Skip to contents

Naive Least Squares Separate (LSS) Analysis

lss_naive.Rd

Performs LSS analysis using the naive approach where each trial model is fit separately. This is the conceptually simplest implementation but less efficient than the optimized lss function.

Usage

lss_naive(Y = NULL, bdes, dset = NULL)

Arguments

Y

A numeric matrix where rows are timepoints and columns are voxels/features. If NULL, the function will attempt to extract data from dset.

bdes

A list containing design matrices with components:

  • dmat_base: Base design matrix (e.g., intercept, drift terms)

  • dmat_fixed: Fixed effects design matrix (optional)

  • dmat_ran: Random/trial design matrix for LSS analysis

  • fixed_ind: Indices for fixed effects (optional)

dset

Optional dataset object. If provided and Y is NULL, data will be extracted using get_data_matrix.

Value

A numeric matrix with dimensions (n_events x n_voxels) containing the LSS beta estimates for each trial and voxel.

Details

This function implements the naive LSS approach where for each trial, a separate GLM is fitted that includes:

  • All base regressors (intercept, drift, etc.)

  • All fixed effects regressors (if any)

  • Only the current trial's regressor from the trial design matrix

While less efficient than the optimized lss function, this implementation is conceptually simpler and can serve as a reference or for validation purposes.

See also

lss for the optimized implementation

Examples

if (FALSE) { # \dontrun{
# Using same setup as lss() examples
beta_estimates_naive <- lss_naive(Y = Y, bdes = bdes)

# Compare with optimized version
beta_estimates_fast <- lss(Y = Y, bdes = bdes)
max(abs(beta_estimates_naive - beta_estimates_fast))
} # }