Fit a registered group reducer over frame feature blocks

fit_group() is the eager convenience wrapper for group_plan() followed by compute(). The default known-variance random-intercept model is diag(sampling_variance) + vc_resid I + vc_intercept Z Z'; other registered frame kernels may use different input and model contracts.

Usage

fit_group(
  frame,
  estimate = "beta",
  variance = "variance",
  design,
  memory_budget = 256 * 1024^2,
  block_size = NULL,
  fit = c("REML", "ML"),
  method = "lmm:ri_knownvar",
  options = list()
)

Arguments

frame: An fmri_frame or synchronized view.
estimate: Name of the effect assay.
variance: Name of the known sampling-variance assay.
design: A design_spec or compiled_design.
memory_budget: Maximum bytes for the two input feature blocks.
block_size: Optional explicit feature-block width.
fit: Likelihood criterion, "REML" or "ML".
method: Registered frame-capable reducer. The default is "lmm:ri_knownvar"; "ols:voxelwise" provides unweighted fixed-effects fitting when no sampling-variance assay is available.
options: Named reducer options.

Value

An fmri_group_fit whose result is an fmri_frame sharing the input feature IDs and spatial domain.