Compute the (Pearson) correlation matrix of a Filebacked Big Matrix.
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"))
)An object of class FBM.
An optional vector of the row indices that are used. If not specified, all rows are used. Don't use negative indices.
An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices.
Maximum number of columns read at once. Default uses block_size.
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".
A temporary FBM, with the following two attributes:
a numeric vector center of column scaling,
a numeric vector scale of column scaling.
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().
X <- FBM(13, 17, init = rnorm(221))
# Comparing with cor
K <- big_cor(X)
class(K)
#> [1] "FBM"
#> attr(,"package")
#> [1] "bigstatsr"
dim(K)
#> [1] 17 17
K$backingfile
#> [1] "C:\\Users\\au639593\\AppData\\Local\\Temp\\RtmpSeLJ92\\file577073804415.bk"
true <- cor(X[])
all.equal(K[], true)
#> [1] 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)
#> [1] TRUE