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Example per-element data.frame for user functions

exampleElementData.Rd

Constructs a per-element data.frame from a ModelArray that mirrors the data argument passed to user functions by ModelArray.wrap. This is useful for testing and debugging user-supplied functions outside of the full element-wise analysis loop.

Usage

exampleElementData(x, ...)

# S4 method for class 'ModelArray'
exampleElementData(x, scalar = "FD", i_element = 1L, phenotypes)

Arguments

x

A ModelArray object.

...

Additional arguments passed to the method (currently unused at the generic level).

scalar

Character. The name of the element-wise scalar to append as a column. Must be one of names(scalars(x)). Default is "FD".

i_element

Integer. The 1-based index of the element whose values should be extracted. Must be between 1 and the number of elements for the given scalar.

phenotypes

A data.frame of the cohort with columns of independent variables and covariates. Must have the same number of rows as the number of source files in the ModelArray.

Value

A data.frame: the input phenotypes with one additional column named by scalar containing that element's values across all subjects.

Details

Returns a copy of phenotypes with an extra column named by scalar populated with the selected element's values from the ModelArray. This mirrors the per-element data that ModelArray.wrap passes to user functions (as data = dat).

Use this to verify that your custom function works correctly on a single element before committing to a full ModelArray.wrap run.

See also

ModelArray.wrap, scalars

Examples

if (FALSE) { # \dontrun{
h5_path <- system.file("extdata", "n50_fixels.h5", package = "ModelArray")
csv_path <- system.file("extdata", "n50_cohort.csv", package = "ModelArray")
ma <- ModelArray(h5_path, scalar_types = c("FD"))
phen <- read.csv(csv_path)
df <- exampleElementData(ma, scalar = "FD", i_element = 1, phenotypes = phen)

# Now test your custom function on this single element:
my_fun <- function(data, ...) {
  mod <- lm(FD ~ age + sex, data = data)
  broom::tidy(mod)
}
my_fun(data = df)
} # }