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lm_bayes
output Run the example by typing:
lm_bayes par15_bayes |
The results from lm_bayes
are the LOD scores toward the end of
the output. Two methods of computing the LOD scores are available:
(1) count realizations of locations sampled to estimate the posterior
probability (crude) and (2) Rao-Blackwellized estimator (R-B). The
latter is the preferred method.
LodScore estimates: Trait pos # position (Haldane cM) pseudo freq LodScore or marker male female prior visited crude R-B 0 unlinked unlinked 0.072240 11 NA NA 1 -115.129 -115.129 0.073157 24 0.3333 -0.0170 2 -60.199 -60.199 0.073318 11 -0.0064 -0.0398 3 -34.657 -34.657 0.069086 12 0.0572 -0.0281 4 -17.834 -17.834 0.057102 3 -0.4621 0.0479 5 -5.268 -5.268 0.038580 4 -0.1669 0.2009 marker-1 0.000 0.000 NA NA NA NA 6 5.108 5.108 0.034398 16 0.4850 0.2780 7 12.771 12.771 0.036335 18 0.5123 0.2974 8 20.433 20.433 0.031742 15 0.4918 0.3532 marker-2 25.541 25.541 NA NA NA NA 9 30.650 30.650 0.026108 5 0.0996 0.3707 10 38.312 38.312 0.024442 3 -0.0936 0.3374 11 45.974 45.974 0.021466 6 0.2638 0.3163 marker-3 51.083 51.083 NA NA NA NA 12 56.191 56.191 0.020620 1 -0.4969 0.2723 13 63.853 63.853 0.021574 1 -0.5165 0.2687 14 71.516 71.516 0.020624 6 0.2812 0.3134 marker-4 76.624 76.624 NA NA NA NA 15 81.732 81.732 0.022869 0 NA 0.3285 16 89.394 89.394 0.027993 3 -0.1525 0.3172 17 97.057 97.057 0.030376 4 -0.0631 0.3599 marker-5 102.165 102.165 NA NA NA NA 18 107.433 107.433 0.036779 6 0.0299 0.3137 19 119.999 119.999 0.052239 24 0.4796 0.1360 20 136.822 136.822 0.064913 1 -0.9949 0.0336 21 162.364 162.364 0.071376 12 0.0430 -0.0040 22 217.294 217.294 0.072663 16 0.1602 -0.0054 |
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