estimate_betas: Estimate Beta Coefficients for fMRI Data

In bbuchsbaum/fmrireg: R package for the anlysis of fmri data

Estimate Beta Coefficients for fMRI Data

Description

Estimate beta coefficients (regression parameters) from fMRI data using various methods. This function supports different estimation approaches for:

single

Single-trial beta estimation

effects

Fixed and random effects

regularization

Various regularization techniques

hrf

Optional HRF estimation

This function estimates betas (regression coefficients) for fixed and random effects in a matrix dataset using various methods.

Usage

estimate_betas(x, ...)

## S3 method for class 'latent_dataset'
estimate_betas(
  x,
  fixed = NULL,
  ran,
  block,
  method = c("mixed", "pls", "pls_global", "ols"),
  basemod = NULL,
  ncomp = 4,
  lambda = 0.01,
  prewhiten = FALSE,
  ...
)

Arguments

x	An object of class matrix_dataset representing the matrix dataset
...	Additional arguments passed to the estimation method
fixed	A formula specifying the fixed regressors that model constant effects (i.e., non-varying over trials)
ran	A formula specifying the random (trialwise) regressors that model single trial effects
block	A formula specifying the block factor
method	The regression method for estimating trialwise betas; one of "mixed", "pls", "pls_global", or "ols" (default: "mixed")
basemod	A baseline_model instance to regress out of data before beta estimation (default: NULL)
ncomp	Number of PLS components for the "pls" and "pls_global" methods (default: 4)
lambda	Lambda parameter (not currently used; default: 0.01)
prewhiten	currently experimental, default to FALSE.

Details

This is a generic function with methods for different dataset types:

fmri_dataset

For volumetric fMRI data

matrix_dataset

For matrix-format data

latent_dataset

For dimensionality-reduced data

Available estimation methods include:

mixed

Mixed-effects model using rrBLUP

r1

Rank-1 GLM with joint HRF estimation

lss

Least-squares separate estimation

pls

Partial least squares regression

ols

Ordinary least squares

fracridge

Fractional ridge regression

Value

A list of class "fmri_betas" containing:

betas_fixed

Fixed effect coefficients

betas_ran

Random (trial-wise) coefficients

design_ran

Design matrix for random effects

design_fixed

Design matrix for fixed effects

design_base

Design matrix for baseline model

method_specific

Additional components specific to the estimation method used

A list of class "fmri_betas" containing the following components:

• betas_fixed: Matrix representing the fixed effect betas

• betas_ran: Matrix representing the random effect betas

• design_ran: Design matrix for random effects

• design_fixed: Design matrix for fixed effects

• design_base: Design matrix for baseline model

References

Mumford, J. A., et al. (2012). Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage, 59(3), 2636-2643.

Pedregosa, F., et al. (2015). Data-driven HRF estimation for encoding and decoding models. NeuroImage, 104, 209-220.

See Also

fmri_dataset, matrix_dataset, latent_dataset

matrix_dataset, baseline_model

Other estimate_betas: estimate_betas.matrix_dataset()

Examples

# Create example data
event_data <- data.frame(
  condition = factor(c("A", "B", "A", "B")),
  onsets = c(1, 10, 20, 30),
  run = c(1, 1, 1, 1)
)

# Create sampling frame and dataset
sframe <- sampling_frame(blocklens = 50, TR = 2)
dset <- matrix_dataset(
  matrix(rnorm(100 * 2), 100, 2),
  TR = 2,
  run_length = 50,
  event_table = event_data
)

# Estimate betas using mixed-effects model
betas <- estimate_betas(
  dset,
  fixed = onset ~ hrf(condition),
  ran = onset ~ trialwise(),
  block = ~run,
  method = "mixed"
)

bbuchsbaum/fmrireg documentation built on March 1, 2025, 11:20 a.m.