View source: R/crossprodSelf.R
big_cor | R Documentation |
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)) )
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. |
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 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()
.
cor big_crossprodSelf
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)
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