buildzstat.volume

Indicates the data set prefix of the fMRI data set to use.
  The prefix applies to data files: <file>{fbase}_filtered.nii.gz</file>,
    <file>{fbase}_mask.nii.gz</file>, and <file>{fbase}_design.txt</file>.Two test data sets are included in the packag

fbase

regression variable to represent in PPM; default(vreg=2)

vreg

d.f. parameter for regression error variance prior (def: 3)

nu.e

rg=c(first, last): a vector containing the first and last
   numbers of the sequence of slices to be processed.
    If rg=c(NULL,NULL) (default), all slices in the volume are processed.

logical variable (default = <samp>FALSE</samp>) for choosing the
    right/left data display convention consistent with FSLVIEW

swap

numeric value (default=<samp>3</samp>). Applies spatial contextual
    information in a 3D immediate neighbourhood for eliminating 3D blobs
    with less than <samp>blobsize</samp> active voxels.

blobsize

Directory (def: <samp>"<tmp>"</tmp>) were the MCMC simulations for all
    slices are saved.</samp>

savedir

<code>buildzstat.volume</code> builds a PPM statistical volume
  from slices processed by <code>cudaMultireg.volume</code>.

Compute Unified Device Architecture (CUDA) is a software
        platform for massively parallel high-performance computing on
        NVIDIA GPUs. This package provides a CUDA implementation of a
        Bayesian multilevel model for the analysis of brain fMRI data.
        A fMRI data set consists of time series of volume data in 4D
        space. Typically, volumes are collected as slices of 64 x 64
        voxels. Analysis of fMRI data often relies on fitting linear
        regression models at each voxel of the brain. The volume of the
        data to be processed, and the type of statistical analysis to
        perform in fMRI analysis, call for high-performance computing
        strategies. In this package, the CUDA programming model uses a
        separate thread for fitting a linear regression model at each
        voxel in parallel. The global statistical model implements a
        Gibbs Sampler for hierarchical linear models with a normal
        prior. This model has been proposed by Rossi, Allenby and
        McCulloch in `Bayesian Statistics and Marketing', Chapter 3,
        and is referred to as `rhierLinearModel' in the R-package
        bayesm. A notebook equipped with a NVIDIA `GeForce 8400M GS'
        card having Compute Capability 1.1 has been used in the tests.
        The data sets used in the package's examples are available in
        the separate package cudaBayesregData.

Adelino da Silva

cudaBayesreg

CUDA Parallel Implementation of a Bayesian Multilevel Model for
        fMRI Data Analysis

buildzstat.volume function

<ul><li>bayes</li><li>linear model</li><li>NIFTI</li></ul>

buildzstat.volume: Build a Posterior Probability Map (PPM) NIFTI volume

Description

Usage

Arguments

concept