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Pre-defined Hemodynamic Response Function Objects

Source: R/hrf.R
HRF_objects.Rd

A collection of pre-defined HRF objects for common fMRI analysis scenarios. These objects can be used directly in model specifications or as templates for creating custom HRFs.

Usage

HRF_GAMMA(t)

HRF_GAUSSIAN(t)

HRF_SPMG1(t)

HRF_SPMG2(t)

HRF_SPMG3(t)

HRF_BSPLINE(t)

HRF_FIR(t)

Arguments

t

Numeric vector of time points (in seconds) at which to evaluate the HRF

Value

When called as functions, return numeric vectors or matrices of HRF values. When used as objects, they are HRF objects with class c("HRF", "function").

Canonical HRFs

HRF_SPMG1

SPM canonical HRF (single basis function)

HRF_SPMG2

SPM canonical HRF with temporal derivative (2 basis functions)

HRF_SPMG3

SPM canonical HRF with temporal and dispersion derivatives (3 basis functions)

HRF_GAMMA

Gamma function-based HRF

HRF_GAUSSIAN

Gaussian function-based HRF

Flexible Basis Sets

HRF_BSPLINE

B-spline basis HRF (5 basis functions)

HRF_FIR

Finite Impulse Response (FIR) basis HRF (12 basis functions)

Creating Custom Basis Sets

The pre-defined objects above have fixed numbers of basis functions. To create basis sets with custom parameters (e.g., different numbers of basis functions), use one of these approaches:

Using getHRF():

  • getHRF("fir", nbasis = 20) - FIR basis with 20 functions

  • getHRF("bspline", nbasis = 10, span = 30) - B-spline with 10 functions

  • getHRF("fourier", nbasis = 7) - Fourier basis with 7 functions

  • getHRF("daguerre", nbasis = 5, scale = 3) - Daguerre basis

Using generator functions directly:

  • hrf_fir_generator(nbasis = 20, span = 30)

  • hrf_bspline_generator(nbasis = 10, span = 30)

  • hrf_fourier_generator(nbasis = 7, span = 24)

  • hrf_daguerre_generator(nbasis = 5, scale = 3)

Usage

All HRF objects can be:

  • Called as functions with time argument: HRF_SPMG1(t)

  • Used in model specifications: hrf(condition, basis = HRF_SPMG1)

  • Evaluated with evaluate() method

  • Combined with decorators like lag_hrf() or block_hrf()

See also

evaluate.HRF for evaluating HRF objects, gen_hrf for creating HRFs with decorators, list_available_hrfs for listing all HRF types, getHRF for creating HRFs by name with custom parameters, hrf_fir_generator, hrf_bspline_generator, hrf_fourier_generator, hrf_daguerre_generator for creating custom basis sets directly

Other hrf: deriv(), penalty_matrix()

Examples

# Evaluate HRFs at specific time points
times <- seq(0, 20, by = 0.5)

# Single basis canonical HRF
canonical_response <- HRF_SPMG1(times)
plot(times, canonical_response, type = "l", main = "SPM Canonical HRF")


# Multi-basis HRF with derivatives
multi_response <- HRF_SPMG3(times)  # Returns 3-column matrix
matplot(times, multi_response, type = "l", main = "SPM HRF with Derivatives")


# Gamma and Gaussian HRFs
gamma_response <- HRF_GAMMA(times)
gaussian_response <- HRF_GAUSSIAN(times)

# Compare different HRF shapes
plot(times, canonical_response, type = "l", col = "blue", 
     main = "HRF Comparison", ylab = "Response")
lines(times, gamma_response, col = "red")
lines(times, gaussian_response, col = "green")
legend("topright", c("SPM Canonical", "Gamma", "Gaussian"), 
       col = c("blue", "red", "green"), lty = 1)


# Create custom FIR basis with 20 bins
custom_fir <- getHRF("fir", nbasis = 20, span = 30)
fir_response <- evaluate(custom_fir, times)
matplot(times, fir_response, type = "l", main = "Custom FIR with 20 bins")


# Create custom B-spline basis  
custom_bspline <- hrf_bspline_generator(nbasis = 8, span = 25)
bspline_response <- evaluate(custom_bspline, times)
matplot(times, bspline_response, type = "l", main = "Custom B-spline with 8 basis functions")