fmri_latent_lm: Fast fMRI Regression Model Estimation from a Latent Component...

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

Fast fMRI Regression Model Estimation from a Latent Component Dataset

Description

This function estimates a regression model for fMRI data using a latent component dataset. The dataset must be of type latent_dataset, which itself requires a LatentNeuroVec input.

Usage

fmri_latent_lm(
  formula,
  block,
  baseline_model = NULL,
  dataset,
  durations,
  drop_empty = TRUE,
  robust = FALSE,
  autocor = c("none", "auto", "ar1", "ar2", "arma"),
  bootstrap = FALSE,
  nboot = 1000,
  ...
)

Arguments

formula	A formula specifying the regression model.
block	A factor indicating the block structure of the data.
baseline_model	An optional baseline model.
dataset	A dataset of class 'latent_dataset'.
durations	The duration of events in the dataset.
drop_empty	Whether to drop empty events from the model. Default is TRUE.
robust	Whether to use robust regression methods. Default is FALSE.
autocor	The autocorrelation correction method to use on components. One of 'none', 'auto', 'ar1', 'ar2', or 'arma'. Default is 'none'.
bootstrap	Whether to compute bootstrapped parameter estimates. Default is FALSE.
nboot	The number of bootstrap iterations. Default is 1000.
...	Additional arguments.

Value

An object of class 'fmri_latent_lm' containing the regression model and dataset.

Note

This method is currently experimental.

Examples

# Estimate the fMRI regression model using the latent dataset
#result <- fmri_latent_lm(formula = formula, block = block, dataset = dset,
#                          durations = NULL, drop_empty = TRUE, robust = FALSE)

# Print the result
#print(result)

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