Cores used by the package

Sets or reads the number of cores that regsen_bounds(), regsen_breakdown(), regsen_multi() and regsen_boot() use by default. Each of those also takes an ncores argument for a single call; this sets the session-wide default those arguments fall back to, held in options(regsensitivity.ncores).

Usage

regsen_cores(n = NULL)

Arguments

n

One of:

• NULL (default): read the current setting without changing it.

• a positive integer: use that many cores.

• "auto": use parallel::detectCores() - 2, and at least 1. Two are left free so the session, and whatever else the machine is doing, stay responsive.

Whatever is set, at most 2 are used while R CMD check --as-cran is running, which forbids more.

Value

The number of cores in effect after the call, invisibly when n is given.

Details

Parallel runs return exactly what serial runs return. The identified set and the breakdown frontiers draw no random numbers, and the bootstrap seeds each replicate from a vector drawn once up front, so nothing depends on how the work was divided.

On macOS and Linux the work is forked with parallel::mclapply(), so the data and model need no copying. Windows has no fork; it gets a PSOCK cluster, which is slower to start and pays to ship the data once, so there the gain is only worth having for long jobs (a bootstrap, or a finite-rybar sweep with many grid points).

What is parallel: the grid points of a regsen_bounds() sweep (only the finite-rybar regime costs anything; the closed forms are cheap either way), the values of a regsen_breakdown() frontier, the treatments of regsen_multi(), and the replicates and jackknife of regsen_boot(). Work inside a replicate or a treatment is run serially, so cores are never oversubscribed by nesting.

Examples

old <- getOption("regsensitivity.ncores")
regsen_cores()          # the current setting, 1 unless changed
#> [1] 1
regsen_cores(2)         # run the sweeps on two cores
regsen_cores(1)         # back to serial
options(regsensitivity.ncores = old)