Description Arguments Matrix parallelization Author(s) See Also
Easy-to-use, efficient, flexible and scalable statistical tools. Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping. It provides for instance matrix operations, Principal Component Analysis, sparse linear supervised models, utility functions and more <doi:10.1093/bioinformatics/bty185>.
X |
An object of class FBM. |
X.code |
An object of class FBM.code256. |
y.train |
Vector of responses, corresponding to |
y01.train |
Vector of responses, corresponding to |
ind.train |
An optional vector of the row indices that are used, for the training part. If not specified, all rows are used. Don't use negative indices. |
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. |
ncores |
Number of cores used. Default doesn't use parallelism. You may use nb_cores. |
fun.scaling |
A function that returns a named list of
\frac{X_{i,j} - mean_j}{sd_j}. Default doesn't use any scaling. |
covar.train |
Matrix of covariables to be added in each model to correct
for confounders (e.g. the scores of PCA), corresponding to |
covar.row |
Matrix of covariables to be added in each model to correct
for confounders (e.g. the scores of PCA), corresponding to |
center |
Vector of same length of |
scale |
Vector of same length of |
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()
.
Maintainer: Florian PrivĂ© florian.prive.21@gmail.com
Other contributors:
Michael Blum michael.blum@univ-grenoble-alpes.fr [thesis advisor]
Hugues Aschard hugues.aschard@pasteur.fr [thesis advisor]
Useful links:
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