The OUTCOV= data set has the following variables:
a list of BY variables, if there is a BY statement
the generation variable, if there is a CLASS statement
the gender variable, if there is a GENDER statement
_Type_
, a variable indicating the type of observation. The valid values of the _Type_
variable are ‘COV’ for covariance estimates and ‘INBREED’ for inbreeding coefficients.
_Panel_
, a variable indicating the panel number used when populations delimited by BY groups contain different numbers of individuals.
If there are n individuals in the first BY group and if any subsequent BY group contains a larger population, then its covariance/inbreeding
matrix is divided into panels, with each panel containing n columns of data. If you put these panels side by side in increasing _Panel_
number order, then you can reconstruct the covariance or inbreeding matrix.
_Col_
, a variable used to name columns of the inbreeding or covariance matrix. The values of this variable start with ‘COL’, followed
by a number indicating the column number. The names of the individuals corresponding to any given column i can be found by reading the individual’s name across the row that has a _Col_
value of ‘COLi’. When the inbreeding or covariance matrix is divided into panels, all the rows repeat for the first n columns, all the rows repeat for the next n columns, and so on.
the variable containing the names of the individuals, that is, the first variable listed in the VAR statement
the variable containing the names of the first parents, that is, the second variable listed in the VAR statement
the variable containing the names of the second parents, that is, the third variable listed in the VAR statement
a list of covariance variables Col1
–Col
n, where n is the maximum number of individuals in the first population
The functions of the variables _Panel_
and _Col_
can best be demonstrated by an example. Assume that there are three individuals in the first BY group and that, in the current
BY group (Byvar
=2), there are five individuals with the following covariance matrix.
COV 
1 
2 
3 
4 
5 

1 
Cov(1,1) 
Cov(1,2) 
Cov(1,3) 
Cov(1,4) 
Cov(1,5) 
2 
Cov(2,1) 
Cov(2,2) 
Cov(2,3) 
Cov(2,4) 
Cov(2,5) 
3 
Cov(3,1) 
Cov(3,2) 
Cov(3,3) 
Cov(3,4) 
Cov(3,5) 
4 
Cov(4,1) 
Cov(4,2) 
Cov(4,3) 
Cov(4,4) 
Cov(4,5) 
5 
Cov(5,1) 
Cov(5,2) 
Cov(5,3) 
Cov(5,4) 
Cov(5,5) 
Panel 1 
Panel 2 
Then the OUTCOV= data set appears as follows.










2 
1 
COL1 
1 
Cov(1,1) 
Cov(1,2) 
Cov(1,3) 

2 
1 
COL2 
2 
Cov(2,1) 
Cov(2,2) 
Cov(2,3) 

2 
1 
COL3 
3 
Cov(3,1) 
Cov(3,2) 
Cov(3,3) 

2 
1 
4 
Cov(4,1) 
Cov(4,2) 
Cov(4,3) 

2 
1 
5 
Cov(5,1) 
Cov(5,2) 
Cov(5,3) 

2 
2 
1 
Cov(1,4) 
Cov(1,5) 
. 

2 
2 
2 
Cov(2,4) 
Cov(2,5) 
. 

2 
2 
3 
Cov(3,4) 
Cov(3,5) 
. 

2 
2 
COL1 
4 
Cov(4,4) 
Cov(4,5) 
. 

2 
2 
COL2 
5 
Cov(5,4) 
Cov(5,5) 
. 
Notice that the first three columns go to the first panel (_Panel_
=1), and the remaining two go to the second panel (_Panel_
=2). Therefore, in the first panel, ‘COL1’, ‘COL2’, and ‘COL3’ correspond to individuals 1, 2, and 3, respectively, while
in the second panel, ‘COL1’ and ‘COL2’ correspond to individuals 4 and 5, respectively.