# GIBBS1F90

## Summary

Modification of gibbsf90 that stores only single trait matrices once. Works much faster than gibbsf90 especially in multiple-traits but needs a modified parameter file in models with different designs per trait.

See PREGSF90 with genotypes (SNP) for options.

## Running

You can run as

gibbs1f90 parameter_file --rounds 1000000 --burnin 10000 --thin 10 --thinprint 10

in which case you give the number of rounds, the burn-in, the storage of samples in the file `gibbs_samples`

, and the interval between printouts in the screen. Otherwise you can simply type `gibbs1f90`

and you will be asked the parameter file and the questions below:

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” 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” using fixed variances.

OPTION solution all 1 2 3

Caution: this option will create a huge output solution file when you run many rounds and/or use a large model. 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 2 -1 5

The (co)variance priors are specified in the parameter file.

Degree of belief for all random effects should be specified using the following structure:

OPTION prior eff1 db1 eff2 db2 … effn dbn -1 dbres

effx correspond to the effect number and dbx to the degree of belief for this random effect, -1 corresponds to the degree of belief of the residual variance.

In this example 2 is the degree of belief for the 5th effect, and 5 is the degree of belief for the residual.

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.