| colMedians {DelayedMatrixStats} | R Documentation |
Calculates the median for each row (column) in a matrix.
colMedians(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ...) rowMedians(x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), ...) ## S4 method for signature 'DelayedMatrix' colMedians( x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), force_block_processing = FALSE, ... ) ## S4 method for signature 'DelayedMatrix' rowMedians( x, rows = NULL, cols = NULL, na.rm = FALSE, dim. = dim(x), force_block_processing = FALSE, ... )
x |
A NxK DelayedMatrix. |
rows |
A |
cols |
A |
na.rm |
|
dim. |
An |
... |
Additional arguments passed to specific methods. |
force_block_processing |
|
The implementation of rowMedians() and colMedians() is
optimized for both speed and memory. To avoid coercing to
doubles (and hence memory allocation), there is a
special implementation for integer matrices. That is,
if x is an integer matrix,
then rowMedians(as.double(x)) (rowMedians(as.double(x))) would
require three times the memory of rowMedians(x)
(colMedians(x)), but all this is avoided.
Returns a numeric vector of
length N (K).
Peter Hickey
See rowWeightedMedians() and
colWeightedMedians() for weighted medians.
For mean estimates, see rowMeans2() and
rowMeans().
# A DelayedMatrix with a 'Matrix' seed
dm_Matrix <- DelayedArray(Matrix::Matrix(c(rep(1L, 5),
as.integer((0:4) ^ 2),
seq(-5L, -1L, 1L)),
ncol = 3))
colMedians(dm_Matrix)
rowMedians(dm_Matrix)