adaptiveGMRF2COVAR {adaptsmoFMRI}  R Documentation 
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.
adaptiveGMRF2COVAR(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)
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
( 
b 
scalar, scale hyperparameter of the inverse
gamma distribution of the variance parameter
( 
c 
scalar, shape hyperparameter of the inverse
gamma distribution of the precision parameter
( 
d 
scalar, scale hyperparameter of the inverse
gamma distribution of the precision parameter
( 
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
( 
block 
scalar, when 
burnin 
scalar, defining the first iteration steps which should be omitted from MCMC path. 
thin 
scalar, only every 
dx 
scalar, number of pixels in xdirection. 
dy 
scalar, number of pixels in ydirection. 
I 
scalar, number of pixels. 
coord 
matrix, coordinates of pixels. 
NEI 
scalar, number of weights. 
nei 
matrix, locations of weights in precision matrix. 
mask 
matrix, masked out pixels. 
beta.out 
matrix, MCMC path of covariates. 
w.out 
matrix, MCMC path of weights. 
sigma.out 
matrix, MCMC path of variance parameters. 
tauk.out 
matrix, MCMC path of hyper parameters. 
This function is solely for two covariates and real data sets.
Maximilian Hughes
# See example function for simulated data (one covariate).