R/LG_boot_approx_scribe.R
In LAJordanger/localgaussSpec: Local Gaussian Spectral Analysis
Defines functions
LG_boot_approx_scribe
Documented in
LG_boot_approx_scribe
#' Local Gaussian Approximations for bootstrap replicates, scribe-function.
#'
#' @description This function takes care of the bookkeeping when the
#' local Gaussian (auto- and cross-) correlations are computed for
#' the lag-h pairs for bootstrapped replicates of a given time
#' series.
#'
#' @details This function records its arguments and compares them to a
#' previously stored information-object for the time series under
#' investigation, in order to avoid redoing previously performed
#' computations. The function then calls \code{LG_boot_approx}
#' when a new computation is required, the result is then saved to
#' file, and the information-object is updated with the key
#' details.
#'
#' @details Note that default values are not given for any of the
#' tuning parameters of the local Gaussian estimation algorithm.
#' The basic idea is that such arguments only should be specified
#' for the bootstrap-part if it is of interest to restrict the
#' attention to a subset of the tuning parameters that were used
#' for the local Gaussian investigation that was done on the
#' original sample. When an argument is left unspecified, the
#' bookkeeping-system will look up the value that was used during
#' the investigation of the original sample, and that value will
#' then be inherited to the present investigation.
#'
#' @template main_dir_arg
#' @template data_dir_arg
#' @template lag_max_boot
#' @template LG_points_boot
#'
#' @template content_details_boot
#'
#' @template LG_type_arg
#' @template bws_mixture_arg
#' @template bw_points_arg
#' @template bws_fixed_arg
#' @template bws_fixed_only_arg
#'
#' @template nb_boot
#' @template boot_type_boot
#' @template block_length_boot
#' @template boot_seed_boot
#'
#' @template threshold_arg
#'
#' @return This function is a scribe that reads and records
#' information, whereas another function performs the actual
#' computation, see details for further information. A list
#' containing the following key-information is always returned to
#' the workflow.
#'
#' \describe{
#'
#' \item{done_before}{Logical value that reveals if the computation
#' has been performed before.}
#'
#' \item{main_dir}{The \code{main_dir}-argument is included here.}
#'
#' \item{data_dir}{The \code{data_dir}-argument is included here.}
#'
#' }
#'
#' @export
LG_boot_approx_scribe <- function(
main_dir,
data_dir,
lag_max = NULL,
LG_points = NULL,
content_details = NULL,
LG_type = NULL,
.bws_mixture = NULL,
bw_points = NULL,
.bws_fixed = NULL,
.bws_fixed_only = NULL,
nb = NULL,
boot_type = NULL,
block_length = NULL,
boot_seed = NULL,
threshold = 500) {
## We need a spy_report to capture the arguments.
spy_report <- spy()
kill(data_dir, lag_max, bw_points, nb, boot_type, block_length,
boot_seed, threshold)
## Sanity-check and update the arguments (inside of 'spy_envir'),
## and check them against previous computations.
books <- LG_bookkeeping(spy_report = spy_report)
## If the same computation already has been performed, we can
## just return the relevant parts of 'books'.
if (books$done_before)
return(books[c("done_before", "main_dir", "data_dir")])
## Create and evaluate a call for the reproducible computation of
## the bootstrapped sample. To simplify the feeding of arguments
## into the function, a minor adjustment of `spy_report$envir`
## will be done first, i.e. it must be reassigned in order to
## point one level deeper into the file-hierarchy,
spy_report$envir$save_dir <- books$save_dir
## Compute the (ordinary global) auto- or cross-correlations.
TS_boot <- eval(create_call(
.cc_fun = TS_boot_sample,
spy_report$envir,
.cc_list = TRUE))
## Add information to the `info`-object.
books$info[[books$bookmark]][["TS_boot"]] <- TS_boot$TS
## Compute the (ordinary global) auto-correlations.
.TS_acr <- TS_acr(
.TS_info = TS_boot,
lag_max = spy_report$envir$lag_max)
## Add information to the 'info'-object.
books$info[[books$bookmark]][[.TS_acr$.acr_type]] <-
.TS_acr$.acr_content
kill(.TS_acr)
## Create a 'loop_list' to govern the computations, this will
## decrease the chance for memory-related problems.
loop_list <- LG_splitting(books)
## Create a template for the data-frame needed for information
## about file-names and memory-size (in MB) for the objects.
LG_type <- spy_report$envir$LG_type
L <- length(loop_list) * length(LG_type)
data_files_df <- LG_default$file_info_df(L = L)
## Initiate counter for updating of 'data_files_df'.
row_nr <- 1
## Create list-templates for storing of dimension-names and
## 'eflag' (the latter comes from 'localgauss', and it tells us
## if the computation was successful, i.e. whether or not the
## values can be trusted).
dimnames.par_one <-list()
dimnames.par_five <-list()
convergence <-list()
## Work through 'loop_list'
for (part in names(loop_list)) {
## Evaluate a call for 'LG_approx' based on 'part'.
result <- eval(create_call(
.cc_fun = LG_boot_approx,
c(list(TS_boot = TS_boot),
loop_list[[part]]$arg_list$compute[[1]]),
.cc_list = TRUE))
## When required, do the 'par_one' case.
if ("par_one" %in% result$LG_type) {
## Save data for 'par_one', register the file-name.
save_files.par_one <- LG_save(
data = result$par_one_data,
LG_type = "par_one",
bootstrap = TRUE,
part = part,
save_dir = books$save_dir)
## Update 'dimnames.par_one', such that it contains the
## information about the part stored in the file.
dimnames.par_one <-
c(dimnames.par_one, list(dimnames(result$par_one_data$on_diag)))
names(dimnames.par_one)[length(dimnames.par_one)] <-
save_files.par_one
## Add information about 'par_one' to 'data_files_df'.
data_files_df[row_nr, ] <-
list(part_a_of_b = part,
content = "par_one",
data_files = save_files.par_one,
object_size_MB=
as.numeric(object.size(result$data.0) / 1024^2))
}
## When required, do the 'par_five' case.
if ("par_five" %in% result$LG_type) {
## Save data for 'par_five', register the file-name.
save_files.par_five <- LG_save(
data = result$par_five_data,
LG_type = "par_five",
bootstrap = TRUE,
part = part,
save_dir = books$save_dir)
## Update 'dimnames.par_five', such that it contains the
## information about the part stored in the file.
dimnames.par_five <-
c(dimnames.par_five, list(dimnames(result$par_five_data$on_diag)))
names(dimnames.par_five)[length(dimnames.par_five)] <-
save_files.par_five
## Add information about 'par_five' to 'data_files_df'.
data_files_df[row_nr + (L - 1), ] <-
list(part_a_of_b = part,
content = "par_five",
data_files = save_files.par_five,
object_size_MB=
as.numeric(object.size(result$data.0) / 1024^2))
## Add information about the convergence (based on 'eflag').
convergence <- c(
convergence,
list(vapply(X = result$par_five_data,
FUN = function(x) {
if (! is.null(attributes(x)$convergence)) {
attributes(x)$convergence
} else
TRUE
},
FUN.VALUE = logical(1))))
names(convergence)[length(convergence)] <- save_files.par_five
}
## Update 'row_nr' for next loop.
row_nr <- row_nr + L
} ## The end of the loop over 'loop_list'
kill(row_nr, L, part, loop_list, result,
save_files.par_one, save_files.par_five)
## Add the data-frame and the relevant lists to the info-object.
books$info[[books$bookmark]][["data_files_df"]] <- data_files_df
if ("par_one" %in% LG_type)
books$info[[books$bookmark]][["dimnames.par_one"]] <- dimnames.par_one
if ("par_five" %in% LG_type)
books$info[[books$bookmark]][["dimnames.par_five"]] <- dimnames.par_five
books$info[[books$bookmark]][["convergence"]] <- convergence
kill(LG_type, dimnames.par_one, dimnames.par_five)
## Save the info-object to the info-file. Remember to extract it
## from 'books' in order to save 'info' instead of 'books$info'.
info <- books$info
save(info, file = books$info_path)
## Modify 'data_files_df$data_files' to include the full path in
## the _present_ operative system.
data_files_df$data_files <- file.path(
books$save_dir,
data_files_df$data_files)
###-------------------------------------------------------------------
## Return information about 'done_before' and 'save_file'.
return(list(
done_before = books$done_before,
main_dir = books$main_dir,
data_dir = books$data_dir))
}
LAJordanger/localgaussSpec documentation built on May 6, 2023, 4:31 a.m.
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Defines functions LG_boot_approx_scribe
Documented in LG_boot_approx_scribe
#' Local Gaussian Approximations for bootstrap replicates, scribe-function.
#'
#' @description This function takes care of the bookkeeping when the
#' local Gaussian (auto- and cross-) correlations are computed for
#' the lag-h pairs for bootstrapped replicates of a given time
#' series.
#'
#' @details This function records its arguments and compares them to a
#' previously stored information-object for the time series under
#' investigation, in order to avoid redoing previously performed
#' computations. The function then calls \code{LG_boot_approx}
#' when a new computation is required, the result is then saved to
#' file, and the information-object is updated with the key
#' details.
#'
#' @details Note that default values are not given for any of the
#' tuning parameters of the local Gaussian estimation algorithm.
#' The basic idea is that such arguments only should be specified
#' for the bootstrap-part if it is of interest to restrict the
#' attention to a subset of the tuning parameters that were used
#' for the local Gaussian investigation that was done on the
#' original sample. When an argument is left unspecified, the
#' bookkeeping-system will look up the value that was used during
#' the investigation of the original sample, and that value will
#' then be inherited to the present investigation.
#'
#' @template main_dir_arg
#' @template data_dir_arg
#' @template lag_max_boot
#' @template LG_points_boot
#'
#' @template content_details_boot
#'
#' @template LG_type_arg
#' @template bws_mixture_arg
#' @template bw_points_arg
#' @template bws_fixed_arg
#' @template bws_fixed_only_arg
#'
#' @template nb_boot
#' @template boot_type_boot
#' @template block_length_boot
#' @template boot_seed_boot
#'
#' @template threshold_arg
#'
#' @return This function is a scribe that reads and records
#' information, whereas another function performs the actual
#' computation, see details for further information. A list
#' containing the following key-information is always returned to
#' the workflow.
#'
#' \describe{
#'
#' \item{done_before}{Logical value that reveals if the computation
#' has been performed before.}
#'
#' \item{main_dir}{The \code{main_dir}-argument is included here.}
#'
#' \item{data_dir}{The \code{data_dir}-argument is included here.}
#'
#' }
#'
#' @export
LG_boot_approx_scribe <- function(
main_dir,
data_dir,
lag_max = NULL,
LG_points = NULL,
content_details = NULL,
LG_type = NULL,
.bws_mixture = NULL,
bw_points = NULL,
.bws_fixed = NULL,
.bws_fixed_only = NULL,
nb = NULL,
boot_type = NULL,
block_length = NULL,
boot_seed = NULL,
threshold = 500) {
## We need a spy_report to capture the arguments.
spy_report <- spy()
kill(data_dir, lag_max, bw_points, nb, boot_type, block_length,
boot_seed, threshold)
## Sanity-check and update the arguments (inside of 'spy_envir'),
## and check them against previous computations.
books <- LG_bookkeeping(spy_report = spy_report)
## If the same computation already has been performed, we can
## just return the relevant parts of 'books'.
if (books$done_before)
return(books[c("done_before", "main_dir", "data_dir")])
## Create and evaluate a call for the reproducible computation of
## the bootstrapped sample. To simplify the feeding of arguments
## into the function, a minor adjustment of `spy_report$envir`
## will be done first, i.e. it must be reassigned in order to
## point one level deeper into the file-hierarchy,
spy_report$envir$save_dir <- books$save_dir
## Compute the (ordinary global) auto- or cross-correlations.
TS_boot <- eval(create_call(
.cc_fun = TS_boot_sample,
spy_report$envir,
.cc_list = TRUE))
## Add information to the `info`-object.
books$info[[books$bookmark]][["TS_boot"]] <- TS_boot$TS
## Compute the (ordinary global) auto-correlations.
.TS_acr <- TS_acr(
.TS_info = TS_boot,
lag_max = spy_report$envir$lag_max)
## Add information to the 'info'-object.
books$info[[books$bookmark]][[.TS_acr$.acr_type]] <-
.TS_acr$.acr_content
kill(.TS_acr)
## Create a 'loop_list' to govern the computations, this will
## decrease the chance for memory-related problems.
loop_list <- LG_splitting(books)
## Create a template for the data-frame needed for information
## about file-names and memory-size (in MB) for the objects.
LG_type <- spy_report$envir$LG_type
L <- length(loop_list) * length(LG_type)
data_files_df <- LG_default$file_info_df(L = L)
## Initiate counter for updating of 'data_files_df'.
row_nr <- 1
## Create list-templates for storing of dimension-names and
## 'eflag' (the latter comes from 'localgauss', and it tells us
## if the computation was successful, i.e. whether or not the
## values can be trusted).
dimnames.par_one <-list()
dimnames.par_five <-list()
convergence <-list()
## Work through 'loop_list'
for (part in names(loop_list)) {
## Evaluate a call for 'LG_approx' based on 'part'.
result <- eval(create_call(
.cc_fun = LG_boot_approx,
c(list(TS_boot = TS_boot),
loop_list[[part]]$arg_list$compute[[1]]),
.cc_list = TRUE))
## When required, do the 'par_one' case.
if ("par_one" %in% result$LG_type) {
## Save data for 'par_one', register the file-name.
save_files.par_one <- LG_save(
data = result$par_one_data,
LG_type = "par_one",
bootstrap = TRUE,
part = part,
save_dir = books$save_dir)
## Update 'dimnames.par_one', such that it contains the
## information about the part stored in the file.
dimnames.par_one <-
c(dimnames.par_one, list(dimnames(result$par_one_data$on_diag)))
names(dimnames.par_one)[length(dimnames.par_one)] <-
save_files.par_one
## Add information about 'par_one' to 'data_files_df'.
data_files_df[row_nr, ] <-
list(part_a_of_b = part,
content = "par_one",
data_files = save_files.par_one,
object_size_MB=
as.numeric(object.size(result$data.0) / 1024^2))
}
## When required, do the 'par_five' case.
if ("par_five" %in% result$LG_type) {
## Save data for 'par_five', register the file-name.
save_files.par_five <- LG_save(
data = result$par_five_data,
LG_type = "par_five",
bootstrap = TRUE,
part = part,
save_dir = books$save_dir)
## Update 'dimnames.par_five', such that it contains the
## information about the part stored in the file.
dimnames.par_five <-
c(dimnames.par_five, list(dimnames(result$par_five_data$on_diag)))
names(dimnames.par_five)[length(dimnames.par_five)] <-
save_files.par_five
## Add information about 'par_five' to 'data_files_df'.
data_files_df[row_nr + (L - 1), ] <-
list(part_a_of_b = part,
content = "par_five",
data_files = save_files.par_five,
object_size_MB=
as.numeric(object.size(result$data.0) / 1024^2))
## Add information about the convergence (based on 'eflag').
convergence <- c(
convergence,
list(vapply(X = result$par_five_data,
FUN = function(x) {
if (! is.null(attributes(x)$convergence)) {
attributes(x)$convergence
} else
TRUE
},
FUN.VALUE = logical(1))))
names(convergence)[length(convergence)] <- save_files.par_five
}
## Update 'row_nr' for next loop.
row_nr <- row_nr + L
} ## The end of the loop over 'loop_list'
kill(row_nr, L, part, loop_list, result,
save_files.par_one, save_files.par_five)
## Add the data-frame and the relevant lists to the info-object.
books$info[[books$bookmark]][["data_files_df"]] <- data_files_df
if ("par_one" %in% LG_type)
books$info[[books$bookmark]][["dimnames.par_one"]] <- dimnames.par_one
if ("par_five" %in% LG_type)
books$info[[books$bookmark]][["dimnames.par_five"]] <- dimnames.par_five
books$info[[books$bookmark]][["convergence"]] <- convergence
kill(LG_type, dimnames.par_one, dimnames.par_five)
## Save the info-object to the info-file. Remember to extract it
## from 'books' in order to save 'info' instead of 'books$info'.
info <- books$info
save(info, file = books$info_path)
## Modify 'data_files_df$data_files' to include the full path in
## the _present_ operative system.
data_files_df$data_files <- file.path(
books$save_dir,
data_files_df$data_files)
###-------------------------------------------------------------------
## Return information about 'done_before' and 'save_file'.
return(list(
done_before = books$done_before,
main_dir = books$main_dir,
data_dir = books$data_dir))
}
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