Eigendecomposition-Based LD Statistic

Pre-computed per-block eigendecompositions of the LD correlation matrix. Used by LDER, HDL and sHDL.

Unlike LdScore, whose statistics are per variant, an eigendecomposition describes a whole LD block. It therefore stays in a slot rather than mcols – and because a slot does not narrow when the ranges do, subsetting an LdEigen is refused rather than allowed to produce an object whose decompositions no longer describe its variants.

Usage

# S4 method for class 'LdEigen,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

Arguments

x

An LdEigen.

i, j, ...

Subscripts; any use is an error.

drop

Ignored.

Value

Nothing: this method always signals an error.

Functions

• x = LdEigen[i = ANY, j = ANY, drop = ANY]: Refused. Subsetting would narrow the variants while eigenList – a slot, because it is per block rather than per variant – stayed as it was, leaving decompositions that describe variants the object no longer has. Recompute over the subset instead.

Slots

eigenList

A list of length nBlocks, each element a list with components:

values

Numeric vector of eigenvalues

vectors

Numeric matrix of eigenvectors (SNPs x retained components)

snpIdx

Integer vector of variant indices

eigenvalueTruncation

Numeric, proportion of variance retained (e.g. 0.9 for HDL's default). If 1.0, no truncation.