ASReml-R: Storing A inverse as a sparse matrix
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I was testing ASReml-R program (an R package that links propriety ASReml binaries that can be used only with valid licence) this week and had to do some manipulations with the numerator relationship matrix (A). ASReml-R provides a function (asreml.Ainverse) that can create inverse of A directly from the pedigree as this inverse is needed in pedigree based mixed model. Bulding inverse of A directly from a pedigree is a well known result dating back to Henderson in 1970s or so. The funny thing is that it is cheaper to setup inverse of A directly than to setup up first A and then to invert it. In addition, inverse of A is very spare so it is easy/cheap to store it. Documentation for asreml.Ainverse has a tiny example of usage. Since the result of this function is a list with several elements (data.frame with “triplets” for non-zero elements of inverse of A, inbreeding coefficients, …) example also shows how to create a matrix object in R as shown bellow:
library(package="asreml")
## Create test pedigree
ped <- data.frame( me=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
dad=c(0, 0, 0, 1, 1, 2, 4, 5, 7, 9),
mum=c(0, 0, 0, 1, 1, 2, 6, 6, 8, 9))
## Create inverse of A in triplet form
tmp <- asreml.Ainverse(pedigree=ped)$ginv
## Create a "proper" matrix
AInv <- asreml.sparse2mat(x=tmp)
## Print AInv
AInvSo the inverse of A would be:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 5 0 0 -2 -2 0 0.0 0.0 0.000000 0.000000 [2,] 0 3 0 0 0 -2 0.0 0.0 0.000000 0.000000 [3,] 0 0 1 0 0 0 0.0 0.0 0.000000 0.000000 [4,] -2 0 0 3 0 1 -2.0 0.0 0.000000 0.000000 [5,] -2 0 0 0 3 1 0.0 -2.0 0.000000 0.000000 [6,] 0 -2 0 1 1 4 -2.0 -2.0 0.000000 0.000000 [7,] 0 0 0 -2 0 -2 4.5 0.5 -1.000000 0.000000 [8,] 0 0 0 0 -2 -2 0.5 4.5 -1.000000 0.000000 [9,] 0 0 0 0 0 0 -1.0 -1.0 4.909091 -2.909091 [10,] 0 0 0 0 0 0 0.0 0.0 -2.909091 2.909091
However, this is problematic as it creates a dense matrix - zero values are also stored (you can see them). If we would have 1,000 individuals, such a matrix would consume 7.6 Mb of RAM (= (((1000 * (1000 + 1)) / 2) * 16) / 2^20). This is not a lot, but with 10,000 individuals we would already need 763 Mb of RAM, which can create some bottlenecks. A solution is to create a sparse matrix using the Matrix R package. Luckily we have all the ingredients prepared by asreml.Ainverse function - the triplets. However, the essential R code is a bit daunting and I had to test several options before I figured it out - code from my previous post helped;)
## Load package
library(package="Matrix")
## Number of pedigree members
nI <- nrow(ped)
## Store inverse of A in sparse form
AInv2 <- as(new("dsTMatrix",
Dim=c(nI, nI),
uplo="L",
i=(as.integer(tmp$Row) - 1L),
j=(as.integer(tmp$Column) - 1L),
x=tmp$Ainverse),
"dsCMatrix")
## Add row and column names - optional
dimnames(AInv2) <- list(attr(x=tmp, which="rowNames"),
attr(x=tmp, which="rowNames"))
## Print AInv
AInv2
And the inverse of A is now:
10 x 10 sparse Matrix of class "dsCMatrix"
[[ suppressing 10 column names ‘1’, ‘2’, ‘3’ ... ]]
1 5 . . -2 -2 . . . . .
2 . 3 . . . -2 . . . .
3 . . 1 . . . . . . .
4 -2 . . 3 . 1 -2.0 . . .
5 -2 . . . 3 1 . -2.0 . .
6 . -2 . 1 1 4 -2.0 -2.0 . .
7 . . . -2 . -2 4.5 0.5 -1.000000 .
8 . . . . -2 -2 0.5 4.5 -1.000000 .
9 . . . . . . -1.0 -1.0 4.909091 -2.909091
10 . . . . . . . . -2.909091 2.909091you can clearly see the structure and it soon becomes obvious why such a storage is more efficient.
If we want to go back from matrix to triplet form (this might be useful if we want to create a matrix for programs as ASReml) we can use the following code:
## Convert back to triplet form - first the matrix tmp2 <- as(AInv2, "dsTMatrix") ## ... - now to data.frame tmp3 <- data.frame(Row=tmp2@i + 1, Column=tmp2@j + 1, Ainverse=tmp2@x) ## Sort tmp3 <- tmp3[order(tmp3$Row, tmp3$Column), ] ## Test that we get the same stuff any((tmp[, 3] - tmp3[, 3]) > 0) ## ASReml-R specificities attr(x=tmp3, which="rowNames") <- rownames(tmp) attr(x=tmp3, which="geneticGroups") <- c(0, 0)
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