View source: R/adaptive-gmrf.R
adaptiveGMRF | 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 non-approximate case and the Gibbs sampler for the approximate case.
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)
data |
fMRI-data, 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 inverse-gamma 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 fMRI-data 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 |
dx |
scalar, number of pixels in x-direction. |
dy |
scalar, number of pixels in y-direction. |
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 one covariate and real data sets.
Maximilian Hughes
# See example function for simulated data (one covariate).
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