This function estimates the effects of functional MR Images (fMRI), with the method of efficient Markov Chain Monte Carlo (MCMC) simulation. The Metropolis Hastings (MH) algorithm is used for the nonapproximate case and the Gibbs sampler for the approximate case.
1 2 3 
data 
fMRIdata, needs to be an array of dimension

hrf 
haemodynamic response function, needs to be a
vector of length 
approximate 
logical, if 
K 
scalar, length of the MCMC path, hence iteration steps. 
a 
scalar, shape hyperparameter of the inversegamma distribution of the variance parameter (σ_i^2). 
b 
scalar, scale hyperparameter of the inverse gamma distribution of the variance parameter (σ_i^2). 
c 
scalar, shape hyperparameter of the inverse gamma distribution of the precision parameter (τ). 
d 
scalar, scale hyperparameter of the inverse gamma distribution of the precision parameter (τ). 
filter 
scalar, a value between 0 and 1 defining to
which extent the fMRIdata should be filtered. The
corresponding formular is 
nu 
scalar, shape and scale hyperparameter of the gamma distribution of the interaction weights (w_{ij}). 
block 
scalar, when 
burnin 
scalar, defining the first iteration steps which should be omitted from MCMC path. 
thin 
scalar, only every 
This function is solely for two covariates and real data sets.
Max Hughes
1  # See example function for simulated data (one covariate).

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.