prepare_sequential: Sequentially prepare data for sandwich smooth

In hero: Spatio-Temporal (Hero) Sandwich Smoother

Sequentially prepare data for sandwich smooth

Description

Sequentially prepare each observation for smoothing. It is assumed that each observation resides in its own file and that do.call(import_fun, list(import_list[i])) will import the data associated with observation i into memory. The import_fun argument should be a function after the style of readRDS, where the object can be assigned a name once it is read in. The import_fun argument should NOT be like load, where the object loaded has a preassigned name.

Usage

prepare_sequential(
  import_list,
  import_fun = base::readRDS,
  x,
  splines,
  assembled,
  package = "base",
  call_args = list(),
  ...
)

Arguments

import_list	A vector or list whose elements tell import_fun which files to import.
import_fun	A function that will read each observation into memory based on the elements of import_list.
x	The list of arguments at which to evaluate each of the splines used to construct assembled.
splines	A list of spline-related objects. Each element of splines corresponds to the set of splines for the corresponding element of x.
assembled	A list of assembled_splines. See Examples.
package	A character string indicating the package to use for the computations. The choices are "base", "parallel", "pbapply", "future.apply", and "Rmpi". The default is "base", in which case a standard for loop is used. If package == "parallel", then mclapply is used, which is only appropriate when mc.cores is integer-valued or NULL. If package == "pbapply", then pblapply is used, which automatically provides a progress bar. If package == "future.apply", then future_lapply is used. If package == "Rmpi", then mpi.applyLB is used.
call_args	A named list providing relevant arguments to the mclapply, pblapply, future_lapply, or mpi.applyLB depending on the value of package.
...	Not implemented

Value

A prepared_sequential object

Author(s)

Joshua P. French

See Also

prepare, mclapply, pblapply, future_lapply, mpi.applyLB

hero documentation built on July 26, 2023, 5:11 p.m.