adaptsmoFMRI (version 1.1)

sim.adaptiveGMRF: Adaptive GMRF Model for Simulated Data

Description

This function estimates the effects of a synthetic spatiotemporal data set resembling functional MR Images (fMRI), with the method of efficient Markov Chain Monte Carlo (MCMC) simulation. The Metropolis Hastings (MH) algorithm is used for the non-approximate case and the Gibbs sampler for the approximate case.

Usage

sim.adaptiveGMRF(data, hrf, approximate = FALSE, K = 500, a = 1, b = 1, c = 1, d = 1, nu = 1, block = 1, burnin = 1, thin = 1)

Arguments

data

simulated fMRI-data, needs to be an array of dimension (20 x 20 x T).

hrf

haemodynamic response function, needs to be a vector of length T.

approximate

logical, if TRUE then the approximate case is chosen. Default is FALSE.

K

scalar, length of the MCMC path, hence iteration steps.

a

scalar, shape hyperparameter of the inverse-gamma distribution of the variance parameter ($\sigma_i^2$).

b

scalar, scale hyperparameter of the inverse gamma distribution of the variance parameter ($\sigma_i^2$).

c

scalar, shape hyperparameter of the inverse gamma distribution of the precision parameter ($\tau$).

d

scalar, scale hyperparameter of the inverse gamma distribution of the precision parameter ($\tau$).

nu

scalar, shape and scale hyperparameter of the gamma distribution of the interaction weights ($w_{ij}$).

block

scalar, when approximate==TRUE then a block of weights is updated at a time.

burnin

scalar, defining the first iteration steps which should be omitted from MCMC path.

thin

scalar, only every thin step of MCMC path is saved to output.

Examples

# non-transformed hr-function
T <- 210
seq.length <- T*3
index <- seq(3, T*3, by = 3)
hrf <- rep(c(-0.5, 0.5), each=30, times=ceiling(T/30*1.5))
hrf <- as.matrix(hrf[index])
# get simulated data
data("sim_fmri")
data <- data_simfmri
# execute function
set.seed(111222)
K <- 2
a <- b <- c <- d <- nu <- 1
test.sim.adaptive <- sim.adaptiveGMRF(data, hrf, approximate=TRUE, K,
                                      a, b, c, d, nu)