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