sim.weightsplot

This function plots the weights interacting between
 estimated effects for each pixel.

Adaptive smoothing functions for estimating the blood
oxygenation level dependent (BOLD) effect by using functional Magnetic
Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random
fields, for real as well as simulated data. The implemented models make
use of efficient Markov Chain Monte Carlo methods. Implemented methods
are based on the research developed by A. Brezger, L. Fahrmeir, A.
Hennerfeind (2007) <https://www.jstor.org/stable/4626770>.

Max Hughes

adaptsmoFMRI

Adaptive Smoothing of FMRI Data

Maximilian Hughes

sim.weightsplot function

<dl> <dt>weights</dt>
<dd>matrix, containing MCMC-output the of
 posterior estimates of weights.</dd>
 <dt>nei</dt>
<dd>matrix, locations of weights in precision
 matrix.</dd>
 <dt>nx</dt>
<dd>scalar, number of pixels in x-direction.</dd>
 <dt>ny</dt>
<dd>scalar, number of pixels in y-direction.</dd>
 <dt>thresh</dt>
<dd>scalar, defining the threshold to which the
 median of the weights smaller than this threshold should
 be plotted.</dd>
 <dt>...</dt>
<dd>graphical parameters for <code>image</code> can
 also be passed on as arguments to this function.</dd></dl>

Arguments

Author

Plot Function for Weights (Simulated Data) — sim.weightsplot

<dl>

 <dt>weights</dt>
<dd>matrix, containing MCMC-output the of
 posterior estimates of weights.</dd>


 <dt>nei</dt>
<dd>matrix, locations of weights in precision
 matrix.</dd>


 <dt>nx</dt>
<dd>scalar, number of pixels in x-direction.</dd>


 <dt>ny</dt>
<dd>scalar, number of pixels in y-direction.</dd>


 <dt>thresh</dt>
<dd>scalar, defining the threshold to which the
 median of the weights smaller than this threshold should
 be plotted.</dd>


 <dt>...</dt>
<dd>graphical parameters for <code>image</code> can
 also be passed on as arguments to this function.</dd>

</dl>

sim.weightsplot: Plot Function for Weights (Simulated Data)

Description

Usage

Value

Arguments

Author