adaptiveGMRF2COVAR: Adaptive GMRF Model (Real Data)

In adaptsmoFMRI: Adaptive Smoothing of FMRI Data

Adaptive GMRF Model (Real Data)

Description

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.

Usage

  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)

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. Def#' ault 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 (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 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.

Value

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.

Note

This function is solely for two covariates and real data sets.

Author(s)

Maximilian Hughes

Examples

# See example function for simulated data (one covariate).

adaptsmoFMRI documentation built on Sept. 25, 2022, 5:06 p.m.