User Tools

Site Tools


readme.gibbs3

GIBBS3F90

Summary

Modifications of gibbs2f90 for heterogeneous residual variances written by Ignacy Misztal, September 19, 2002
It allows estimation of separate residual (co)variances for different subsets of observations. The estimation is enabled by addition of the following line at the end of the parameter file:
See PREGSF90 with genotypes (SNP) for options.

Running

   OPTION hetres_int col nlev

where col is column in the data file that selects which residual (co)variance to select, and nlev is the maximum number of levels. Different residual (co)variances need to be numbered consecutively starting from 1.

Initially, all residual (co)variances are assigned identical (co)variances as in the parameter file. During the estimation, (co)variances that are 0 in the parameter file will not be estimated.

The number of observations per each subset must be large enough to allow the estimation, and the missing-trait pattern should be similar. The program has not been tested in multiple-trait situations when one trait is present in some subclasses but always missing in another subclass.

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 hetres_int 5 10

The position “5” to identify the interval in the data file and the number of intervals “10” for heterogeneous residual variances.

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”.

x11 x12 x13
x21 x22 x23
x31 x32 x33
y11 y12 y13
y21 y22 y23
y31 y32 y33
:
:
:
z11 z12 z13
z21 z22 z23
z31 z32 z33

When fixed_var is used, you should provide the residual (co)variances in the file hetres. Give residual covariances for each interval. For example, in the above file, we assume a 3 trait-model and each covariance matrix is 3 x 3 (e.g., x, y, and z). Note that the older version reads the variance components from the parameter file. The current version reads the external file “hetres” and the variance components included in the parameter file will not be used any more.

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

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

Example

Model

y_ijk1=hys_i1+sire_j1+e_ijk1 y_ijk2=hys_i2+sire_j2+e_ijk2

var(sire_i1)=75; var(e_ijk1)=50+ceil(k/2000)*50 y2=2*y1+normal(0,16)

50 sires, 10,000 records, 5 intervals

Data (datasire)

1 - HYS
2 - sire
3 - y1
4 - heterogeneous class
5 - y2

cat datasire

6 13 317.55 1 644.26
3 10 280.44 1 563.05
....................
37 1 270.52 5 543.63
53 10 286.43 5 579.84

Parameter file (ex5)

DATAFILE
datasire
NUMBER_OF_TRAITS
NUMBER_OF_EFFECTS
OBSERVATION(S)
WEIGHT(S)
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT  [EFFECT NESTED]
1 1 100 cross
2 2 50 cross
RANDOM_RESIDUAL VALUES
500 100
100 1000
RANDOM_GROUP
RANDOM_TYPE
diagonal
FILE
(CO)VARIANCES
75 10
10 150
OPTION hetres_int 4 5

Execution

gibbs3f90

 name of parameter file?ex5
     GIBBS3F90  ver. 1.50 by I. Misztal
 Parameter file:             ex5
 Data file:                  datasire
 Number of Traits             2
 Number of Effects            2
 Position of Observations      3  5
 Position of Weight (1)        0
 Value of Missing Trait/Observation           0
EFFECTS
 #  type                position (2)        levels   [positions for nested]
 1  cross-classified     1  1                                                    100
 2  cross-classified     2  2                                                     50
 Residual (co)variance Matrix
  500.00      100.00
  100.00      1000.0
 Random Effect(s)    2
 Type of Random Effect:      diagonal
 trait   effect    (CO)VARIANCES
  1       2     75.00       10.00
  2       2     10.00       150.0
 REMARKS
  (1) Weight position 0 means no weights utilized
  (2) Effect positions of 0 for some effects and traits means that such
      effects are missing for specified traits
  number of samples and length of burn-in
100 20
 Give n to store every n-th sample? (1 means store all samples)
1
 Option hetres_int -- to estimate heteregenous residual variances in intervals
    read:  5  intervals, data column   4

 Data record length =   5
 Missing traits found in   0  records
  77.1      151.
  151.      304.
 Residual variance, interval   1
 df_r  1997 ee/n  108.294454569457
  108.      199.
  199.      416.
 Residual variance, interval   2
 df_r  1997 ee/n  152.131225307972
  150.      287.
  287.      597.
 Residual variance, interval   3
 df_r  1997 ee/n  204.357724084865
  206.      396.
  396.      807.
 Residual variance, interval   4
 df_r  1997 ee/n  240.494017267479
  239.      462.
  462.      941.
 Residual variance, interval   5
 df_r  1997 ee/n  301.022455438506
  294.      579.
  579.     0.119E+04

.....................................

 round   97
  111.      221.
  221.      441.
 Residual variance, interval   1
 df_r  1997 ee/n  100.000009514270
  98.9      197.
  197.      402.
 Residual variance, interval   2
 df_r  1997 ee/n  145.159649196084
  147.      293.
  293.      592.
 Residual variance, interval   3
 df_r  1997 ee/n  195.187063040059
  206.      412.
  412.      834.
 Residual variance, interval   4
 df_r  1997 ee/n  232.588622554471
  230.      460.
  460.      929.
 Residual variance, interval   5
 df_r  1997 ee/n  299.190674678875
  300.      602.
  602.     0.122E+04
 round   98
  209.      416.
  416.      828.
 Residual variance, interval   1
 df_r  1997 ee/n  99.4738134864675
  101.      202.
  202.      412.
 Residual variance, interval   2
 df_r  1997 ee/n  146.518188769043
  148.      296.
  296.      602.
 Residual variance, interval   3
 df_r  1997 ee/n  198.183671561078
  198.      397.
  397.      806.
 Residual variance, interval   4
 df_r  1997 ee/n  232.307903786663
  228.      455.
  455.      917.
 Residual variance, interval   5
 df_r  1997 ee/n  301.189371418363
  311.      622.
  622.     0.126E+04
 round   99
  80.4      160.
  160.      319.
 ave G
  90.1      180.
  180.      358.
 SD G
  24.2      48.3
  48.3      96.3
 Residual variance, interval   1
 df_r  1997 ee/n  99.4617504906976
  99.7      199.
  199.      406.
 ave R
  99.5      199.
  199.      406.
 SD R
  2.91      5.93
  5.93      12.2
 Residual variance, interval   2
 df_r  1997 ee/n  144.322137485480
  139.      279.
  279.      567.
 ave R
  145.      291.
  291.      591.
 SD R
  4.97      10.2
  10.2      21.0
 Residual variance, interval   3
 df_r  1997 ee/n  198.649297883965
  201.      404.
  404.      821.
 ave R
  200.      400.
  400.      810.
 SD R
  5.41      10.9
  10.9      22.1
 Residual variance, interval   4
 df_r  1997 ee/n  233.760179013841
  227.      451.
  451.      904.
 ave R
  232.      464.
  464.      937.
 SD R
  6.56      13.2
  13.2      26.6
 Residual variance, interval   5
 df_r  1997 ee/n  298.462490006407
  303.      607.
  607.     0.123E+04
 ave R
  300.      602.
  602.     0.122E+04
 SD R
  9.13      18.4
  18.4      37.2
 round   100
 solutions stored in file: "solutions"
 Samples stored in file "gibbs_samples"
readme.gibbs3.txt · Last modified: 2019/05/14 02:21 by yutaka