big_tcrossprodSelf {bigstatsr} | R Documentation |
Tcrossprod
Description
Compute X.row X.row^T
for a Filebacked Big Matrix X
after applying a particular scaling to it.
Usage
big_tcrossprodSelf(
X,
fun.scaling = big_scale(center = FALSE, scale = FALSE),
ind.row = rows_along(X),
ind.col = cols_along(X),
block.size = block_size(nrow(X))
)
## S4 method for signature 'FBM,missing'
tcrossprod(x, y)
Arguments
X |
An object of class FBM. |
fun.scaling |
A function with parameters
Default doesn't use any scaling.
You can also provide your own |
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. |
x |
A 'double' FBM. |
y |
Missing. |
Value
A temporary FBM, with the following two attributes:
a numeric vector
center
of column scaling,a numeric vector
scale
of column scaling.
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()
.
See Also
Examples
X <- FBM(13, 17, init = rnorm(221))
true <- tcrossprod(X[])
# No scaling
K1 <- tcrossprod(X)
class(K1)
all.equal(K1, true)
K2 <- big_tcrossprodSelf(X)
class(K2)
K2$backingfile
all.equal(K2[], true)
# big_tcrossprodSelf() provides some scaling and subsetting
# Example using only half of the data:
n <- nrow(X)
ind <- sort(sample(n, n/2))
K3 <- big_tcrossprodSelf(X, fun.scaling = big_scale(), ind.row = ind)
true2 <- tcrossprod(scale(X[ind, ]))
all.equal(K3[], true2)