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Title:

Represent and Use Sparse + Low Rank Matrices

Version:

0.1.0

Description:

Provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects.

License:

MIT + file LICENSE

URL:

https://rohelab.github.io/sparseLRMatrix/, https://github.com/RoheLab/sparseLRMatrix

BugReports:

https://github.com/RoheLab/sparseLRMatrix/issues

Depends:

Matrix, methods

Imports:

RSpectra

Suggests:

covr, testthat (≥ 3.0.0)

Config/testthat/edition:

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.1.1.9000

NeedsCompilation:

Packaged:

2021-03-01 02:58:02 UTC; alex

Author:

Alex Hayes

[aut, cre, cph]

Maintainer:

Alex Hayes <alexpghayes@gmail.com>

Repository:

CRAN

Date/Publication:

2021-03-02 10:50:02 UTC

Check the dimension of a sparseLRMatrix

Description

Check the dimension of a sparseLRMatrix

Usage

## S4 method for signature 'sparseLRMatrix'
dim(x)

Arguments

x

A sparseLRMatrix object.

Value

Dimension of x.

Examples


set.seed(528491)

n <- 50
m <- 40
k <- 3

A <- rsparsematrix(n, m, 0.1)

U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)

# construct the matrix, which represents A + U %*% t(V)
X <- sparseLRMatrix(sparse = A, U = U, V = V)

dim(X)

s <- svds(X, 5)  # efficient

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

RSpectra: svds

Create a sparse plus low rank matrix

Description

Create a sparse plus low rank matrix

Usage

sparseLRMatrix(sparse, U, V)

Arguments

sparse

sparseMatrix.

U

Matrix.

V

Matrix.

Value

A sparseLRMatrix S4 object.

Examples


set.seed(528491)

n <- 50
m <- 40
k <- 3

A <- rsparsematrix(n, m, 0.1)

U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)

# construct the matrix, which represents A + U %*% t(V)
X <- sparseLRMatrix(sparse = A, U = U, V = V)

dim(X)

s <- svds(X, 5)  # efficient

Sparse plus low rank matrix

Description

Eventually this class will subclass Matrix objects, but for now this is a basic implementation that essentially only supports singular value decomposition.

Details

To learn more about S4 classes, please see https://adv-r.hadley.nz/s4.html.

Slots

sparse: sparseMatrix.
U: Matrix.
V: Matrix.

Examples


set.seed(528491)

n <- 50
m <- 40
k <- 3

A <- rsparsematrix(n, m, 0.1)

U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)

# construct the matrix, which represents A + U %*% t(V)
X <- sparseLRMatrix(sparse = A, U = U, V = V)

dim(X)

s <- svds(X, 5)  # efficient

Truncated singular value decomposition of a matrix

Description

A thin wrapper around RSpectra::svds(), please see more detailed documentation there. In particular, this function leverages the function interface.

Usage

## S3 method for class 'sparseLRMatrix'
svds(A, k, nu = k, nv = k, opts = list(), ...)

Arguments

A

Matrix to decompose.

k

Number of singular values to estimate.

nu

Number of left singular vectors to estimate.

nv

Number of right singular vectors to estimate.

opts

Passed to RSpectra::svds().

...

Passed to RSpectra::svds().

Value

A list with the following components:

d

A vector of the computed singular values.

u

An m by nu matrix whose columns contain the left singular vectors. If nu == 0, NULL will be returned.

v

An n by nv matrix whose columns contain the right singular vectors. If nv == 0, NULL will be returned.

nconv

Number of converged singular values.

niter

Number of iterations used.

nops

Number of matrix-vector multiplications used.

Examples


set.seed(528491)

n <- 50
m <- 40
k <- 3

A <- rsparsematrix(n, m, 0.1)

U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)

X <- sparseLRMatrix(sparse = A, U = U, V = V)

svds(X, 5)