`regressor_design` extends [regressor_set()] by allowing onsets to be specified relative to individual blocks and by directly returning the evaluated design matrix.
Usage
regressor_design(
onsets,
fac,
block,
sframe,
hrf = HRF_SPMG1,
duration = 0,
amplitude = 1,
span = 40,
precision = 0.33,
method = c("conv", "loop", "fft", "Rconv"),
sparse = FALSE,
summate = TRUE
)Arguments
- onsets
Numeric vector of event onset times, expressed relative to the start of their corresponding block.
- fac
A factor (or object coercible to a factor) indicating the condition for each onset.
- block
Integer vector identifying the block for each onset. Values must be valid block indices for `sframe`.
- sframe
A [sampling_frame] describing the temporal structure of the experiment.
- hrf
Hemodynamic response function shared by all conditions.
- duration
Numeric scalar or vector of event durations.
- amplitude
Numeric scalar or vector of event amplitudes.
- span
Numeric scalar giving the HRF span in seconds.
- precision
Numeric precision used during convolution.
- method
Evaluation method passed to [evaluate()].
- sparse
Logical; if `TRUE` a sparse design matrix is returned.
- summate
Logical; passed to [regressor()].
Each block (run) is convolved separately: the rows of a block depend only on the events of that block, so a response that starts near the end of one run is truncated at the run boundary rather than carried into the next run.
Value
A numeric matrix (or sparse matrix) with one row per sample defined by `sframe` (blocks stacked in order) and `nbasis(hrf)` columns per factor level, in the order of `levels(fac)`. Columns are named after the levels; when `nbasis(hrf) > 1` they are named `<level>_basis<k>`.
Examples
# Create a sampling frame for 2 blocks, 100 scans each, TR=2
sframe <- sampling_frame(blocklens = c(100, 100), TR = 2)
# Events in block-relative time
onsets <- c(10, 30, 50, 20, 40, 60)
conditions <- factor(c("A", "B", "A", "B", "A", "B"))
blocks <- c(1, 1, 1, 2, 2, 2)
# Build design matrix
design <- regressor_design(
onsets = onsets,
fac = conditions,
block = blocks,
sframe = sframe,
hrf = HRF_SPMG1
)
# Design matrix has 200 rows (total scans) and 2 columns (conditions)
dim(design)
#> [1] 200 2