imreduce: Built-in reductions

In imply: Efficiently Apply Functions to Images

Built-in reductions

Description

imreduce() computes a summary over the margins of an image without calling back into R for each one. That matters for two reasons: there is no per-call interpreter overhead, and (since nothing touches R) the loop can run on worker threads. imapply() recognises the equivalent base functions and routes calls here automatically where the answer has been shown to be the same.

Usage

imreduce(x, margin, what, na.rm = FALSE, threads = NULL)

Arguments

x	An image, or any array.
margin	The dimensions to retain, as for base::apply().
what	The reduction: one of "sum", "mean", "min", "max", "range", "prod", "var", "sd", "which.min", "which.max", "any", "all" or "countNA".
na.rm	Whether to ignore missing values.
threads	Number of threads, or NULL for the default. See parallelism.

Details

Accumulation is done in double mode whatever the values are stored as, so the answer does not depend on the storage type and error does not compound with the number of values.

Note that for the arithmetic reductions this may not agree with the base equivalent to the last bit: R accumulates sum() and mean() in long double, which on some platforms is wider than double. Differences are of the order of 1e-16. imapply() therefore routes automatically only where the answers are provably identical (min, max, range, which.min and which.max) and leaves the rest to be asked for deliberately.

"var" is the variance of the values, as var(as.vector(x)) would give. It is not stats::var() applied to a sub-array, which for a matrix returns a covariance matrix.

Value

A vector or array, shaped over margin as base::apply() would shape it. "range" yields two values per call, so it gains a leading dimension.

imply documentation built on Sept. 15, 2026, 5:09 p.m.