blupf90으로 fixed regression model의 육종가 구하기
R. A. Mrode, Linear Models for the prediction of Animal Breeding Values, 2nd Edition. p137 Example 7.1
data
4 1 4 17.0
4 2 38 18.6
4 3 72 24.0
4 4 106 20.0
4 5 140 20.0
4 6 174 15.6
4 7 208 16.0
4 8 242 13.0
4 9 276 8.2
4 10 310 8.0
5 1 4 23.0
5 2 38 21.0
5 3 72 18.0
5 4 106 17.0
5 5 140 16.2
5 6 174 14.0
5 7 208 14.2
5 8 242 13.4
5 9 276 11.8
5 10 310 11.4
6 6 4 10.4
6 7 38 12.3
6 8 72 13.2
6 9 106 11.6
6 10 140 8.4
7 4 4 22.8
7 5 38 22.4
7 6 72 21.4
7 7 106 18.8
7 8 140 18.3
7 9 174 16.2
7 10 208 15.0
8 1 4 22.2
8 2 38 20.0
8 3 72 21.0
8 4 106 23.0
8 5 140 16.8
8 6 174 11.0
8 7 208 13.0
8 8 242 17.0
8 9 276 13.0
8 10 310 12.6
animal, htd(herd-test-day), dim(days-in-milk), test day fat yield
data.txt로 저장
renumf90(blupf90)은 fixed regression model을 위한 다항식(polynomial)을 자동으로 만들어 주지 않는다. 그래서 미리 dim에 대한 다항식을 만들어 주어야 한다.
dim 변수만 뽑는다.
4
38
72
106
140
174
208
242
276
310
4
38
72
106
140
174
208
242
276
310
4
38
72
106
140
4
38
72
106
140
174
208
4
38
72
106
140
174
208
242
276
310
dim.txt로 저장한다.
다음 R 프로그램을 실행시킨다.
# renumf90에선 자동으로 regression model을 위한 polynomial을 생성하지 못함
# renumf90에서 regression model을 적용하기 위한 polynomial 생성 프로그램
# 작업 디렉토리 설정
# 작업 디렉토리를 다음 " " 사이에 복사, 윈도우즈에선 \ 대신에 / 사용
setwd("C:/AB/blupf90/09_fixed_reg")
getwd()
# MASS library 사용
library(MASS)
# read data and check
dim = as.matrix(read.table("dim.txt"))
dim
# maximum
dim_max = max(dim)
dim_max
# minimum
dim_min = min(dim)
dim_min
# make M matrix(polynomials of the standardized DIM values)
dim_1 = c(rep(1,nrow(dim)))
dim_2 = -1 + 2 * (dim - dim_min) / (dim_max - dim_min)
dim_3 = dim_2 ^ 2
dim_4 = dim_3 * dim_2
dim_5 = dim_4 * dim_2
M = cbind(dim_1, dim_2, dim_3, dim_4, dim_5)
M
# Legendre polynomials 계수를 포함하는 차수 k = 5의 행렬
lambda = matrix(c(
sqrt(.5) , 0.0 , 0.0 , 0.0 , 0.0,
0.0 , sqrt(1.5) , 0.0 , 0.0 , 0.0,
-sqrt(2.5)*.5 , 0.0 , sqrt(2.5)*1.5 , 0.0 , 0.0,
0.0 , -sqrt(3.5)*1.5, 0.0 , sqrt(3.5)*2.5, 0.0,
sqrt(4.5)*3./8., 0.0 , -sqrt(4.5)*30./8., 0.0 , sqrt(4.5)*35./8.),nrow = 5)
lambda
# Legendre Polynomial
dim_poly = M %*% lambda
dim_poly
# export data
write.table(dim_poly, "dim_poly.txt", sep = " ", row.names = FALSE, col.names = FALSE)
위 R 프로그램을 legendre_polynomial.R로 저장
위 프로그램에 주석으로 설명을 추가하였으므로 추가적인 설명은 하지 않음
다음은 R 프로그램 실행화면
“파일 -> 스크립트 열기” 하여 위에서 저장한 legendre_polynomial.R 프로그램을 연다.
dim.txt가 있는 디렉토리를 setwd에 적어준다.
실행할 스크립트 부분을 선택한 후 ctrl + R 을 눌러 실행하면 실행결과가 왼쪽 창에 나타난다.
다음은 R 프로그램 실행결과 생긴 dim_poly.txt의 내용
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157
0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157
0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465
0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036
0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912
0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157
0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157
0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465
0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036
0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912
0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157
0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157
0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157
0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157
0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465
0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036
0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912
0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964
dim_poly.txt 내용을 data.txt의 왼쪽 컬럼에 붙여 data_2.txt를 만든다. 다음은 data_2.txt의 내용이다.
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 4 1 4 17.0
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 4 2 38 18.6
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 4 3 72 24.0
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 4 4 106 20.0
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 4 5 140 20.0
0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157 4 6 174 15.6
0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465 4 7 208 16.0
0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036 4 8 242 13.0
0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912 4 9 276 8.2
0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964 4 10 310 8.0
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 5 1 4 23.0
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 5 2 38 21.0
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 5 3 72 18.0
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 5 4 106 17.0
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 5 5 140 16.2
0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157 5 6 174 14.0
0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465 5 7 208 14.2
0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036 5 8 242 13.4
0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912 5 9 276 11.8
0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964 5 10 310 11.4
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 6 6 4 10.4
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 6 7 38 12.3
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 6 8 72 13.2
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 6 9 106 11.6
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 6 10 140 8.4
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 7 4 4 22.8
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 7 5 38 22.4
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 7 6 72 21.4
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 7 7 106 18.8
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 7 8 140 18.3
0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157 7 9 174 16.2
0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465 7 10 208 15.0
0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 8 1 4 22.2
0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 8 2 38 20.0
0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 8 3 72 21.0
0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 8 4 106 23.0
0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 8 5 140 16.8
0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157 8 6 174 11.0
0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465 8 7 208 13.0
0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036 8 8 242 17.0
0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912 8 9 276 13.0
0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964 8 10 310 12.6
pedigree
1 0 0
2 0 0
3 0 0
4 1 2
5 3 2
6 1 5
7 3 4
8 1 7
pedi.txt로 저장
renumf90을 위한 parameter 파일 작성
# Parameter file for program renf90; it is translated to parameter
# file for BLUPF90 family programs.
DATAFILE
data_2.txt
TRAITS
9
FIELDS_PASSED TO OUTPUT
WEIGHT(S)
RESIDUAL_VARIANCE
3.71
EFFECT
7 cross numer
EFFECT
1 cov
EFFECT
2 cov
EFFECT
3 cov
EFFECT
4 cov
EFFECT
5 cov
EFFECT
6 cross numer
RANDOM
animal
OPTIONAL
pe
FILE
pedi.txt
FILE_POS
1 2 3
PED_DEPTH
0
(CO)VARIANCES
5.521
(CO)VARIANCES_PE
8.47
설명
DATAFILE
data_2.txt
자료 파일 이름
TRAITS
9
자료 파일에서 관측치의 위치(컬럼)
FIELDS_PASSED TO OUTPUT
WEIGHT(S)
RESIDUAL_VARIANCE
3.71
잔차 분산
EFFECT
7 cross numer
7 컬럼(HTD:herd-test-day)이 고정효과로 쓰임
EFFECT
1 cov
EFFECT
2 cov
EFFECT
3 cov
EFFECT
4 cov
EFFECT
5 cov
첫째 컬럼에서 다섯째 컬럼이 회귀 변수
EFFECT
6 cross numer
6째 컬럼이 분류 효과
RANDOM
animal
임의 개체 효과
OPTIONAL
pe
개체의 자료가 중복됨에 따라 영구 환경 효과 추가
FILE
pedi.txt
혈통 파일 이름
FILE_POS
1 2 3
혈통 파일은 animal, sire, dam
PED_DEPTH
0
끝까지 혈통 추적
(CO)VARIANCES
5.521
개체 효과 분산
(CO)VARIANCES_PE
8.47
영구 환경 효과의 분산
실행 화면
renf90.tables
Effect group 1 of column 1 with 10 levels, effect # 1
Value # consecutive number
1 3 1
2 3 2
3 3 3
4 4 4
5 4 5
6 5 6
7 5 7
8 5 8
9 5 9
10 5 10
고정효과 : 원래 번호, 개수, 새로운 번호
renf90.dat
17.0 1 0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 2
18.6 2 0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 2
24.0 3 0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 2
20.0 4 0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 2
20.0 5 0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 2
15.6 6 0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157 2
16.0 7 0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465 2
13.0 8 0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036 2
8.2 9 0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912 2
8.0 10 0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964 2
23.0 1 0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 5
21.0 2 0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 5
18.0 3 0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 5
17.0 4 0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 5
16.2 5 0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 5
14.0 6 0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157 5
14.2 7 0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465 5
13.4 8 0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036 5
11.8 9 0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912 5
11.4 10 0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964 5
10.4 6 0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 3
12.3 7 0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 3
13.2 8 0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 3
11.6 9 0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 3
8.4 10 0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 3
22.8 4 0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 1
22.4 5 0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 1
21.4 6 0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 1
18.8 7 0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 1
18.3 8 0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 1
16.2 9 0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157 1
15.0 10 0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465 1
22.2 1 0.707106781186548 -1.22474487139159 1.58113883008419 -1.87082869338697 2.12132034355964 4
20.0 2 0.707106781186548 -0.95257934441568 0.644167671515781 -0.0179640615277216 -0.620456293139911 4
21.0 3 0.707106781186548 -0.680413817439772 -0.0585606974105255 0.757056878668253 -0.775651197104036 4
23.0 4 0.707106781186548 -0.408248290463863 -0.52704627669473 0.762189467676173 0.0261891400439461 4
16.8 5 0.707106781186548 -0.136082763487954 -0.761289066336832 0.305389045971261 0.698700390555157 4
11.0 6 0.707106781186548 0.136082763487954 -0.761289066336832 -0.305389045971261 0.698700390555157 4
13.0 7 0.707106781186548 0.408248290463863 -0.52704627669473 -0.762189467676173 0.0261891400439465 4
17.0 8 0.707106781186548 0.680413817439772 -0.0585606974105255 -0.757056878668253 -0.775651197104036 4
13.0 9 0.707106781186548 0.95257934441568 0.64416767151578 0.0179640615277207 -0.620456293139912 4
12.6 10 0.707106781186548 1.22474487139159 1.58113883008419 1.87082869338697 2.12132034355964 4
trait1, htd 고정효과, dim 회귀효과(1-5), 개체효과
renadd03.ped
1 8 2 1 0 2 7 0 1 7
7 0 0 3 0 0 0 0 2 2
2 6 7 1 0 2 10 0 1 4
3 6 5 1 0 2 5 0 0 6
6 0 0 3 0 0 0 3 0 1
4 6 1 1 0 2 10 0 0 8
8 0 0 3 0 0 0 2 0 3
5 8 7 1 0 2 10 0 1 5
renumbered 된 혈통. 자세한 설명은 single trait animal model 참조
renf90.par
# BLUPF90 parameter file created by RENF90
DATAFILE
renf90.dat
NUMBER_OF_TRAITS
1
NUMBER_OF_EFFECTS
8
OBSERVATION(S)
1
WEIGHT(S)
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
2 10 cross
3 1 cov
4 1 cov
5 1 cov
6 1 cov
7 1 cov
8 8 cross
8 8 cross
RANDOM_RESIDUAL VALUES
3.7100
RANDOM_GROUP
7
RANDOM_TYPE
add_animal
FILE
renadd07.ped
(CO)VARIANCES
5.5210
RANDOM_GROUP
8
RANDOM_TYPE
diagonal
FILE
(CO)VARIANCES
8.4700
설명
DATAFILE
renf90.dat
자료 파일의 이름
NUMBER_OF_TRAITS
1
형질의 수
NUMBER_OF_EFFECTS
8
효과의 수(htd, dim1 ~ dim5, 개체효과, 영구환경효과)
OBSERVATION(S)
1
관측치의 위치
WEIGHT(S)
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
2 10 cross
3 1 cov
4 1 cov
5 1 cov
6 1 cov
7 1 cov
8 8 cross
8 8 cross
둘째 컬럼이 효과. 레벨 개수는 10, 분류 효과
셋째 컬럼에서 일곱째 컬럼이 연속 변수 효과.
여덢째 컬럼이 효과. 레벨 개수는 8, 분류 효과, 개체 효과와 영구 환경 효과로 쓰일 것이므로 두 번 쓰임
RANDOM_RESIDUAL VALUES
3.7100
잔차 효과의 분산
RANDOM_GROUP
7
효과 중 일곱째 효과가 임의 효과 그룹
RANDOM_TYPE
add_animal
additive genetic animal effect
FILE
renadd07.ped
혈통 파일의 이름
(CO)VARIANCES
5.5210
개체 효과의 분산
RANDOM_GROUP
8
효과 중 여덢째 효과가 임의 효과
RANDOM_TYPE
diagonal
영구 환경 효과이므로 diagonal
FILE
파일 지정하지 않음
(CO)VARIANCES
8.4700
영구 환경 효과의 분산
blupf90 실행 화면
solutions 결과 파일
trait/effect level solution
1 1 1 14.42864952
1 1 2 11.44540510
1 1 3 12.15349915
1 1 4 11.73089165
1 1 5 9.83164400
1 1 6 6.63965057
1 1 7 6.76022401
1 1 8 6.84005017
1 1 9 4.12538085
1 1 10 3.45032758
1 2 1 11.42856202
1 3 1 -0.52287756
1 4 1 -0.12446398
1 5 1 0.53544835
1 6 1 -0.41949562
1 7 1 1.14770055
1 7 2 0.00432843
1 7 3 -0.83674118
1 7 4 0.37860887
1 7 5 -0.24493941
1 7 6 -0.32999236
1 7 7 -0.16040807
1 7 8 0.49039971
1 8 1 2.80888824
1 8 2 -0.61561990
1 8 3 -1.68533255
1 8 4 -0.09280070
1 8 5 -0.41513362
1 8 6 0.00000000
1 8 7 0.00000000
1 8 8 0.00000000
형질 1개, 효과 8개(htd, dim1 ~ dim5, 개체효과, 영구환경효과)
1번 animal은 원래 7번 개체, 개체 육종가는 –0.16040807, 영구환경효과는 2.80888824
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