Post by nuno on Jul 7, 2015 12:26:50 GMT
Hello,
I would like to leave a question:
I am running a bivariate gcta (one case-control and one continuous trait). The analysis seems to be 'stuck' in an eternal iterative loop, and always returning to the same logL value. See below part of the output below. From the 10th iteration onwards, this 'loop' starts.
Running AI-REML algorithm ...
Iter. logL V(G1)_tr1 V(G1)_tr2 C(G1)_tr12 V(G2)_tr1 V(G2)_tr2 C(G2)_tr12 V(e)_tr1 V(e)_tr2 C(e)_tr12
1 454.32 0.30098 0.01743 0.03680 0.28825 0.03839 0.02892 0.48883 0.05687 0.03708
2 587.15 0.27289 0.00818 0.03611 0.24170 0.03542 0.01347 0.52843 0.06421 0.03731
Note: to constrain the correlation being from -1 to 1, a genetic (or residual) variance-covariance matrix is bended to be positive definite.
3 655.76 0.25212 0.00537 0.03679 0.21058 0.03193 0.00340 0.56004 0.07062 0.03669
4 689.90 0.23557 0.00584 0.03708 0.18942 0.02895 -0.00304 0.58431 0.07596 0.03563
5 703.93 0.22368 0.00624 0.03736 0.17491 0.02669 -0.00722 0.60242 0.08024 0.03446
6 708.88 0.21496 0.00657 0.03757 0.16494 0.02505 -0.00995 0.61566 0.08352 0.03340
7 709.54 0.20852 0.00681 0.03769 0.15808 0.02391 -0.01175 0.62520 0.08594 0.03255
8 708.45 0.20337 0.00840 0.04133 0.14317 0.01597 -0.01557 0.64669 0.08594 0.03054 (1 component(s) constrained)
9 721.36 0.20259 0.00826 0.04092 0.14332 0.01719 -0.01546 0.64756 0.08645 0.03072 (1 component(s) constrained)
10 719.03 0.19988 0.00794 0.03984 0.14331 0.01866 -0.01546 0.64757 0.08791 0.03071
11 714.62 0.20226 0.00840 0.04123 0.14318 0.01670 -0.01550 0.64770 0.08606 0.03065 (1 component(s) constrained)
12 720.06 0.20256 0.00826 0.04091 0.14332 0.01713 -0.01542 0.64758 0.08634 0.03070 (1 component(s) constrained)
13 719.26 0.19988 0.00794 0.03984 0.14331 0.01861 -0.01543 0.64759 0.08784 0.03070
14 714.84 0.20226 0.00840 0.04122 0.14319 0.01672 -0.01550 0.64770 0.08607 0.03065 (1 component(s) constrained)
15 720.02 0.20256 0.00826 0.04092 0.14331 0.01712 -0.01542 0.64758 0.08634 0.03070 (1 component(s) constrained)
16 719.27 0.19988 0.00794 0.03984 0.14330 0.01861 -0.01543 0.64759 0.08784 0.03070
17 714.84 0.20226 0.00840 0.04122 0.14319 0.01672 -0.01550 0.64770 0.08607 0.03065 (1 component(s) constrained)
18 720.02 0.20256 0.00827 0.04092 0.14331 0.01712 -0.01542 0.64758 0.08634 0.03070 (1 component(s) constrained)
19 719.27 0.19988 0.00794 0.03984 0.14330 0.01861 -0.01543 0.64759 0.08784 0.03070
20 714.84 0.20226 0.00840 0.04122 0.14319 0.01672 -0.01550 0.64770 0.08607 0.03065 (1 component(s) constrained)
21 720.02 0.20256 0.00827 0.04092 0.14331 0.01712 -0.01542 0.64758 0.08634 0.03070 (1 component(s) constrained)
22 719.27 0.19988 0.00794 0.03984 0.14 ...
...
This continues for the length of the analysis. Anyone has suggestions for what might be wrong?
Thanks for the help
Nuno
I would like to leave a question:
I am running a bivariate gcta (one case-control and one continuous trait). The analysis seems to be 'stuck' in an eternal iterative loop, and always returning to the same logL value. See below part of the output below. From the 10th iteration onwards, this 'loop' starts.
Running AI-REML algorithm ...
Iter. logL V(G1)_tr1 V(G1)_tr2 C(G1)_tr12 V(G2)_tr1 V(G2)_tr2 C(G2)_tr12 V(e)_tr1 V(e)_tr2 C(e)_tr12
1 454.32 0.30098 0.01743 0.03680 0.28825 0.03839 0.02892 0.48883 0.05687 0.03708
2 587.15 0.27289 0.00818 0.03611 0.24170 0.03542 0.01347 0.52843 0.06421 0.03731
Note: to constrain the correlation being from -1 to 1, a genetic (or residual) variance-covariance matrix is bended to be positive definite.
3 655.76 0.25212 0.00537 0.03679 0.21058 0.03193 0.00340 0.56004 0.07062 0.03669
4 689.90 0.23557 0.00584 0.03708 0.18942 0.02895 -0.00304 0.58431 0.07596 0.03563
5 703.93 0.22368 0.00624 0.03736 0.17491 0.02669 -0.00722 0.60242 0.08024 0.03446
6 708.88 0.21496 0.00657 0.03757 0.16494 0.02505 -0.00995 0.61566 0.08352 0.03340
7 709.54 0.20852 0.00681 0.03769 0.15808 0.02391 -0.01175 0.62520 0.08594 0.03255
8 708.45 0.20337 0.00840 0.04133 0.14317 0.01597 -0.01557 0.64669 0.08594 0.03054 (1 component(s) constrained)
9 721.36 0.20259 0.00826 0.04092 0.14332 0.01719 -0.01546 0.64756 0.08645 0.03072 (1 component(s) constrained)
10 719.03 0.19988 0.00794 0.03984 0.14331 0.01866 -0.01546 0.64757 0.08791 0.03071
11 714.62 0.20226 0.00840 0.04123 0.14318 0.01670 -0.01550 0.64770 0.08606 0.03065 (1 component(s) constrained)
12 720.06 0.20256 0.00826 0.04091 0.14332 0.01713 -0.01542 0.64758 0.08634 0.03070 (1 component(s) constrained)
13 719.26 0.19988 0.00794 0.03984 0.14331 0.01861 -0.01543 0.64759 0.08784 0.03070
14 714.84 0.20226 0.00840 0.04122 0.14319 0.01672 -0.01550 0.64770 0.08607 0.03065 (1 component(s) constrained)
15 720.02 0.20256 0.00826 0.04092 0.14331 0.01712 -0.01542 0.64758 0.08634 0.03070 (1 component(s) constrained)
16 719.27 0.19988 0.00794 0.03984 0.14330 0.01861 -0.01543 0.64759 0.08784 0.03070
17 714.84 0.20226 0.00840 0.04122 0.14319 0.01672 -0.01550 0.64770 0.08607 0.03065 (1 component(s) constrained)
18 720.02 0.20256 0.00827 0.04092 0.14331 0.01712 -0.01542 0.64758 0.08634 0.03070 (1 component(s) constrained)
19 719.27 0.19988 0.00794 0.03984 0.14330 0.01861 -0.01543 0.64759 0.08784 0.03070
20 714.84 0.20226 0.00840 0.04122 0.14319 0.01672 -0.01550 0.64770 0.08607 0.03065 (1 component(s) constrained)
21 720.02 0.20256 0.00827 0.04092 0.14331 0.01712 -0.01542 0.64758 0.08634 0.03070 (1 component(s) constrained)
22 719.27 0.19988 0.00794 0.03984 0.14 ...
...
This continues for the length of the analysis. Anyone has suggestions for what might be wrong?
Thanks for the help
Nuno