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lm_lods
output
Run the lm_lods
example by typing:
lm_lods par15_lods |
The main results of interest from lm_lods
are the LOD scores which
are given at the end of the output for each component (connected pedigree)
at each position requested.
The LOD scores from this example look like this (some outputs omitted to save space):
ESTIMATED LOD SCORES Component 1 The largest eigenvalue : 2.12703 The second largest eigenvalue : 1.96799 Cumulative from left : 0.12331 Cumulative from right : 8.10940 LodScore estimates: Trait pos # position (Haldane cM) or marker male female eigen left right 1 -115.129 -115.129 0.16014 -0.02971 0.87928 2 -60.199 -60.199 0.38449 -0.13656 0.77243 3 -34.657 -34.657 0.58356 -0.14461 0.76438 4 -17.834 -17.834 0.90068 -0.04843 0.86056 5 -5.268 -5.268 1.53650 0.15850 1.06749 marker-1 0.000 0.000 NA NA NA 6 5.108 5.108 2.00899 0.33920 1.24819 7 12.771 12.771 2.07100 0.30338 1.21237 8 20.433 20.433 2.05423 0.31048 1.21947 marker-2 25.541 25.541 NA NA NA 9 30.650 30.650 1.61970 0.24335 1.15234 10 38.312 38.312 1.32440 0.26820 1.17719 11 45.974 45.974 1.13920 0.18546 1.09445 marker-3 51.083 51.083 NA NA NA 12 56.191 56.191 0.78887 0.12538 1.03436 13 63.853 63.853 0.74783 0.17440 1.08339 14 71.516 71.516 0.64426 0.11037 1.01936 marker-4 76.624 76.624 NA NA NA 15 81.732 81.732 0.64836 0.24527 1.15426 16 89.394 89.394 0.39596 0.06815 0.97714 17 97.057 97.057 0.33385 0.02844 0.93743 marker-5 102.165 102.165 NA NA NA 18 107.433 107.433 0.08793 -0.21787 0.69112 19 119.999 119.999 -0.13217 -0.47354 0.43545 20 136.822 136.822 -0.30042 -0.75551 0.15348 21 162.364 162.364 -0.25966 -0.89959 0.00940 22 217.294 217.294 -0.11343 -0.89276 0.01623 |
As we mentioned earlier, there are three methods to combine the likelihood ratios (for each test position over the position to the left, and over the position to the right): the eigenvalue method, simple averaging starting from the left, and simple averaging starting from the right.
The largest real eigenvalue should, in theory, be equal to 2.0 and the eigenvector corresponding to the largest real eigenvalue is given as the LOD scores. However, when the second largest eigenvalue is very close to the largest one, the eigenvector can be very unstable and sometimes gives very bad LOD scores. When that happens, the "left" and "right" method, though, simpler, actually perform better.
The "Cumulative from left" and "Cumulative from right" values
should, ideally, be one (their product is always one). Usually they are
not and one can see that the LOD scores differ a lot for these three
methods. This was a very short MCMC run. For longer runs, the LOD
scores can be more consistent for the three methods. Nevertheless,
lm_lods
is now giving way to our newer method, lm_bayes
.
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