Build a Design Matrix from Block-wise Onsets

`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", "fft", "Rconv", "loop"),
  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()].

Value

A numeric matrix (or sparse matrix) with one column per factor level and one row per sample defined by `sframe`.

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