R/bigstatsr-package.R

In privefl/bigstatsr: Statistical Tools for Filebacked Big Matrices

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#' @useDynLib bigstatsr, .registration = TRUE
#' @importFrom Rcpp sourceCpp
#' @importFrom methods new
#' @import foreach
#'
#' @param X An object of class [FBM][FBM-class].
#' @param X.code An object of class [FBM.code256][FBM.code256-class].
#'
#' @param y.train Vector of responses, corresponding to `ind.train`.
#' @param y01.train Vector of responses, corresponding to `ind.train`.
#'   __Must be only 0s and 1s.__
#'
#' @param 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.__
#' @param ind.row An optional vector of the row indices that are used.
#'   If not specified, all rows are used. __Don't use negative indices.__
#'
#' @param ind.col An optional vector of the column indices that are used.
#'   If not specified, all columns are used. __Don't use negative indices.__
#'
#' @param block.size Maximum number of columns read at once.
#'   Default uses [block_size].
#'
#' @param ncores Number of cores used. Default doesn't use parallelism.
#'   You may use [nb_cores].
#'
#' @param fun.scaling A function with parameters `X`, `ind.row` and `ind.col`,
#'   and that returns a data.frame with `$center` and `$scale` for the columns
#'   corresponding to `ind.col`, to scale each of their elements such as followed:
#'   \deqn{\frac{X_{i,j} - center_j}{scale_j}.} Default doesn't use any scaling.
#'   You can also provide your own `center` and `scale` by using [as_scaling_fun()].
#'
#' @param covar.train Matrix of covariables to be added in each model to correct
#'   for confounders (e.g. the scores of PCA), corresponding to `ind.train`.
#'   Default is `NULL` and corresponds to only adding an intercept to each model.
#'   You can use [covar_from_df()] to convert from a data frame.
#' @param covar.row Matrix of covariables to be added in each model to correct
#'   for confounders (e.g. the scores of PCA), corresponding to `ind.row`.
#'   Default is `NULL` and corresponds to only adding an intercept to each model.
#'   You can use [covar_from_df()] to convert from a data frame.
#'
#' @param center Vector of same length of `ind.col` to subtract from columns of `X`.
#' @param scale Vector of same length of `ind.col` to divide from columns of `X`.
#'
#' @section 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 may use [Microsoft R Open](https://mran.microsoft.com/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()`.
#'
#' @keywords internal
#'
"_PACKAGE"

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