wombat으로 single trait random regression model 풀기
wombat으로 single trait random regression model 풀기
예제 자료
R.A. Mrode, Linear Models for the Prediction of Animal Breeding Values. 2nd. Edition
Page 144. Example 7.2
자료입력
4 4 1 4 17.0
4 4 2 38 18.6
4 4 3 72 24.0
4 4 4 106 20.0
4 4 5 140 20.0
4 4 6 174 15.6
4 4 7 208 16.0
4 4 8 242 13.0
4 4 9 276 8.2
4 4 10 310 8.0
5 5 1 4 23.0
5 5 2 38 21.0
5 5 3 72 18.0
5 5 4 106 17.0
5 5 5 140 16.2
5 5 6 174 14.0
5 5 7 208 14.2
5 5 8 242 13.4
5 5 9 276 11.8
5 5 10 310 11.4
6 6 6 4 10.4
6 6 7 38 12.3
6 6 8 72 13.2
6 6 9 106 11.6
6 6 10 140 8.4
7 7 4 4 22.8
7 7 5 38 22.4
7 7 6 72 21.4
7 7 7 106 18.8
7 7 8 140 18.3
7 7 9 174 16.2
7 7 10 208 15.0
8 8 1 4 22.2
8 8 2 38 20.0
8 8 3 72 21.0
8 8 4 106 23.0
8 8 5 140 16.8
8 8 6 174 11.0
8 8 7 208 13.0
8 8 8 242 17.0
8 8 9 276 13.0
8 8 10 310 12.6
개체, 개체(영구환경효과), HTD(herd-test-day), DIM(days in milk), test day fat yield
위 자료를 data.txt로 저장
혈통입력
1 0 0
2 0 0
3 0 0
4 1 2
5 3 2
6 1 5
7 3 4
8 1 7
개체, 아비, 어미
위 자료를 pedi.txt로 저장
파라미터 파일 작성
# run option - 육종가를 구할 때
# RnSoln_xxx.dat 출력 파일 확인
#RUNOP -v --solvit
# run option - 육종가와 SEP(standard error of prediction)
# reliability(r2) = 1- SEP^2 / sigma_a^2
# RnSoln_xxx.dat 출력 파일 확인
RUNOP -v --blup
# run option - 좋은 초기값으로 분산성분을 추정할 때,
#RUNOP -v --good
# run option - 나쁜 초기값일 때 분산성분을 추정할 때,
#RUNOP -v --bad
# 요약 출력파일에 출력할 내용
COMMENT Single Trait Random Regression Model from Mrode, 2nd Edition, Example 7.2
# Analysis Type : random regression model
ANALYSIS RR
# 혈통 파일 이름
# SumPedigree.out 확인
PED pedi.txt
# 자료 파일 이름
DATA data.txt
animal
pe_ani 99
htd 10
dim 100
fat_yld
END DATA
# Model of analysis
# SumModel.out 확인
# 고정효과의 차수, 5개를 구하고 싶으면 (4, LEG)라고 넣을 것
MODEL
TR fat_yld
FIX htd
COV dim(4,LEG)
RRC dim
RAN animal(3,LEG) NRM
RAN pe_ani(3,LEG)
END MODEL
# 분산 성분
VAR animal 3
3.297 0.594 -1.381
0.921 -0.289
1.005
VAR pe_ani 3
6.872 -0.254 -1.101
3.171 0.167
2.457
VAR residual 1 HOM
3.71
#1 99 3.71
# SumModel.out 확인
# SumEstimates.out 확인
# SumPedigree.out 확인
# FixSolutions.out 확인
# RnSoln_xxx.dat 확인
위 파라미터 파일을 wombat.par로 저장
실행
위 세 파일을 한 폴더에 넣고 다음과 같이 실행
결과 확인
SumModel.out
======= Version 19-05-2012 ======================================= **KM** ====
Program WOMBAT : Summary of information from Set-up step
==============================================================================
Fixed Regression Model from Mrode, 2nd Edition, Example 7.1
Analysis type : "RR"
Data file : "data.txt"
Pedigree file : "pedi.txt"
Parameter file : "wombat.par"
No. of traits = 1
nrec mean sdev min. max.
1 "fat_yld" 42 16.2095 4.45283 8.00000 24.0000
Covariables
1 "dim(4,LEG)" 42 138.381 94.9724 4.00000 310.000
Control variables in RR analysis
nrec mean sdev min. max.
1 "dim" 42 138.381 94.9724 4.00000 310.000
Fixed effects
1 "fat_yld" nlev
1 "htd" 10
Random effects nlev
1 "animal" 8 NRM
2 "pe_ani" 5 IDE
======== end of file ============================13-03-2014==========23:26====
SumPedigree.out
======= Version 19-05-2012 ======================================= **KM** ====
Program WOMBAT : Summary of Pedigree Information
==============================================================================
Fixed Regression Model from Mrode, 2nd Edition, Example 7.1
Analysis type : "RR"
Data file : "data.txt"
Pedigree file : "pedi.txt"
Parameter file : "wombat.par"
No. of animal IDs in data file = = 5
No. of animal IDs in total = = 8
*****Pedigree Structure for random effect : 1 ****************************
Original no. of animals = 8
No. of animals after pruning = 8
... proportion (%) remaining = 100.0
No. of levels w/out records = 3
No. of levels with records = 5 100.0%
... 5 record(s) = 1 20.0%
... 7-10 record(s) = 4 80.0%
Minimum no. of records specified = 2
No. of animals with at least 2 records = 5 100.0%
No. of parents which have progeny which
have at least 2 records = 6
No. of animals with at least 2 records
themselves or on a parent = 5
No. of animals with at least 2 records
themselves, on a parent or sib(s) = 5 62.5%
... in the data = 5 100.0%
Minimum no. of records specified = 3
No. of animals with at least 3 records = 5 100.0%
No. of parents which have progeny which
have at least 3 records = 6
No. of animals with at least 3 records
themselves or on a parent = 5
No. of animals with at least 3 records
themselves, on a parent or sib(s) = 5 62.5%
... in the data = 5 100.0%
Minimum no. of records specified = 4
No. of animals with at least 4 records = 5 100.0%
No. of parents which have progeny which
have at least 4 records = 6
No. of animals with at least 4 records
themselves or on a parent = 5
No. of animals with at least 4 records
themselves, on a parent or sib(s) = 5 62.5%
... in the data = 5 100.0%
No. of animals w/out offspring = 2 25.0%
No. of animals with offspring = 6 75.0%
... and records = 3 37.5%
No. of animals with unknown sire = 3
No. of animals with unknown dam = 3
No. of animals with both parents unknown = 3
No. of animals with records =
... and unknown sire = 0
... and unknown dam = 0
... and both parents unknown = 0
No. of sires = 2
... with progeny in the data = 2
... with records & progeny in data = 0
No. of dams = 4
... with progeny in the data = 4
... with records & progeny in data = 3
No. of animals with known/unpruned grand-parents
... with paternal grandsire = 0
... with paternal granddam = 0
... with maternal grandsire = 3
... with maternal granddam = 3
random effect no. = 1 NRM
no. of elements in NRM/GIN inverse 23
log determinant = -3.4657359027997265
random effect no. = 2 IDE
no. of elements in NRM/GIN inverse 0
log determinant = 0.
======== end of file ============================13-03-2014==========23:26====
FixSolutions.out
======= Version 19-05-2012 ======================================= **KM** ======================================
Program WOMBAT : GLS solutions for fixed effects
================================================================================================================
Fixed Regression Model from Mrode, 2nd Edition, Example 7.1
Covariables for trait no. 1 "fat_yld"
Covariable Reg.coeff Solution S.Error
1 dim(4,LEG) 1 -0.625391 2.03781
1 dim(4,LEG) 2 -0.134573 1.24413
1 dim(4,LEG) 3 0.347903 0.554777
1 dim(4,LEG) 4 -0.421767 0.516324
Fixed effects for trait no. 1 "fat_yld"
Effect Orig.code Level Solution S.Error SolSum=0 No.recs Eff.Mean
1 htd 1 1 5.64162 3.97819 5.02704 3 20.733
1 htd 2 2 3.14627 3.04818 2.53169 3 19.867
1 htd 3 3 4.11556 2.66465 3.50099 3 21.000
1 htd 4 4 3.79846 2.35169 3.18388 4 20.700
1 htd 5 5 1.87155 2.08872 1.25698 4 18.850
1 htd 6 6 -1.43445 1.82919 -2.04902 5 14.480
1 htd 7 7 -1.33602 1.66856 -1.95060 5 14.860
1 htd 8 8 -1.27273 1.74578 -1.88730 5 14.980
1 htd 9 9 -3.94007 1.96976 -4.55464 5 12.160
1 htd 10 10 -4.44444 2.44300 -5.05902 5 11.080
1 htd 0.614574
** marks effects which have been set to zero for the analysis
======== end of file ============================13-03-2014==========23:26======================================
RnSoln_animal.dat 확인
Run N Original ID Tr Solution St.Error Ignore Inbr %
1 1 1 -0.583124E-01 1.72949 0.305 0.000
1 2 0.551960E-01 0.931802 0.239
1 3 -0.441837E-01 0.958263 0.294
2 2 1 -0.727789E-01 1.78148 0.193 0.000
2 2 -0.304888E-01 0.948263 0.154
2 3 -0.243957E-01 0.985140 0.185
3 3 1 0.131091 1.75464 0.257 0.000
3 2 -0.247072E-01 0.941440 0.194
3 3 0.685794E-01 0.971617 0.246
4 4 1 0.344565 1.70689 0.341 0.000
4 2 0.628266E-02 0.923341 0.273
4 3 -0.316413 0.947214 0.327
5 5 1 -0.453733 1.67477 0.386 0.000
5 2 -0.520159E-01 0.911875 0.312
5 3 0.279820 0.931168 0.370
6 6 1 -0.548552 1.76190 0.242 0.000
6 2 0.730074E-01 0.945966 0.168
6 3 0.194574 0.970028 0.252
7 7 1 0.851809 1.71901 0.322 0.000
7 2 -0.950157E-02 0.938473 0.209
7 3 -0.313065 0.952393 0.312
8 8 1 0.220854 1.77165 0.392 12.500
8 2 0.126967E-01 0.965884 0.316
8 3 -0.174409E-01 0.984880 0.377
RnSoln_pe_ani.dat
Run N Original ID Tr Solution St.Error Ignore
1 4 1 -0.648602 1.86278 0.704
1 2 -0.360050 1.26306 0.705
1 3 -1.47183 1.13897 0.687
2 5 1 -0.776143 1.94435 0.671
2 2 0.137007 1.30086 0.683
2 3 0.968819 1.17481 0.662
3 6 1 -1.99268 2.41239 0.391
3 2 0.985107 1.66794 0.350
3 3 -0.693079E-01 1.40407 0.445
4 7 1 3.51876 1.94636 0.670
4 2 -1.05097 1.54725 0.495
4 3 -0.404755 1.33179 0.527
5 8 1 -0.101334 1.88597 0.695
5 2 0.288905 1.27583 0.698
5 3 0.977070 1.15081 0.679
관련 파일
10_STRR.zip