Linear Model for FMRI Data

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

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

References

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

Author

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

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")