GIBBS3F90 - modifications for heterogeneous residual variances written by Ignacy Misztal, September 19, 2002 Gibbs3f90 is a modification of gibbs2f90. 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: 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. Available options: 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. How do OPTIONs work? 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. 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 GIBBS2F90 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"