First alignment routine which create the subtree founded on a single subject as though it were the only tree.
Arguments
- idx
Indexes of the subjects
- dadx
Indexes of the fathers
- momx
Indexes of the mothers
- level
Vector of the level of each subject
- horder
A named numeric vector with one element per subject in the Pedigree. It determines the relative horizontal order of subjects within a sibship, as well as the relative order of processing for the founder couples. (For this latter, the female founders are ordered as though they were sisters). The names of the vector should be the individual identifiers.
- packed
Should the Pedigree be compressed. (i.e. allow diagonal lines connecting parents to children in order to have a smaller overall width for the plot.)
- spouselist
Matrix of spouses with 4 columns:
1: husband index2: wife index3: husband anchor4: wife anchor
Value
A list containing the elements to plot the Pedigree. It contains a set of matrices along with the spouselist matrix. The latter has marriages removed as they are processed.
n: A vector giving the number of subjects on each horizonal level of the plotnid: A matrix with one row for each level, giving the numeric id of each subject plotted. (A value of17means the 17th subject in the Pedigree).pos: A matrix giving the horizontal position of each plot pointfam: A matrix giving the family id of each plot point. A value of3would mean that the two subjects in positions 3 and 4, in the row above, are this subject's parents.spouselist: Spouse matrix with anchors informations
Details
In this routine the nid array consists of the final
nid array + 1/2 of the final spouse array.
Note that the spouselist matrix will only contain spouse pairs
that are not yet processed. The logic for anchoring is slightly tricky.
1. Anchoring:
First, if col 4 of the spouselist matrix is 0, we anchor at the first
opportunity. Also note that if spouselist[, 3] == spouselist[, 4]
it is the husband who is the anchor (just write out the possibilities).
2. Return values initialization:
Create the set of 3 return structures, which will be matrices
with 1 + nspouse columns.
If there are children then other routines will widen the result.
3. Create lspouse and rspouse:
This two complimentary lists denote the spouses plotted on the left
and on the right.
For someone with lots of spouses we try to split them evenly.
If the number of spouses is odd, then men should have more on
the right than on the left, women more on the right.
Any hints in the spouselist matrix override.
We put the undecided marriages closest to idx, then add
predetermined ones to the left and right. The majority of marriages will
be undetermined singletons, for which nleft will be 1 for
female (put my husband to the left) and 0 for male. In one bug found
by plotting canine data, lspouse could initially be empty but
length(rspouse) > 1. This caused nleft > length(indx).
A fix was to not let indx to be indexed beyond its length,
fix by JPS 5/2013.
4. List the children:
For each spouse get the list of children. If there are any we
call alignped2() to generate their tree and
then mark the connection to their parent.
If multiple marriages have children we need to join the trees.
5. Splice the tree:
To finish up we need to splice together the tree made up from
all the kids, which only has data from lev + 1 down, with the data here.
There are 3 cases:
No children were found.
The tree below is wider than the tree here, in which case we add the data from this level onto theirs.
The tree below is narrower, for instance an only child.
Examples
data(sampleped)
ped <- Pedigree(sampleped)
align(ped)
#> $`1`
#> $`1`$n
#> [1] 2 10 16 14
#>
#> $`1`$nid
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 35 36 0 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 1 2 3 4 37 38 5 6 7 8 0 0 0 0
#> [3,] 9 10 11 12 14 39 40 41 14 15 12 18 17 16
#> [4,] 21 22 23 24 27 28 25 26 29 30 31 32 33 34
#> [,15] [,16]
#> [1,] 0 0
#> [2,] 0 0
#> [3,] 19 20
#> [4,] 0 0
#>
#> $`1`$pos
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 3.8 4.8 0.0 0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
#> [2,] 0.0 1.0 2.8 3.8 4.80 5.80 11.25 12.25 14.01 15.01 0.00 0.00 0.00
#> [3,] 0.0 1.0 2.0 3.0 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00
#> [4,] 0.0 1.0 2.0 3.0 6.01 7.01 8.01 9.01 10.01 11.01 12.01 13.01 14.01
#> [,14] [,15] [,16]
#> [1,] 0.00 0 0
#> [2,] 0.00 0 0
#> [3,] 13.00 14 15
#> [4,] 15.01 0 0
#>
#> $`1`$fam
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 1 0 0 1 0 0 0 0 0 0 0 0
#> [3,] 1 3 3 3 3 5 5 5 0 7 0 7 0 7
#> [4,] 1 1 1 1 9 9 11 11 13 15 15 15 15 15
#> [,15] [,16]
#> [1,] 0 0
#> [2,] 0 0
#> [3,] 7 9
#> [4,] 0 0
#>
#> $`1`$spouse
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 1 0 1 0 1 0 1 0 1 0 0 0 0 0
#> [3,] 1 0 0 0 0 0 0 0 1 0 1 0 1 0
#> [4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [,15] [,16]
#> [1,] 0 0
#> [2,] 0 0
#> [3,] 1 0
#> [4,] 0 0
#>
#>
#> $`2`
#> $`2`$n
#> [1] 2 7 5
#>
#> $`2`$nid
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1 2 0 0 0 0 0
#> [2,] 3 4 5 6 7 9 8
#> [3,] 10 11 12 13 14 0 0
#>
#> $`2`$pos
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 2.7 3.7 0.0 0.0 0.0 0 0
#> [2,] 0.0 1.0 2.0 3.0 4.0 5 6
#> [3,] 0.0 1.0 4.5 5.5 6.5 0 0
#>
#> $`2`$fam
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 0 0 0 0 0 0 0
#> [2,] 0 1 1 1 1 0 1
#> [3,] 1 1 6 6 6 0 0
#>
#> $`2`$spouse
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1 0 0 0 0 0 0
#> [2,] 1 0 0 0 0 1 0
#> [3,] 0 0 0 0 0 0 0
#>
#>