Fast fMRI Regression Model Estimation from a Latent Component Dataset

This function estimates a regression model for fMRI data using a latent component dataset. The dataset must be an fmri_frame whose feature space is a fmridataset::basis_space (see latent_frame()), so that the assay holds component scores and the space carries the spatial loadings.

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 latent fmri_frame (see latent_frame()).

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)