binMeans: Fast mean calculations in non-overlapping bins

In HenrikBengtsson/matrixStats: Functions that Apply to Rows and Columns of Matrices (and to Vectors)

Fast mean calculations in non-overlapping bins

Description

Computes the sample means in non-overlapping bins

Usage

binMeans(y, x, idxs = NULL, bx, na.rm = TRUE, count = TRUE,
  right = FALSE, ...)

Arguments

y	A numeric or logical vector of K values to calculate means on.
x	A numeric vector of K positions for to be binned.
idxs	A vector indicating subset of elements to operate over. If NULL, no subsetting is done.
bx	A numeric vector of B + 1 ordered positions specifying the B > 0 bins [bx[1], bx[2]), [bx[2], bx[3]), ..., [bx[B], bx[B + 1]).
na.rm	If TRUE, missing values in y are dropped before calculating the mean, otherwise not.
count	If TRUE, the number of data points in each bins is returned as attribute count, which is an integer vector of length B.
right	If TRUE, the bins are right-closed (left open), otherwise left-closed (right open).
...	Not used.

Details

binMeans(x, bx, right = TRUE) gives equivalent results as rev(binMeans(-x, bx = sort(-bx), right = FALSE)), but is faster.

Value

Returns a numeric vector of length B.

Missing and non-finite values

Data points where either of y and x is missing are dropped (and therefore are also not counted). Non-finite values in y are not allowed and gives an error. Missing values in bx are not allowed and gives an error.

Author(s)

Henrik Bengtsson with initial code contributions by Martin Morgan [1].

References

[1] R-devel thread Fastest non-overlapping binning mean function out there? on Oct 3, 2012

See Also

binCounts(). aggregate and mean().

Examples

x <- 1:200
mu <- double(length(x))
mu[1:50] <- 5
mu[101:150] <- -5
y <- mu + rnorm(length(x))

# Binning
bx <- c(0, 50, 100, 150, 200) + 0.5
y_s <- binMeans(y, x = x, bx = bx)

plot(x, y)
for (kk in seq_along(y_s)) {
  lines(bx[c(kk, kk + 1)], y_s[c(kk, kk)], col = "blue", lwd = 2)
}

HenrikBengtsson/matrixStats documentation built on Nov. 15, 2024, 3:20 p.m.