Nothing
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval=FALSE---------------------------------------------------------------
# library(PublicationBiasBenchmark)
#
# # Specify path to the directory containing results
# PublicationBiasBenchmark.options(resources_directory = "/path/to/files")
## ----eval=FALSE---------------------------------------------------------------
# # List of DGMs to evaluate
# dgm_names <- c(
# "Stanley2017",
# "Alinaghi2018",
# "Bom2019",
# "Carter2019"
# )
#
# # Your method information
# method_name <- "myNewMethod"
# method_setting <- "default" # Or other setting name if you have multiple
## ----eval=FALSE---------------------------------------------------------------
# # Download datasets for all DGMs
# for (dgm_name in dgm_names) {
# message("Downloading datasets for: ", dgm_name)
# download_dgm_datasets(dgm_name)
# }
## ----eval=FALSE---------------------------------------------------------------
# # Set seed for reproducibility
# set.seed(1)
#
# # Process each DGM
# for (dgm_name in dgm_names) {
#
# message("Processing DGM: ", dgm_name)
#
# # Get condition information
# conditions <- dgm_conditions(dgm_name)
# message("Number of conditions: ", nrow(conditions))
#
# # Container to store all results for this DGM
# all_results <- list()
#
# # Process each condition
# for (condition_id in conditions$condition_id) {
#
# message(" Condition ", condition_id, " / ", nrow(conditions))
#
# # Retrieve all repetitions for this condition
# condition_datasets <- retrieve_dgm_dataset(
# dgm_name = dgm_name,
# condition_id = condition_id,
# repetition_id = NULL # NULL retrieves all repetitions
# )
#
# # Get unique repetition IDs
# repetition_ids <- unique(condition_datasets$repetition_id)
# message(" Repetitions: ", length(repetition_ids))
#
# # Compute results for each repetition
# condition_results <- list()
# for (repetition_id in repetition_ids) {
#
# # Extract data for this specific repetition
# repetition_data <- condition_datasets[
# condition_datasets$repetition_id == repetition_id,
# ]
#
# # Apply your method (error handling is done internally)
# result <- run_method(
# method_name = method_name,
# data = repetition_data,
# settings = method_setting
# )
#
# # Attach metadata
# result$condition_id <- condition_id
# result$repetition_id <- repetition_id
#
# condition_results[[repetition_id]] <- result
# }
#
# # Combine results for this condition
# all_results[[condition_id]] <- do.call(rbind, condition_results)
# }
#
# # Combine all results for this DGM
# dgm_results <- do.call(rbind, all_results)
#
# # Save results
# results_dir <- file.path(data_folder, dgm_name, "results")
# if (!dir.exists(results_dir)) {
# dir.create(results_dir, recursive = TRUE)
# }
#
# results_file <- file.path(
# results_dir,
# paste0(method_name, "-", method_setting, ".csv")
# )
#
# write.csv(dgm_results, file = results_file, row.names = FALSE)
# message("Results saved to: ", results_file)
#
# # Save session information
# metadata_dir <- file.path(data_folder, dgm_name, "metadata")
# if (!dir.exists(metadata_dir)) {
# dir.create(metadata_dir, recursive = TRUE)
# }
#
# # sessionInfo() output
# sessioninfo_file <- file.path(
# metadata_dir,
# paste0(method_name, "-", method_setting, "-sessionInfo.txt")
# )
# writeLines(
# capture.output(sessionInfo()),
# sessioninfo_file
# )
#
# # Detailed session info (using the sessioninfo package)
# session_log_file <- file.path(
# metadata_dir,
# paste0(method_name, "-", method_setting, "-session.log")
# )
# sessioninfo::session_info(to_file = session_log_file)
#
# message("Session info saved to: ", metadata_dir)
# }
#
# message("All computations completed!")
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