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#' @title
#' Compile pseudo population
#'
#' @description
#' Compiles pseudo population based on the original population and estimated GPS
#' value.
#'
#' @param data_obj A S3 object including the following:
#' - Original data set + GPS values
#' - e_gps_pred
#' - e_gps_std_pred
#' - w_resid
#' - gps_mx (min and max of gps)
#' - w_mx (min and max of w).
#' @param ci_appr Causal inference approach.
#' @param gps_density Model type which is used for estimating GPS value,
#' including `normal` and `kernel`.
#' @param exposure_col_name Exposure data column name.
#' @param nthread An integer value that represents the number of threads to be
#' used by internal packages.
#' @param ... Additional parameters.
#'
#' @details
#' For matching approach, use an extra parameter, `bin_seq`, which is sequence
#' of w (treatment) to generate pseudo population. If `NULL` is passed the
#' default value will be used, which is
#' `seq(min(w)+delta_n/2,max(w), by=delta_n)`.
#'
#'
#' @export
#'
#' @return
#' `compile_pseudo_pop` returns the pseudo population data that is compiled based
#' on the selected causal inference approach.
#'
#' @examples
#' \donttest{
#' set.seed(112)
#' m_d <- generate_syn_data(sample_size = 100)
#'
#' m_xgboost <- function(nthread = 1,
#' ntrees = 35,
#' shrinkage = 0.3,
#' max_depth = 5,
#' ...) {SuperLearner::SL.xgboost(
#' nthread = nthread,
#' ntrees = ntrees,
#' shrinkage=shrinkage,
#' max_depth=max_depth,
#' ...)}
#'
#' data_with_gps <- estimate_gps(.data = m_d,
#' .formula = w ~ cf1 + cf2 + cf3 +
#' cf4 + cf5 + cf6,
#' gps_density = "normal",
#' sl_lib = c("m_xgboost")
#' )
#'
#'
#' pd <- compile_pseudo_pop(data_obj = data_with_gps,
#' ci_appr = "matching",
#' gps_density = "normal",
#' bin_seq = NULL,
#' exposure_col_name = c("w"),
#' nthread = 1,
#' dist_measure = "l1",
#' covar_bl_method = 'absolute',
#' covar_bl_trs = 0.1,
#' covar_bl_trs_type= "mean",
#' delta_n = 0.5,
#' scale = 1)
#'}
compile_pseudo_pop <- function(data_obj,
ci_appr,
gps_density,
exposure_col_name,
nthread,
...) {
dist_measure <- delta_n <- bin_seq <- NULL
if (!(is.object(data_obj) && !isS4(data_obj))) {
stop("data_obj should be a S3 object.")
}
if (!(is.element(".data", attributes(data_obj)$names))) {
stop("data_obj should have the required .data field.")
}
logger::log_info("Starting compiling pseudo population ",
" (original data size: {nrow(data_obj$.data)}) ... ")
## collect additional arguments
dot_args <- list(...)
arg_names <- names(dot_args)
for (i in arg_names){
assign(i, unlist(dot_args[i], use.names = FALSE))
}
auxilary_columns <- c("e_gps_pred", "e_gps_std_pred", "w_resid")
if (ci_appr == 'matching'){
matched_set <- create_matching(.data = data_obj$.data,
exposure_col_name = exposure_col_name,
matching_fn = matching_fn,
dist_measure = dist_measure,
gps_density = gps_density,
delta_n = delta_n,
scale = scale,
bin_seq = bin_seq,
nthread = nthread)
logger::log_info("Finished compiling pseudo population ",
" (Pseudo population data size: {nrow(matched_set)})")
matched_set[, (auxilary_columns) := NULL]
return(matched_set)
} else if (ci_appr == 'weighting'){
weighted_set <- create_weighting(data_obj$.data,
exposure_col_name)
logger::log_info("Finished compiling pseudo population ",
" (Pseudo population data size: {nrow(weighted_set)})")
weighted_set[, (auxilary_columns) := NULL]
return(weighted_set)
} else {
stop(paste('The code should not get here.',
'Something is wrong with checking arguments.'))
}
}
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