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### ============================================================================
### colWeightedMeans
###
### ----------------------------------------------------------------------------
### Non-exported methods
###
.DelayedMatrix_block_colWeightedMeans <- function(x, w = NULL, rows = NULL,
cols = NULL, na.rm = FALSE,
..., useNames = TRUE) {
# Check input type
stopifnot(is(x, "DelayedMatrix"))
DelayedArray:::.get_ans_type(x, must.be.numeric = FALSE)
# Check and subset 'w' (must be either NULL, or a numeric vector of
# length 1 or 'nrow(x)')
if (!is.null(w)) {
stopifnot(is.numeric(w))
if (length(w) != 1L) {
stopifnot(length(w) == nrow(x))
if (!is.null(rows))
w <- w[rows]
}
}
# Subset 'x'
x <- ..subset(x, rows, cols)
# Compute result
val <- colblock_APPLY(x = x,
FUN = colWeightedMeans,
w = w,
na.rm = na.rm,
...,
useNames = useNames)
if (length(val) == 0L) {
return(numeric(ncol(x)))
}
unlist(val, recursive = FALSE, use.names = useNames)
}
### ----------------------------------------------------------------------------
### Exported methods
###
# ------------------------------------------------------------------------------
# General method
#
#' @inherit MatrixGenerics::colWeightedMeans
#' @importMethodsFrom DelayedArray seed
#' @rdname colWeightedMeans
#' @template common_params
#' @template lowercase_x
#' @export
#' @template example_dm_MatrixMatrix
#' @author Peter Hickey
#' @examples
#'
#' colWeightedMeans(dm_Matrix)
#' # Specifying weights inversely proportional to rowwise variances
#' colWeightedMeans(dm_Matrix, w = 1 / rowVars(dm_Matrix))
setMethod("colWeightedMeans", "DelayedMatrix",
function(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE,
force_block_processing = FALSE, ..., useNames = TRUE) {
.smart_seed_dispatcher(x, generic = MatrixGenerics::colWeightedMeans,
blockfun = .DelayedMatrix_block_colWeightedMeans,
force_block_processing = force_block_processing,
w = w,
rows = rows,
cols = cols,
na.rm = na.rm,
...,
useNames = useNames)
}
)
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