Defines functions pSapply rMapply isRaw isR2 isPhi isInx isInt isBet isGls isMer isGlm isMod isBoot isList

Documented in isBet isBoot isGlm isGls isInt isInx isList isMer isMod isPhi isR2 isRaw pSapply rMapply

#' @keywords internal
#' @title Object Types
#' @description Functions to determine the 'type' of an R object using classes.
#'   Intended largely for convenience and internal use.
#' @param x An R object.
#' @return A logical value.
#' @name Object.Type
#' @describeIn Object.Type Is object a list (class `"list"` only)?
isList <- function(x) {
  class(x)[1] == "list"
#' @describeIn Object.Type Is object a boot object (class `"boot"`)?
isBoot <- function(x) {
  "boot" %in% class(x)
#' @describeIn Object.Type Is object a fitted model?
isMod <- function(x) {
  any(c("lm", "glm", "lmerMod", "glmerMod", "lmerModLmerTest", "gls", "betareg")
      %in% class(x))
#' @describeIn Object.Type Is object a generalised linear model (i.e. uses a
#'   link function)?
isGlm <- function(x) {
  any(c("glm", "glmerMod", "betareg") %in% class(x))
#' @describeIn Object.Type Is object a mixed model (class `"merMod"`)?
isMer <- function(x) {
  any(c("lmerMod", "glmerMod", "lmerModLmerTest") %in% class(x))
#' @describeIn Object.Type Is object a generalised least squares model (class
#'   `"gls"`)?
isGls <- function(x) {
  "gls" %in% class(x)
#' @describeIn Object.Type Is object a beta regression model (class
#'   `"betareg"`)?
isBet <- function(x) {
  "betareg" %in% class(x)

#' @keywords internal
#' @title Parameter Types
#' @description Functions to determine the presence/absence of certain model
#'   parameter types using their names. Intended largely for convenience and
#'   internal use.
#' @param x A character vector of parameter names (e.g. names of coefficients
#'   from [coef()] or [stdEff()]).
#' @return A logical vector of the same length as `x`.
#' @name Param.Type
#' @describeIn Param.Type Is parameter an intercept?
isInt <- function(x) {
  x == "(Intercept)"
#' @describeIn Param.Type Is parameter a variable interaction (product term)?
isInx <- function(x) {
  grepl("(?<!:):(?!:)", x, perl = TRUE)
#' @describeIn Param.Type Is parameter a beta regression precision coefficient?
isPhi <- function(x) {
  grepl("^\\(phi\\)", x)
#' @describeIn Param.Type Is parameter an R-squared value?
isR2 <- function(x) {
  x %in% c("(R.squared)", "(R.squared.adj)", "(R.squared.pred)",
           "(R_squared)", "(R_squared_adj)", "(R_squared_pred)")
#' @describeIn Param.Type Is parameter a raw (unstandardised) coefficient?
isRaw <- function(x) {
  grepl("^\\(raw\\)_", x)

#' @title Recursive [mapply()]
#' @description Recursively apply a function to a list or lists.
#' @param FUN Function to apply.
#' @param ... Object(s) to which `FUN` can be applied, or lists of such objects
#'   to iterate over (defined narrowly, as of class `"list"`).
#' @param MoreArgs A list of additional arguments to `FUN`.
#' @param SIMPLIFY Logical, whether to simplify the results to a vector or
#'   array.
#' @param USE.NAMES Logical, whether to use the names of the first list object
#'   in `...` for the output.
#' @details `rMapply()` recursively applies `FUN` to the elements of the lists
#'   in `...` via [mapply()]. If only a single list is supplied, the function
#'   acts like a recursive version of [sapply()]. The particular condition that
#'   determines if the function should stop recursing is if either the first or
#'   second objects in `...` are not of class `"list"`. Thus, unlike [mapply()],
#'   it will not iterate over non-list elements in these objects, but instead
#'   returns the output of `FUN(...)`.
#'   This is primarily a convenience function used internally to enable
#'   recursive application of functions to lists or nested lists. Its particular
#'   stop condition for recursing is also designed to either *a)* act as a
#'   wrapper for `FUN` if the first object in `...` is not a list, or *b)* apply
#'   a weighted averaging operation if the first object is a list and the second
#'   object is a numeric vector of weights.
#' @return The output of `FUN` in a list or nested list, or simplified to a
#'   vector or array (or list of arrays).
#' @export
rMapply <- function(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE,
                    USE.NAMES = TRUE) {
  l <- list(...)
  n <- length(l)
  i <- if (n > 0) l[[1]] else l
  j <- if (n > 1) l[[2]] else i
  if (!isList(i) || !isList(j)) {
    do.call(FUN, c(l, MoreArgs))
  } else {
    a <- list(FUN = FUN, MoreArgs = MoreArgs, SIMPLIFY = SIMPLIFY,
              USE.NAMES = USE.NAMES)
    mapply(rMapply, ..., MoreArgs = a, SIMPLIFY = SIMPLIFY,
           USE.NAMES = USE.NAMES)

#' @title Parallel [sapply()]
#' @description Apply a function to a vector using parallel processing.
#' @param X A vector object (numeric, character, or list).
#' @param FUN Function to apply to the elements of `X`.
#' @param 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.
#' @param ncpus Number of system cores to use for parallel processing. If `NULL`
#'   (default), all available cores are used.
#' @param cl Optional cluster to use if `parallel = "snow"`. If `NULL`
#'   (default), a local cluster is created using the specified number of cores.
#' @param 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.
#' @param ... Additional arguments to [parSapply()],
#'   [`mcmapply()`](https://rdrr.io/r/parallel/unix/mclapply.html), or
#'   [sapply()] (note: arguments `"simplify"` and `"SIMPLIFY"` are both
#'   allowed).
#' @details This is a wrapper for [parallel::parSapply()] (`"snow"`) or
#'   [`parallel::mcmapply()`](https://rdrr.io/r/parallel/unix/mclapply.html)
#'   (`"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`.
#' @return The output of `FUN` in a list, or simplified to a vector or array.
#' @export
pSapply <- function(X, FUN, parallel = c("snow", "multicore", "no"),
                    ncpus = NULL, cl = NULL, add.obj = NULL, ...) {

  parallel <- match.arg(parallel); nc <- ncpus; ao <- add.obj; a <- list(...)
  if (parallel == "multicore") a$simplify <- NULL else a$SIMPLIFY <- NULL

  if (parallel != "no") {

    # No. cores to use
    if (is.null(nc)) nc <- parallel::detectCores()

    if (parallel == "snow") {

      # Create local cluster using system cores
      if (is.null(cl)) {
        cl <- parallel::makeCluster(getOption("cl.cores", nc))

      # Export required objects/functions to cluster
      # (search global env. for objects in calls to X/FUN)
      P <- function(...) {
        paste(..., collapse = " ")
      xc <- P(unlist(rMapply(function(i) {
        if (isMod(i) || isBoot(i)) P(getCall(i))
      }, X)))
      fa <- P(sapply(match.call(expand.dots = FALSE)$..., deparse))
      fc <- P(xc, enquote(FUN)[2], fa)
      o <- unlist(lapply(search(), ls))
      o <- o[sapply(o, function(i) grepl(i, fc, fixed = TRUE))]
      o <- c("X", o, ao)
      parallel::clusterExport(cl, o, environment())

      # Run parSapply using cluster
      out <- do.call(parallel::parSapply, c(list(cl, X, FUN), a))

    } else {
      out <- parallel::mcmapply(FUN, X, mc.cores = nc, MoreArgs = a)

  } else {
    out <- do.call(sapply, c(list(X, FUN), a))



Try the semEff package in your browser

Any scripts or data that you put into this service are public.

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