This function takes the output from convolve_design_matrix and transforms it into a design suitable for sampling with EMC2. It properly configures parameter types, bounds, and transformations for the specified model.
Examples
# Generate a simple example timeseries
ts <- data.frame(
subjects = rep(1, 100),
run = rep(1, 100),
time = cumsum(rep(1.38, 100)),
ROI1 = rnorm(100)
)
# Generate example events
events <- data.frame(
subjects = rep(1, 4),
run = rep(1, 4),
onset = c(10, 30, 50, 70),
duration = rep(0.5, 4),
event_type = c("A", "B", "A", "B"),
modulation = c(1, 1, 1, 1)
)
# Create convolved design matrix
design_matrix <- convolve_design_matrix(
timeseries = ts,
events = events,
factors = list(condition = c("A", "B")),
hrf_model = "glover"
)
#> event_type subjects run onset duration modulation regressor
#> 1 A 1 1 30 0.5 1 conditionB
#> 2 B 1 1 70 0.5 1 conditionB
#> Filtering out high_pass noise, make sure you also use high_pass_filter(<timeseries>)
# Create fMRI design for EMC2
fmri_design <- design_fmri(design_matrix, model = MRI_AR1)