# Linear Model for FMRI Data

### Description

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

### Usage

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

### Arguments

`stimulus` |
matrix containing expected BOLD repsonse(s) for the linear model as columns |

`order` |
order of the polynomial drift terms |

`cef` |
confounding effects |

`verbose` |
Report more if |

### 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

### References

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

### See Also

`fmri.stimulus`

, `fmri.lm`

### Examples

1 2 3 4 5 | ```
# 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")
``` |

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