11.7.8 Location LOD scores MCMC parameters and options

• All statements described in Autozyg MCMC parameters and options, for specifying the MCMC parameters are also used for the location LOD scores programs.

As with the Autozg programs, the number of desired MC iterations must be specified, as there is no default value.

set MC iterations I

This statement sets the total number of "main" L- and M-sampler iterations. For lm_markers, the total MCMC run length is the sum of the number of burn-in iterations and main iterations. For lm_lods and lm_schnell, the total MCMC run length is the number of burn-in iterations, plus the product of the number of test positions for the trait, (see Location LOD scores mapping model parameters), and the number of main iterations. For lm_bayes, the total MCMC run length is the sum of the number of burn-in, pseudo-prior (see below) and main iterations.

Additional statements for lm_bayes include the following:

set pseudo-prior iterations I

Following burn-in, lm_bayes performs iterations to calculate the pseudo-priors. These pseudo-priors are used to encourage the MCMC sampler to visit test positions of low posterior probability. The default number of iterations to compute pseudo-priors is 50% of the number of main iterations specified in the `set MC iterations' statement.

set sequential imputation proposals every I iterations

This option applies to lm_bayes's pseudo-prior and main MCMC iterations. It allows the MCMC chain to "restart" every Ith iteration. Sequential imputation is used to propose potential restart configurations which are accepted/rejected with Metropolis-Hastings probability.

set test position window I

This lm_bayes statement specifies the window size for the Metropolis-Hastings algorithm. I is the number of hypothesized trait positions on either side of the current position, with equal weight given to the 2*I + 1 trait positions. The default is window size is 6.