fmridesign front end for glmsingle() — glmsingle

Fits glmsingle() using an fmridesign::event_model() to describe the trials. The event model must contain one event term with a single factor whose levels are the conditions (repeated conditions drive cross-validation). Onsets must lie on the TR grid.

Usage

glmsingle_design(Y, event_model, baseline_model = NULL, stimdur = NULL, ...)

Arguments

Y: Time x voxel data matrix covering all runs of the sampling frame.
event_model: An event_model from fmridesign.
baseline_model: Optional baseline_model from fmridesign.
stimdur: Trial duration in seconds. Default: the event durations, which must then be a single positive value.
...: Further arguments passed to glmsingle().

Value

A glmsingle_fit; see glmsingle().

Details

A baseline_model contributes its nuisance term (e.g. motion) as extra_regressors. Its drift and block terms are not used: GLMsingle models drift with its own per-run polynomials (max_poly_deg).

Examples

set.seed(1)
sf <- fmrihrf::sampling_frame(c(80L, 80L), TR = 1)
events <- data.frame(onset = rep(c(5, 20, 35, 50), 2),
                     run = rep(1:2, each = 4),
                     stimulus = factor(rep(c("A", "B"), 4)))
em <- fmridesign::event_model(
  onset ~ fmridesign::hrf(stimulus), data = events, block = ~run,
  sampling_frame = sf, durations = rep(2, nrow(events)))
Y <- matrix(100 + rnorm(160 * 4), 160, 4)
fit <- glmsingle_design(Y, em, want_glmdenoise = FALSE,
                        want_fracridge = FALSE, verbose = FALSE)
dim(coef(fit, type = "b"))
#> [1] 8 4