prepare_confounds: Prepare weighted nuisance projectors for each run

In neuroim2: Data Structures for Brain Imaging Data

Prepare weighted nuisance projectors for each run

Description

Converts per-run confound matrices and time weights into orthonormal projectors that can be consumed directly by the C++ graph builder. Each run produces Q_k (columns spanning the weighted confound space) and \sqrt{w_k} (per-time-point square-root weights). Supplying a NULL confound matrix yields a zero-column projector, enabling pure time-weighting without regression.

Usage

prepare_confounds(
  confounds,
  time_weights = NULL,
  run_lengths,
  include_intercept = TRUE,
  center_cols = TRUE,
  scale_cols = FALSE
)

Arguments

confounds	List of matrices (T_k \times p_k), or a single matrix reused for all runs. Each row corresponds to a time point.
time_weights	Optional list of numeric vectors (length T_k) or a single vector reused for every run. If NULL, unit weights are used.
run_lengths	Integer vector with the number of time points per run. Required when any run has both confounds=NULL and time_weights=NULL.
include_intercept	Logical; prepend a column of ones before QR (default TRUE).
center_cols	Logical; center each confound column before weighting (default TRUE).
scale_cols	Logical; scale columns to unit variance before weighting (default FALSE).

Value

A list with elements Q_list (list of matrices) and sqrtw_list (list of numeric vectors). Each entry has the same length as run_lengths.

neuroim2 documentation built on April 16, 2026, 5:07 p.m.