#-----------------------------------------------------------------------------
# Global State Vars (can be controlled globally with options(tmlenet.optname = ))
#-----------------------------------------------------------------------------
gvars <- new.env(parent = emptyenv())
gvars$verbose <- FALSE # verbose mode (print all messages)
gvars$opts <- list() # named list of package options that is controllable by the user (tmlenet_options())
gvars$misval <- NA_integer_ # the default missing value for observations (# gvars$misval <- -.Machine$integer.max)
gvars$misXreplace <- 0L # the default replacement value for misval that appear in the design matrix
gvars$tolerr <- 10^-12 # tolerance error: assume for abs(a-b) < gvars$tolerr => a = b
gvars$sVartypes <- list(bin = "binary", cat = "categor", cont = "contin")
# setopt <- function(optname, val) {
# opt <- gvars$opts
# if (!(optname %in% (names(opt)))) stop(optname %+% ": this options does not exist")
# old.optval <- opt[[optname]]
# opt[[optname]] <- val
# invisible(old.optval)
# }
getopt <- function(optname) {
opt <- gvars$opts
if (!(optname %in% (names(opt)))) stop(optname %+% ": this options does not exist")
return(opt[[optname]])
}
#' Print Current Option Settings for \code{tmlenet}
#' @return Invisibly returns a list of \code{tmlenet} options.
#' @seealso \code{\link{tmlenet_options}}
#' @export
print_tmlenet_opts <- function() {
print(gvars$opts)
invisible(gvars$opts)
}
#' Setting Options for \code{tmlenet}
#'
#' Additional options that control the estimation algorithm in \code{tmlenet} package
#' @param bin_estimator The estimator to use for fitting the binary outcomes (defaults to \code{speedglmR6} which estimates with \code{\link[speedglm]{speedglmR6}})
#' another default option is \code{\link[stats]{glmR6}}.
#' @param bin.method The method for choosing bins when discretizing and fitting the conditional continuous summary
#' exposure variable \code{sA}. The default method is \code{"equal.len"}, which partitions the range of \code{sA}
#' into equal length \code{nbins} intervals. Method \code{"equal.mass"} results in a data-adaptive selection of the bins
#' based on equal mass (equal number of observations), i.e., each bin is defined so that it contains an approximately
#' the same number of observations across all bins. The maximum number of observations in each bin is controlled
#' by parameter \code{maxNperBin}. Method \code{"dhist"} uses a mix of the above two approaches,
#' see Denby and Mallows "Variations on the Histogram" (2009) for more detail.
#' @param parfit Default is \code{FALSE}. Set to \code{TRUE} to use \code{foreach} package and its functions
#' \code{foreach} and \code{dopar} to perform
#' parallel logistic regression fits and predictions for discretized continuous outcomes. This functionality
#' requires registering a parallel backend prior to running \code{tmlenet} function, e.g.,
#' using \code{doParallel} R package and running \code{registerDoParallel(cores = ncores)} for integer
#' \code{ncores} parallel jobs. For an example, see a test in "./tests/RUnit/RUnit_tests_04_netcont_sA_tests.R".
#' @param nbins Set the default number of bins when discretizing a continous outcome variable under setting
#' \code{bin.method = "equal.len"}.
#' If left as \code{NA} the total number of equal intervals (bins) is determined by the nearest integer of
#' \code{nobs}/\code{maxNperBin}, where \code{nobs} is the total number of observations in the input data.
#' @param maxncats Max number of unique categories a categorical variable \code{sA[j]} can have.
#' If \code{sA[j]} has more it is automatically considered continuous.
#' @param poolContinVar Set to \code{TRUE} for fitting a pooled regression which pools bin indicators across all bins.
#' When fitting a model for binirized continuous outcome, set to \code{TRUE}
#' for pooling bin indicators across several bins into one outcome regression?
#' @param maxNperBin Max number of observations per 1 bin for a continuous outcome (applies directly when
#' \code{bin.method="equal.mass"} and indirectly when \code{bin.method="equal.len"}, but \code{nbins = NA}).
#' @return Invisibly returns a list with old option settings.
#' @seealso \code{\link{print_tmlenet_opts}}
#' @export
tmlenet_options <- function(bin_estimator = speedglmR6$new(),
parfit = FALSE,
bin.method = c("equal.len", "equal.mass", "dhist"),
nbins = NA,
maxncats = 20,
poolContinVar = FALSE,
maxNperBin = 1000
) {
old.opts <- gvars$opts
bin.method <- bin.method[1L]
if (bin.method %in% "equal.len") {
} else if (bin.method %in% "equal.mass") {
} else if (bin.method %in% "dhist") {
} else {
stop("bin.method argument must be either 'equal.len', 'equal.mass' or 'dhist'")
}
opts <- list(
bin_estimator = bin_estimator,
bin.method = bin.method,
parfit = parfit,
nbins = nbins,
maxncats = maxncats,
poolContinVar = poolContinVar,
maxNperBin = maxNperBin
)
gvars$opts <- opts
invisible(old.opts)
}
# returns a function (alternatively a call) that tests for missing values in (sA, sW)
testmisfun <- function() {
if (is.na(gvars$misval)) {
return(is.na)
} else if (is.null(gvars$misval)){
return(is.null)
} else if (is.integer(gvars$misval)) {
return(function(x) {x==gvars$misval})
} else {
return(function(x) {x%in%gvars$misval})
}
}
get.misval <- function() {
gvars$misfun <- testmisfun()
gvars$misval
}
set.misval <- function(gvars, newmisval) {
oldmisval <- gvars$misval
gvars$misval <- newmisval
gvars$misfun <- testmisfun() # EVERYTIME gvars$misval HAS CHANGED THIS NEEDS TO BE RESET/RERUN.
invisible(oldmisval)
}
gvars$misfun <- testmisfun()
# Allows tmlenet functions to use e.g., getOption("tmlenet.verbose") to get verbose printing status
.onLoad <- function(libname, pkgname) {
op <- options()
op.tmlenet <- list(
tmlenet.verbose = gvars$verbose
)
# reset all options to their defaults on load:
tmlenet_options()
toset <- !(names(op.tmlenet) %in% names(op))
if(any(toset)) options(op.tmlenet[toset])
invisible()
}
.onAttach <- function(...) {
packageStartupMessage('tmlenet')
packageStartupMessage('The tmlenet package is still in beta testing. Interpret results with caution.')
# packageStartupMessage('Version: ', utils::packageDescription('tmlenet')$Version)
# packageStartupMessage('Package created on ', utils::packageDescription('tmlenet')$Date, '\n')
# packageStartupMessage('Please note this package is still in its early stages of development.
# Check for updates and report bugs at http://github.com/osofr/tmlenet.', '\n')
# packageStartupMessage('To see the vignette use vignette("tmlenet_vignette", package="tmlenet").
# To see all available package documentation use help(package = "tmlenet") and ?tmlenet.', '\n')
# packageStartupMessage('To see the latest updates for this version, use news(package = "tmlenet").', '\n')
}
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