runExactSVD: Exact SVD
In BiocSingular: Singular Value Decomposition for Bioconductor Packages

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/runExactSVD.R

Perform an exact singular value decomposition.

1 2	runExactSVD(x, k=min(dim(x)), nu=k, nv=k, center=FALSE, scale=FALSE, deferred=FALSE, fold=Inf, BPPARAM=SerialParam())

`x`	A numeric matrix-like object to use in the SVD.
`k`	Integer scalar specifying the number of singular values to return.
`nu`	Integer scalar specifying the number of left singular vectors to return.
`nv`	Integer scalar specifying the number of right singular vectors to return.
`center`	A logical scalar indicating whether columns should be centered. Alternatively, a numeric vector or `NULL` - see `?"BiocSingular-options"`.
`scale`	A logical scalar indicating whether columns should be scaled. Alternatively, a numeric vector or `NULL` - see `?"BiocSingular-options"`.
`deferred`	Logical scalar indicating whether centering/scaling should be deferred, see `?"BiocSingular-options"`.
`fold`	Numeric scalar specifying the minimum fold difference between dimensions of `x` to compute the cross-product, see `?"BiocSingular-options"`.
`BPPARAM`	A BiocParallelParam object specifying how parallelization should be performed.

If any of k, nu or nv exceeds min(dim(x)), they will be capped and a warning will be raised. The exception is when they are explicitly set to Inf, in which case all singular values/vectors of x are returned without any warning.

Note that parallelization via BPPARAM is only applied to the calculation of the cross-product. It has no effect for near-square matrices where the SVD is computed directly.

A list containing:

d, a numeric vector of the first k singular values.
u, a numeric matrix with nrow(x) rows and nu columns. Each column contains a left singular vector.
u, a numeric matrix with ncol(x) rows and nv columns. Each column contains a right singular vector.

Aaron Lun

svd for the underlying algorithm.

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a <- matrix(rnorm(100000), ncol=20)
out <- runExactSVD(a)
str(out)

List of 3
 $ d: num [1:20] 75.1 74.7 73.8 73.4 72.5 ...
 $ u: num [1:5000, 1:20] -0.019406 -0.023272 -0.008959 0.004626 0.000333 ...
 $ v: num [1:20, 1:20] -0.133 0.016 0.118 0.177 -0.216 ...