GLM OLS Estimation Convenience Function

A convenience wrapper around estimate_betas for ordinary least squares (OLS) estimation. This function provides a simplified interface for fitting GLMs using OLS on matrix datasets.

Usage

glm_ols(
  dataset,
  model_obj,
  basis_obj,
  basemod = NULL,
  block = ~1,
  progress = TRUE,
  ...
)

Arguments

dataset: A matrix_dataset object containing the fMRI time series data
model_obj: An event_model object specifying the experimental design
basis_obj: An HRF basis object (e.g., from fmrihrf::HRF_SPMG1, HRF_FIR, etc.)
basemod: A baseline_model instance to regress out of data before beta estimation (default: NULL)
block: A formula specifying the block factor (default: ~ 1 for single block)
progress: Logical; show progress bar (default: TRUE)
...: Additional arguments passed to estimate_betas

Value

A list of class "fmri_betas" containing the estimated coefficients

Details

Use Cases:

Condition-level estimation: Estimates average responses for each experimental condition
General linear modeling: Standard GLM approach for group-level or condition-level effects
Multi-trial averaging: Combines trials of the same condition to estimate mean responses

For single-trial estimation where each trial gets its own beta estimate, use glm_lss() instead.

Examples

if (FALSE) { # \dontrun{
# Create event model and data
event_data <- data.frame(
  onset = c(10, 30, 50, 70),
  condition = factor(c("A", "B", "A", "B")),
  run = rep(1, 4)
)
sframe <- fmrihrf::sampling_frame(blocklens = 100, TR = 2)
model_obj <- event_model(onset ~ hrf(condition), 
                        data = event_data, 
                        block = ~ run, 
                        sampling_frame = sframe)

# Create data matrix (100 timepoints, 10 voxels)
Y <- matrix(rnorm(1000), 100, 10)

# Create matrix_dataset with event table
dset <- matrix_dataset(Y, TR = 2, run_length = 100, event_table = event_data)

# Fit with OLS - estimates average response for each condition
fit <- glm_ols(dset, model_obj, fmrihrf::HRF_SPMG1)
dim(fit$betas_ran)  # 2 conditions x 10 voxels
} # }