pSapply: Parallel 'sapply()'

Description Usage Arguments Details Value

View source: R/helper-fun.R

Description

Apply a function to a vector using parallel processing.

Usage

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pSapply(
  X,
  FUN,
  parallel = c("snow", "multicore", "no"),
  ncpus = NULL,
  cl = NULL,
  add.obj = NULL,
  ...
)

Arguments

X

A vector object (numeric, character, or list).

FUN

Function to apply to the elements of X.

parallel

The type of parallel processing to use. Can be one of "snow" (default), "multicore" (not available on Windows), or "no" (for none). See Details.

ncpus

Number of system cores to use for parallel processing. If NULL (default), all available cores are used.

cl

Optional cluster to use if parallel = "snow". If NULL (default), a local cluster is created using the specified number of cores.

add.obj

A character vector of any additional object names to be exported to the cluster. Use if a required object or function cannot be found.

...

Additional arguments to parSapply(), mcmapply(), or sapply() (note: arguments "simplify" and "SIMPLIFY" are both allowed).

Details

This is a wrapper for parallel::parSapply() ("snow") or parallel::mcmapply() ("multicore"), enabling (potentially) faster processing of a function over a vector of objects. If parallel = "no", sapply() is used instead.

Parallel processing via option "snow" (default) is carried out using a cluster of workers, which is automatically set up via makeCluster() using all available system cores or a user supplied number of cores. The function then exports the required objects and functions to this cluster using clusterExport(), after performing a (rough) match of all objects and functions in the current global environment to those referenced in the call to FUN (and also any calls in X). Any additional required object names can be supplied using add.obj.

Value

The output of FUN in a list, or simplified to a vector or array.


semEff documentation built on Oct. 12, 2021, 5:06 p.m.