big_crossprodSelf.Rd
Compute \(X.row^T X.row\) for a Filebacked Big Matrix X
after applying a particular scaling to it.
big_crossprodSelf( 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 FBM,missing crossprod(x, y)
X  An object of class FBM. 

fun.scaling  A function that returns a named list of

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. 
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 blockwise and won't be parallelized
in order to not have to reduce the size of these blocks.
Instead, you may use Microsoft R Open
or OpenBLAS in order to accelerate these block matrix computations.
You can also control the number of cores used with
bigparallelr::set_blas_ncores()
.
X < FBM(13, 17, init = rnorm(221)) true < crossprod(X[]) # No scaling K1 < crossprod(X) class(K1)#> [1] "matrix"#> [1] TRUE#> [1] "FBM" #> attr(,"package") #> [1] "bigstatsr"K2$backingfile#> [1] "C:\\Users\\au639593\\AppData\\Local\\Temp\\Rtmpq8tKLv\\file2e3479b864e2.bk"#> [1] TRUE# big_crossprodSelf() provides some scaling and subsetting # Example using only half of the data: n < nrow(X) ind < sort(sample(n, n/2)) K3 < big_crossprodSelf(X, fun.scaling = big_scale(), ind.row = ind) true2 < crossprod(scale(X[ind, ])) all.equal(K3[], true2)#> [1] TRUE