fmri.design: Linear Model for FMRI Data
In fmri: Analysis of fMRI Experiments

fmri.design

R Documentation

Linear Model for FMRI Data

Description

Return a design matrix for a linear model with given stimuli and possible polynomial drift terms.

Usage

  fmri.design(stimulus, order = 2, cef = NULL, verbose = FALSE)

Arguments

`stimulus`	matrix containing expected BOLD response(s) for the linear model as columns or list of expected BOLD responses containing matrices of dimension `scans, number of slices` as returned by function `fmri.stimulus`.
`order`	order of the polynomial drift terms
`cef`	confounding effects
`verbose`	Report more if `TRUE`

Details

The stimuli given in stimulus are used as first columns in the design matrix.

The order of the polynomial drift terms is given by order, which defaults to 2.

Confounding effects can be included in a matrix cef.

The polynomials are defined orthogonal to the stimuli given in stimulus.

Value

design matrix of the linear model

Author(s)

Karsten Tabelow tabelow@wias-berlin.de, Joerg Polzehl polzehl@wias-berlin.de

References

Polzehl, J. and Tabelow, K.(2007). fmri: A Package for Analyzing fmri Data, R News, 7:13-17 .

Examples

  # Example 1
  hrf <- fmri.stimulus(107, c(18, 48, 78), 15, 2)
  z <- fmri.design(hrf, 2)
  par(mfrow=c(2, 2))
  for (i in 1:4) plot(z[, i], type="l")

fmri documentation built on Sept. 8, 2023, 5:23 p.m.