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GIBBS2F90
Ignacy Misztal, University of Georgia, 3/7/2001
Summary
Gibbs2f90 is a modification of gibbs1f90 to sample correlated effects jointly. This modification results in better mixing for models with correlated effects such as maternal or random-regression models. Memory requirements and CPU time per round are somewhat higher than in gibbs1f90.
Running
number of samples and length of burn-in?
In the first run, if you have no idea about the number of samples and burn-in, just type your guess (10000 or whatever) for samples and (0) for burn-in. You may need 2 or 3 runs to figure out the convergence.
Give n to store every n-th sample?
Gibbs samples are usually highly correlated, so you do not have to keep all samples. Maybe every 10th,20th, 50th, …
To check the convergence and to calculate posterior means and SD, run postgibbsf90.
OPTION fixed_var all 1 2 3
All solutions and posterior means and SD for effects for effects1, 2, and 3 are stored in “all_solutions” and in “final_solutions” every round using fixed variances. Without numbers, all solutions for all effects are stored.
OPTION fixed_var mean 1 2 3
Posterior means and SD for effects1, 2, and 3 in “final_solutions”.
OPTION solution all 1 2 3
All solutions and posterior means and SD for effects1, 2, and 3 are stored in “all_solutions” and in “final_solutions” every round. Without numbers, all solutions for all effects are stored.
OPTION solution mean 1 2 3
Posterior means and SD for effects1, 2, and 3 in “final_solutions”.
OPTION cont 10000
10000 is the number of samples run previously when restarting the program from the last run.
OPTION prior 5
5 is the degree of belief for the priors specified in the parameter file.
OPTION seed 123 321
Two seeds for a random number generator can be specified.
OPTION SNP_file snp
Specify the SNP file name to use genotype data.