Binds an fmri_template to one subject's dataset_spec. The result is a
fully serializable recipe (no voxel data, no captured environment) that
run() turns into a fitted model and reduced output. Build jobs with
instantiate() rather than by hand in the common case.
Usage
fmri_job(id, template, dataset_spec, meta = list(), nuisance = NULL)Arguments
- id
A unique job identifier (e.g. a subject label).
- template
An fmri_template.
- dataset_spec
A dataset_spec describing this subject's data.
- meta
Optional named list of metadata used for output keying (e.g.
list(subject = "01", task = "stroop", space = "MNI152...")).- nuisance
Optional per-subject nuisance / confound regressors fed to the baseline model at run time:
NULL, a numeric matrix with one row per scan (split across runs), or a list of per-run matrices. (A deferred file-read spec is also accepted byrun().)
See also
instantiate(), run(), export_jobs()
Examples
tmpl <- fmri_template(onset ~ hrf(condition), ~ run)
ds <- dataset_spec("fmri_dataset",
args = list(scans = "run-1_bold.nii.gz", TR = 2,
run_length = 200), source = "file")
fmri_job("sub-01", tmpl, ds, meta = list(subject = "01"))
#> <fmri_job> sub-01
#> dataset: fmri_dataset() [file]
#> meta: subject=01
#> formula: onset ~ hrf(condition)