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 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

# Create a LatentNeuroVec, and then create a latent_dataset
# ... (see example for latent_dataset)

# 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)
#> Error in inherits(dataset, "latent_dataset"): object 'dset' not found

# Print the result
print(result)
#> Error in print(result): object 'result' not found