colMeans2 {DelayedMatrixStats} | R Documentation |
Calculates the mean for each row (column) in a matrix.
colMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ...) rowMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ...) ## S4 method for signature 'DelayedMatrix' colMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), force_block_processing = FALSE, ...) ## S4 method for signature 'Matrix' colMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ...) ## S4 method for signature 'SolidRleArraySeed' colMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ...) ## S4 method for signature 'DelayedMatrix' rowMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), force_block_processing = FALSE, ...) ## S4 method for signature 'Matrix' rowMeans2(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ...)
x |
A NxK DelayedMatrix. |
rows |
A |
cols |
A |
na.rm |
|
dim. |
An |
... |
Additional arguments passed to specific methods. |
force_block_processing |
|
The implementation of rowMeans2()
and colMeans2()
is
optimized for both speed and memory.
Returns a numeric
vector
of
length N (K).
# A DelayedMatrix with a 'matrix' seed dm_matrix <- DelayedArray(matrix(c(rep(1L, 5), as.integer((0:4) ^ 2), seq(-5L, -1L, 1L)), ncol = 3)) # A DelayedMatrix with a 'SolidRleArraySeed' seed dm_Rle <- RleArray(Rle(c(rep(1L, 5), as.integer((0:4) ^ 2), seq(-5L, -1L, 1L))), dim = c(5, 3)) colMeans2(dm_matrix) # NOTE: Temporarily use verbose output to demonstrate which method is # which method is being used options(DelayedMatrixStats.verbose = TRUE) # By default, this uses a seed-aware method for a DelayedMatrix with a # 'SolidRleArraySeed' seed rowMeans2(dm_Rle) # Alternatively, can use the block-processing strategy rowMeans2(dm_Rle, force_block_processing = TRUE) options(DelayedMatrixStats.verbose = FALSE)