# sweep

0th

Percentile

##### Sweep out Array Summaries

Return an array obtained from an input array by sweeping out a summary statistic.

Keywords
array, iteration
##### Usage
sweep(x, MARGIN, STATS, FUN = "-", check.margin = TRUE, …)
##### Arguments
x

an array.

MARGIN

a vector of indices giving the extent(s) of x which correspond to STATS.

STATS

the summary statistic which is to be swept out.

FUN

the function to be used to carry out the sweep.

check.margin

logical. If TRUE (the default), warn if the length or dimensions of STATS do not match the specified dimensions of x. Set to FALSE for a small speed gain when you know that dimensions match.

optional arguments to FUN.

##### Details

FUN is found by a call to match.fun. As in the default, binary operators can be supplied if quoted or backquoted.

FUN should be a function of two arguments: it will be called with arguments x and an array of the same dimensions generated from STATS by aperm.

The consistency check among STATS, MARGIN and x is stricter if STATS is an array than if it is a vector. In the vector case, some kinds of recycling are allowed without a warning. Use sweep(x, MARGIN, as.array(STATS)) if STATS is a vector and you want to be warned if any recycling occurs.

##### Value

An array with the same shape as x, but with the summary statistics swept out.

##### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

apply on which sweep used to be based; scale for centering and scaling.
library(base) # NOT RUN { require(stats) # for median med.att <- apply(attitude, 2, median) sweep(data.matrix(attitude), 2, med.att) # subtract the column medians ## More sweeping: A <- array(1:24, dim = 4:2) ## no warnings in normal use sweep(A, 1, 5) (A.min <- apply(A, 1, min)) # == 1:4 sweep(A, 1, A.min) sweep(A, 1:2, apply(A, 1:2, median)) ## warnings when mismatch sweep(A, 1, 1:3) # STATS does not recycle sweep(A, 1, 6:1) # STATS is longer ## exact recycling: sweep(A, 1, 1:2) # no warning sweep(A, 1, as.array(1:2)) # warning # }