fmri.designG: Design matrix for fMRI group analysis
In neuroconductor-releases/fmri: Analysis of fMRI Experiments

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

This function returns a design matrix for multi-subject fMRI data to fit a Linear Mixed-effects Model (one-stage procedure) with given stimuli, polynomial drift terms and a set of known population parameters.

1	fmri.designG(hrf, subj = 1, runs = 1, group = NULL, XG = NULL)

`hrf`	vector or matrix containing expected BOLD response(s) for one session, typically a `fmri.stimulus` object.
`subj`	number of subjects in the study.
`runs`	number of repeated measures within subjects.
`group`	optional vector to define groups. It is expected one value per subject. A grouping factor can also be part of `XG`.
`XG`	optionally, a group-level design matrix of class `"data.frame"`, which contains population parameters such as ages or gender corresponding to the subjects. It is expected one value per subject.

Based on the dimensionality of the hrf object, which provides the total number of scans (time-points) within each session, the entered number of subjects and repeated measures the auxiliary variables: "subj", "run", "scan" and "session" are generated as first part of the returned design matrix.

If no group argument is specified, only one population will be assumed; otherwise group labels are replicated within sessions of the same subject.

First a design matrix for a single run is created by calling: x <- fmri.design(hrf, order = 2). Hence the polynomial drift terms are defined orthogonal to the stimuli (see fmri.design). This matrix is replicated blockwise to all sessions assuming the same experimental design for all runs. The first drift term, a column of ones, is called "drift0" and models an intercept.

If given, further subject characteristics are filled in the design matrix.

A design matrix as a data frame, which contains the following variables:

`subj`	consecutive subject number: 1 to `subj` specified as factor
`run`	consecutive run number within the subjects: 1 to `runs` specified as factor
`scan`	consecutive scan number: 1 to T within each session
`session`	consecutive experiment number: 1 to `(subj*runs)` specified as factor
`group`	grouping variable specified as factor, one group by default
`hrf, hrf2, ...`	replicated expected BOLD-response(s)
`drift0, drift1, drift2`	replicated polynomial drift terms created with `fmri.design(hrf, order = 2)` orthogonal to the stimuli given in `hrf`
`...`	further expanded between-subject factors and covariates

Sibylle Dames

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

fmri.stimulus, fmri.design, fmri.lmePar

subj <- 6
runs <- 1
scans <- 121
times <- c(12, 48, 84, 120, 156, 192, 228, 264) 
duration <- 24          
tr <- 2.5

hrf <- fmri.stimulus(scans, times, duration, tr, times = TRUE)
x.group <- fmri.designG(hrf, subj = subj, runs = runs)
# View(x.group)