# adaptiveGMRF2COVAR: Adaptive GMRF Model (Real Data) In adaptsmoFMRI: Adaptive Smoothing of FMRI 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

 ```1 2 3``` ``` 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.

## Note

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

Max Hughes

## Examples

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

adaptsmoFMRI documentation built on May 29, 2017, 12:09 p.m.