Slices up families by additive relatedness, creating CSV files grouped by degree of relatedness. Operates on a potentially large file by reading in chunks and binning links by additive relatedness.
Usage
sliceFamilies(
outcome_name = "AD_demo",
biggest = TRUE,
bin_width = 0.1,
degreerelatedness = 12,
chunk_size = 2e+07,
max_lines = 1e+13,
addRel_ceiling = 1.5,
input_file = NULL,
folder_prefix = "data",
progress_csv = "progress.csv",
progress_status = "progress.txt",
data_directory = NULL,
verbose = FALSE,
error_handling = FALSE,
file_column_names = c("ID1", "ID2", "addRel", "mitRel", "cnuRel")
)Arguments
- outcome_name
Name of the outcome variable (used for naming input/output files)
- biggest
Logical; whether to process the "biggest" family dataset (TRUE) or all-but-biggest (FALSE)
- bin_width
Width of additive relatedness bins (default is 0.10)
Maximum degree of relatedness to consider (default 12)
- chunk_size
Number of lines to read in each chunk (default 2e7)
- max_lines
Max number of lines to process from input file (default 1e13)
- addRel_ceiling
Numeric. Maximum relatedness value to bin to. Default is 1.5
- input_file
Path to the input CSV file. If NULL, defaults to a specific file based on `biggest` flag.
- folder_prefix
Prefix for the output folder (default "data")
- progress_csv
Path to a CSV file for tracking progress (default "progress.csv")
- progress_status
Path to a text file for logging progress status (default "progress.txt")
- data_directory
Directory where output files will be saved. If NULL, it is constructed based on `outcome_name` and `folder_prefix`.
- verbose
Logical; whether to print progress messages (default FALSE)
- error_handling
Logical. Should more aggressive error handing be attemptted? Default is false
- file_column_names
Names of the columns in the input file (default c("ID1", "ID2", "addRel", "mitRel", "cnuRel"))