Usage
adaptiveGMRF(data, hrf, approximate = FALSE, K = 500, a = 0.001, b = 0.001, c = 0.001, d = 0.001, nu = 1, filter = NULL, block = 1, burnin = 1, thin = 1)
Arguments
data
fMRI-data, needs to be an array of dimension
(dx x dy x T).
hrf
haemodynamic response function, needs to be a
vector of length T.
approximate
logical, if TRUE then the
approximate case is choosen. 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
($\tau$).
filter
scalar, a value between 0 and 1 defining to
which extent the fMRI-data should be filtered. The
corresponding formular is max(fmri)*filter.
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.