colMedians | R Documentation |
Calculates the median for each row (column) of a matrix x
.
This is the same as but more efficient than apply(x, MM, median)
for MM=2 or MM=1, respectively.
colMedians(x, na.rm = FALSE, hasNA = TRUE, keep.names=TRUE) rowMedians(x, na.rm = FALSE, hasNA = TRUE, keep.names=TRUE)
x |
a |
na.rm |
if |
hasNA |
logical indicating if |
keep.names |
logical indicating if row or column names of |
The implementation of rowMedians()
and colMedians()
is optimized for both speed and memory.
To avoid coercing to double
s (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.
a numeric
vector of length n or p, respectively.
Missing values are excluded before calculating the medians
unless hasNA
is false. Note that na.rm
has no
effect and is automatically false when hasNA
is false, i.e.,
internally, before computations start, the following is executed:
if (!hasNA) ## If there are no NAs, don't try to remove them narm <- FALSE
Henrik Bengtsson, Harris Jaffee, Martin Maechler
See wgt.himedian()
for a weighted hi-median, and
colWeightedMedians()
etc from package
matrixStats for weighted medians.
For mean estimates, see rowMeans()
in colSums
().
set.seed(1); n <- 234; p <- 543 # n*p = 127'062 x <- matrix(rnorm(n*p), n, p) x[sample(seq_along(x), size= n*p / 256)] <- NA R1 <- system.time(r1 <- rowMedians(x, na.rm=TRUE)) C1 <- system.time(y1 <- colMedians(x, na.rm=TRUE)) R2 <- system.time(r2 <- apply(x, 1, median, na.rm=TRUE)) C2 <- system.time(y2 <- apply(x, 2, median, na.rm=TRUE)) R2 / R1 # speedup factor: ~= 4 {platform dependent} C2 / C1 # speedup factor: ~= 5.8 {platform dependent} stopifnot(all.equal(y1, y2, tol=1e-15), all.equal(r1, r2, tol=1e-15)) (m <- cbind(x1=3, x2=c(4:1, 3:4,4))) stopifnot(colMedians(m) == 3, all.equal(colMeans(m), colMedians(m)),# <- including names ! all.equal(rowMeans(m), rowMedians(m)))
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