Lightweight wrapper around [bilinear_mixed()] that uses [bilinear_mixed_recommend()] defaults. Optionally performs compact subject-block CV tuning via [bilinear_mixed_tune()].
Usage
bilinear_mixed_easy(
data,
subject,
z = NULL,
y = NULL,
row_design = NULL,
mode = c("auto", "seed_axis", "seed_repeat", "both"),
connectivity_type = c("auto", "cross", "symmetric"),
profile = c("fast", "balanced", "adaptive"),
tune = FALSE,
tune_grid = NULL,
n_folds = 3,
metric = c("auto", "reconstruction", "trait_r2"),
seed = 1,
verbose = FALSE,
...
)Arguments
- data
A list of numeric connectivity matrices.
- subject
Subject identifier.
- z
Optional repeat-level design.
- y
Optional subject-level traits.
- row_design
Optional row-level covariates.
- mode
One of
"auto","seed_axis","seed_repeat", or"both".- connectivity_type
One of
"auto","cross", or"symmetric".- profile
One of
"fast","balanced", or"adaptive".- tune
Logical; if
TRUE, run [bilinear_mixed_tune()].- tune_grid
Optional grid for tuning; see [bilinear_mixed_tune()].
- n_folds
Number of subject-block CV folds for tuning.
- metric
One of
"auto","reconstruction", or"trait_r2".- seed
Integer random seed for fold assignment.
- verbose
Logical verbosity.
- ...
Optional overrides passed to fitting/tuning.