This is a combined program of gibbs2f90, gibbs3f90, thrgibbs1f90, and thrgibbs3f90.
GIBBS1F90 implements Gibbs sampler for mixed threshold-linear models involving multiple categorical and linear variables. Thresholds and variances can be estimated or assumed. Another version of thrgibbs1f90b for binary responses is available.
See PREGSF90 with genotypes (SNP) for options.
When you run the program, the program will ask you several questions. You have to type-in (input) the answer to each question. The first one is the name of parameter file.
name of parameter file?
Then, the program asks you the number of total samples (rounds) and the number of burn-in. The program runs through up to the rounds specified here and the first burn-in samples will not be saved.
number of samples and length of burn-in
Here, you put two integer numbers separated by spaces (or Enter key).
For example, if you need 1000 samples with no burn-in, the correct input is
Finally, the program asks you the interval to save samples. It is also known as thinning.
Give n to store every n-th sample? (1 means store all samples)
For example, if you put
10, the program save the sample every 10 rounds.
The other samples will be discarded.
To save all samples, please input
Instead of typing the above parameters, you can put them in the command line in shell. Here is the general form of command line.
gibbsf90test parfile --samples i --burnin j --interval k
Each item should be replaced with your favorite value.
parfile: the name of parameter file (characters)
i: the number of samples (integer)
j: the number of burn-in (integer)
k: interval to save samples (integer)
For example, when you use `renf90.par` as parameter file, sample 10000 times with no burn-in, and save all samples, the following command works.
gibbsf90test renf90.par --samples 10000 --burnin 0 --interval 1
Tips: you can use
--rounds instead of
--samples (even the singular form is okay:
--thinning is acceptable as
The parameter file is the same as for BLUPF90 except for options.
OPTION cat 0 0 2 5
“0” indicate that the first and second traits are linear. “2” and “5” indicate that the third and fourth traits are categorical with 2 (binary) and 5 categories.
OPTION fixed_var all
Store all samples for solutions in “all_solutions” and posterior means and SD for all effects in “final_solutions”, assuming that (co)variances in the parameter file are known.
OPTION fixed_var all 1 2 3
Store all samples for solutions in “all_solutions” and posterior means and SD for 1, 2, and 3 effects in “final_solutions”, assuming that (co)variances in the parameter file are known.
OPTION fixed_var mean
Only posterior means and SD for solutions are calculated for all effects in “final_solutions”, assuming that (co)variances in the parameter file are known.
OPTION fixed_var mean 1 2 3
Only posterior means and SD for solutions are calculated for effects 1, 2, and 3 in “final_solutions”, assuming that (co)variances in the parameter file are known.
OPTION solution all
Caution: this option will create a huge output solution file when you run many rounds and/or use a large model. Store all samples for solutions in “all_solutions” and posterior means and SD for all effects. The file “all_solutions” could be very large.
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. Store all samples for solutions in “all_solutions” and posterior means and SD for 1, 2, and 3 effects. The file “all_solutions” could be very large.
OPTION solution mean
Only posterior means and SD for solutions are calculated for all effects in “final_solutions” while sampling (co)variances.
OPTION solution mean 1 2 3
Only posterior means and SD for solutions are calculated for effects 1, 2, and 3 in “final_solutions” while sampling (co)variances.
OPTION save_halfway_samples 5000
The program saves every “5000” samples to restart or recover the job right after the last saved samples. It is useful when the program accidentally stopped.
OPTION cont 10000
“10000” is the number of samples run previously. The user can restart the program from the last run. This option requires “binary_final_solutions”, “gibbs_samples”, and “fort.99” files. When using “OPTION cont”, all output files will be replaced by new ones. Before running with this option, all files should be backed up.
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 -432
Two seeds for a random number generator can be specified.
OPTION thresholds 0.0 1.0 2.0
Set the fixed thresholds. No need to set 0 for binary traits.
OPTION residual 1
Set the residual variance = 1 for categorical traits. For binary traits, the residual variance is automatically set to 1, so no need to use this option.
OPTION pos_def x.x
Specify checking pos-def for fixed effects where x.x is a tolerance (default=1d-08).
OPTION censored 1 0
Negative values for the categorical trait in the data set indicate censored records. “1 0” determines that the first categorical trait is censored and the second uncensored.
OPTION SNP_file snp
Specify the SNP file name to use genotype data.
OPTION external_halfway_script name n
program gibbstest will stop every n iterations, write samples of variances, unknowns and thresholds, wait for external
'name' to run and then restart
This is useful to compute diagnostics from samples without storing them or modifying the program.
OPTION save_halfway_samples n
This option can help the 'cold start' (to continue the sampling when the program accidentally stops before completing the run). An integer value n is needed. In every n rounds, the program saves intermediate samples to 2 files (
binary_final_solutions). The program can restart the sampling form the last round where the intermediate files were saved. The program also writes a log file
save_halfway_samples.txt with useful information for the next run.
To restart, add
OPTION cont 1 to your parameter file and run
gibbsf90test again. Input 3 numbers (samples, burn-in, and interval) according to save_halfway_samples.txt. Gibbsf90test can take care of all restarting process by itself, so no other tools are needed.
gibbs_samplesis not created. Recommendation is burn-in=0 (but it doesn't provide posterior mean and SD for solutions).
binary_final_solutionsat some point and write them back if needed.
Put the following option in your parameter file for the first time when you run the program.
OPTION save_halfway_samples 100
gibbsf90test. You will see the following message on screen.
'**** saving halfway samples in every 100 rounds (default=0)
In this example, we assume the number of total samples is 3000, the burn-in is 0, and the interval is 10.
number of samples and length of burn-in 3000 0 Give n to store every n-th sample? (1 means store all samples) 10
Make sure the intermediate results are saved to files.
100 rounds G 2758. 1900. 2019. 1900. 1690. 1656. 2019. 1656. 1737. G 225.5 -91.35 -9.998 -91.35 403.5 474.6 -9.998 474.6 702.2 R 1755. 868.7 817.0 868.7 2122. 1361. 817.0 1361. 1804. * Last seeds = 1877469549 348652151 * Number of samples kept = 100 solutions stored in binary file: "last_solutions" solutions stored in file: "binary_final_solutions"
Stop the program. In this case, program stops in the round 880.
880 rounds forrtl: error (69): process interrupted (SIGINT) Image PC Routine Line Source thrgibbs1f90 0000000000943031 Unknown Unknown Unknown thrgibbs1f90 0000000000941787 Unknown Unknown Unknown
Make sure there are the following 5 files.
binary_final_solutions fort.99 gibbs_samples last_solutions save_halfway_samples.txt
Browse the file
save_halfway_samples.txt. Only information in the 'suggestion' block is needed for the next run. The halfway samples were saved in the round 800 so you will start the sampling from the round 801 in the next run. Remaining 2200 rounds are needed to satisfy the initial goal (3000 rounds).
Saved on 2017-03-10 10:53:22 State in the current run: last round = 800 sampled in this run = 800 total number of samples = 3000 number of burn-in = 0 interval = 10 Suggestion for input in the next run: total number of samples = 2200 number of burn-in = 0 interval = 10 When you restart the program, do not forget to put the following option in your parameter file. OPTION cont 1
Put the option
OPTION cont 1 to your parameter file manually (by hand). It can invoke the 'cold start' module in
OPTION cont 1
gibbsf90test again. You will see the following message on screen.
'*** continuous sampling selected *** previous # samples = 1
NOTE: Although the message may say the previous number of sample is 1, you can safely ignore it. The program runs with the cold start mode and it works correctly.
Input the three numbers that are shown in
number of samples and length of burn-in 2200 0 Give n to store every n-th sample? (1 means store all samples) 10
The program will start from the round 801 as expected.
801 rounds G 828.0 601.3 610.8 601.3 996.3 877.4 610.8 877.4 822.8 G 2541. 1531. 1756. 1531. 1459. 1598. 1756. 1598. 1898. R 1800. 833.0 813.3 833.0 2114. 1303. 813.3 1303. 1778.
You can interrupt the program again. The results will be basically the same to ones from the non-stop analysis.