 3.0 1.0 3.0 2.0 2.0 -1.1660 4.0 4.0 2.0 3.0 2.0 -10.1383 1.0 2.0 1.0 1.0 2.0 3.0 0.0397 4.0 4.0 1.0 1.0 3.0 -7.8850 1.0 3.0 1.0 2.0 1.0 3.0 -0.5987 3.0 2.0 1.0 1.0 3.0 23.2001 2.0 3.0 2.0 2.0 1.0 1.0 0.6683 3.0 3.0 1.0 1.0 3.0 5.5829 2.0 2.0 1.0 1.0 2.0 2.0 -0.0106 1.0 3.0 1.0 1.0 3.0 -4.3698 1.0 2.0 1.0 3.0 2.0 2.0 0.5885 1.0 3.0 1.0 1.0 3.0 2.1274 1.0 1.0 1.0 1.0 1.0 2.0 0.4555 1.0 5.0 2.0 1.0 3.0 -2.7184 2.0 2.0 2.0 1.0 1.0 2.0 0.6502 4.0 3.0 2.0 1.0 3.0 -17.9128 1.0 1.0 1.0 3.0 1.0 1.0 -0.1601 1.0 3.0 2.0 1.0 3.0 -1.2708 2.0 2.0 1.0 3.0 2.0 3.0 1.6910 1.0 1.0 2.0 1.0 3.0 -24.2735 2.0 2.0 2.0 3.0 1.0 2.0 0.1053 4.0 4.0 2.0 1.0 3.0 -14.7374 2.0 1.0 2.0 3.0 2.0 2.0 -0.4037 3.0 4.0 1.0 2.0 3.0 0.1713 1.0 3.0 2.0 3.0 1.0 3.0 -0.5853 3.0 2.0 1.0 2.0 3.0 8.0006 2.0 3.0 2.0 1.0 1.0 1.0 -0.3037 1.0 3.0 1.0 2.0 3.0 1.2100 1.0 3.0 1.0 1.0 2.0 2.0 -0.0774 1.0 4.0 1.0 2.0 3.0 3.3307 2.0 3.0 1.0 2.0 2.0 1.0 0.4733 4.0 5.0 1.0 2.0 3.0 -22.6713 1.0 3.0 2.0 2.0 1.0 2.0 -0.0354 4.0 2.0 2.0 2.0 3.0 7.5562 1.0 3.0 2.0 2.0 1.0 1.0 -0.6640 2.0 1.0 2.0 2.0 3.0 -7.0694 2.0 3.0 1.0 3.0 1.0 1.0 0.0335 4.0 4.0 2.0 2.0 3.0 3.7159 1.0 2.0 2.0 2.0 1.0 3.0 0.1351 1.0 1.0 2.0 2.0 3.0 -4.3135 1.0 1.0 2.0 1.0 2.0 3.0 -0.5951 3.0 4.0 2.0 2.0 3.0 -14.5577 2.0 2.0 2.0 3.0 1.0 3.0 0.2735 3.0 2.0 1.0 3.0 3.0 -12.5107 :: dat, y ## 4th block of data 9 :: n 2.0 2.0 1.0 1.0 1.0 3.0 0.3157 1.0 2.0 1.0 3.0 3.0 4.7708 2.0 2.0 2.0 1.0 1.0 1.0 -1.0843 2.0 3.0 1.0 3.0 3.0 13.2797 1.0 2.0 2.0 1.0 2.0 2.0 -0.0836 4.0 2.0 1.0 3.0 3.0 -6.3243 2.0 1.0 2.0 1.0 1.0 2.0 -0.2884 2.0 1.0 1.0 3.0 3.0 -7.0549 2.0 3.0 2.0 3.0 2.0 3.0 -0.1006 1.0 2.0 2.0 3.0 3.0 -9.2713 1.0 3.0 1.0 2.0 2.0 3.0 0.5710 1.0 3.0 2.0 3.0 3.0 -18.7788 1.0 1.0 2.0 1.0 1.0 2.0 0.2776 2.0 3.0 2.0 3.0 3.0 -7.7230 2.0 3.0 2.0 2.0 1.0 3.0 -0.7561 4.0 4.0 2.0 3.0 3.0 -22.7230 1.0 2.0 2.0 2.0 1.0 2.0 1.5549 1.0 4.0 2.0 3.0 3.0 -11.6609 :: dat, y