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.
prepare_sequential(
import_list,
import_fun = base::readRDS,
x,
splines,
assembled,
package = "base",
call_args = list(),
...
)A prepared_sequential object
A vector or list whose elements tell import_fun
which files to import.
A function that will read each observation into memory
based on the elements of import_list.
The list of arguments at which to evaluate each
of the splines used to construct assembled.
A list of spline-related objects. Each element of
splines corresponds to the set of splines for the corresponding
element of x.
A list of assembled_splines. See
Examples.
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.
A named list providing relevant arguments to the
mclapply, pblapply,
future_lapply, or
mpi.applyLB depending on the value of package.
Not implemented
Joshua P. French
prepare, mclapply,
pblapply, future_lapply,
mpi.applyLB