Run a user-supplied function for element-wise data

`ModelArray.wrap` runs a user-supplied function `FUN` for each requested element and returns a tibble/data.frame of results combined across elements.

Usage

ModelArray.wrap(
  FUN,
  data,
  phenotypes,
  scalar,
  element.subset = NULL,
  num.subj.lthr.abs = 10,
  num.subj.lthr.rel = 0.2,
  verbose = TRUE,
  pbar = TRUE,
  n_cores = 1,
  on_error = "stop",
  ...
)

Arguments

FUN

A function that accepts at least `data` and returns a one-row data.frame/tibble, a named list, or a named vector of results for that element.

data

ModelArray class

phenotypes

A data.frame with cohort covariates; must contain column `source_file`. It must match `sources(data)[[scalar]]` order and contents (reordered if needed).

scalar

Character, the scalar name to analyze

element.subset

Optional integer vector of element indices (1-based); defaults to all

num.subj.lthr.abs

Integer lower threshold for subjects with finite values (default 10)

num.subj.lthr.rel

Relative lower threshold (0-1) (default 0.2)

verbose

TRUE/FALSE to print messages

pbar

TRUE/FALSE to show progress bar

n_cores

Positive integer number of CPU cores

...

Additional arguments forwarded to `FUN`

Value

Tibble/data.frame with one row per element and first column `element_id`

Details

This provides a generic framework reusing ModelArray's per-element looping, alignment, subject-thresholding, and parallelization. The user function will be called as `FUN(data = dat, ...)` where `dat` is `phenotypes` with the response column named `scalar` appended for the current element. The return value from `FUN` for a single element must be either a one-row data.frame/tibble, a named list, or a named atomic vector. The column names from the first successful element determine the final schema.

Note: `ModelArray.wrap` never performs any p-value corrections or modifications. If you need adjusted p-values (e.g., FDR), implement them inside your provided `FUN`.