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

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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`

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.

design matrix of the linear model

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

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

fmri.stimulus, fmri.lm

  # 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")