fmri_rlm: Fit a Robust Linear Model for fMRI Data Analysis

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

Fit a Robust Linear Model for fMRI Data Analysis

Description

This function fits a robust linear regression model for fMRI data analysis using the specified model formula, block structure, and dataset. The model can be fit using either a runwise or chunkwise data splitting strategy.

Usage

fmri_rlm(
  formula,
  block,
  baseline_model = NULL,
  dataset,
  durations = 0,
  drop_empty = TRUE,
  strategy = c("runwise", "chunkwise"),
  nchunks = 10,
  meta_weighting = c("inv_var", "equal"),
  ...
)

Arguments

formula	The model formula for experimental events.
block	The model formula for block structure.
baseline_model	(Optional) A baseline_model object. Default is NULL.
dataset	An fmri_dataset object containing the time-series data.
durations	A vector of event durations. Default is 0.
drop_empty	Logical. Whether to remove factor levels with zero size. Default is TRUE.
strategy	The data splitting strategy, either "runwise" or "chunkwise". Default is "runwise".
nchunks	Number of data chunks when strategy is "chunkwise". Default is 10.
meta_weighting	Method for combining results across runs/chunks. Either "inv_var" for inverse variance weighting or "equal" for equal weighting. Default is "inv_var".
...	Additional arguments.

Value

A fitted robust linear regression model for fMRI data analysis.

Examples

etab <- data.frame(onset=c(1,30,15,25), fac=factor(c("A", "B", "A", "B")), run=c(1,1,2,2))
etab2 <- data.frame(onset=c(1,30,65,75), fac=factor(c("A", "B", "A", "B")), run=c(1,1,1,1))
mat <- matrix(rnorm(100*100), 100,100)
dset <- matrix_dataset(mat, TR=1, run_length=c(50,50),event_table=etab)
dset2 <- matrix_dataset(mat, TR=1, run_length=c(100),event_table=etab2)
lm.1 <- fmri_rlm(onset ~ hrf(fac), block= ~ run, dataset=dset)
lm.2 <- fmri_rlm(onset ~ hrf(fac), block= ~ run, dataset=dset2)

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