big_cor: Correlation

Description

Compute the (Pearson) correlation matrix of a Filebacked Big Matrix.

Usage

big_cor(
  X,
  ind.row = rows_along(X),
  ind.col = cols_along(X),
  block.size = block_size(nrow(X)),
  backingfile = tempfile(tmpdir = getOption("FBM.dir"))
)

Value

A temporary FBM, with the following two attributes:

a numeric vector center of column scaling,
a numeric vector scale of column scaling.

Arguments

X: An object of class FBM.
ind.row: An optional vector of the row indices that are used. If not specified, all rows are used. Don't use negative indices.
ind.col: An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices.
block.size: Maximum number of columns read at once. Default uses block_size.
backingfile: Path to the file storing the FBM data on disk. An extension ".bk" will be automatically added. Default stores in the temporary directory, which you can change using global option "FBM.dir".

Matrix parallelization

Large matrix computations are made block-wise and won't be parallelized in order to not have to reduce the size of these blocks. Instead, you can use the MKL or OpenBLAS in order to accelerate these block matrix computations. You can control the number of cores used by these optimized matrix libraries with bigparallelr::set_blas_ncores().

Examples

Run this code

X <- FBM(13, 17, init = rnorm(221))

# Comparing with cor
K <- big_cor(X)
class(K)
dim(K)
K$backingfile

true <- cor(X[])
all.equal(K[], true)

# Using only half of the data
n <- nrow(X)
ind <- sort(sample(n, n/2))
K2 <- big_cor(X, ind.row = ind)

true2 <- cor(X[ind, ])
all.equal(K2[], true2)