cudaBayesreg (version 0.3-13)

cudaMultireg.slice: CUDA Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis on a fMRI slice

Description

cudaMultireg.slice provides an interface to a CUDA implementation of a Bayesian multilevel model for the analysis of brain fMRI data. cudaMultireg.slice processes a single slice.

Usage

cudaMultireg.slice(slicedata, ymaskdata, R, keep = 5, nu.e = 3,
	fsave = NA, zprior=FALSE, rng = 0)

Arguments

slicedata

list(slice=slice, niislicets=niislicets, mask=mask, dsgn=dsgn); input slice data used in simulation as returned by read.fmrislice. See read.fmrislice for indications on how to process user defined datasets.

ymaskdata

list(yn = yn, kin = kin, nreg = nreg); masked and standardised slice data as returned by premask

R

number of MCMC draws

keep

MCMC thinning parameter: keep every keepth draw (def: 5)

nu.e

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

fsave

filename for saving the MCMC simulation (def: NULL do not save)

zprior

boolean {T,F}; default {F} - use just a mean for Z

rng

integer {0,1,2}: type of RNG to use {0-Marsaglia Multicarry, 1-R. P. Brent xorgens, 2-Mersenne Twister MT19937-64}; (def. 0-Marsaglia Multicarry)

Value

• a list containing
• betadraw
  nreg x nvar x R/keep array of individual regression coef draws
• taudraw
  R/keep x nreg array of error variance draws
• Deltadraw
  R/keep x nz x nvar array of Deltadraws
• Vbetadraw
  R/keep x nvar*nvar array of Vbeta draws

concept

• bayes
• MCMC
• Gibbs Sampling
• hierarchical models
• linear model

Details

The statistical model implemented in CUDA was specified as a Gibbs Sampler for hierarchical linear models with a normal prior. This model has been analysed by Rossi, Allenby and McCulloch in Bayesian Statistics and Marketing, Chapt. 3, and is referred to as rhierLinearModel in the R package bayesm. The main computational work is done in parallel on a CUDA capable GPU. Each thread is responsible for fitting a general linear model at each voxel. The CUDA implementation has the following system requirements: nvcc NVIDIA Cuda Compiler driver, g++ GNU compiler (nvcc compatible version). The package includes source code files to build the library newmat11.so. This is a matrix library by R. B. Davies used in the package's host C++ code. The package includes three optional CUDA-based RNGs. Marsaglia's multicarry RNG follows the R implementation, is the fastest one, and is used by default; Brent's RNG has higher quality but is not-so-fast; Matsumoto's Mersenne Twister is slow. The data sets used in the examples are available in the R package cudaBayesregData.

References

Adelino R. Ferreira da Silva (2011). ``cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis.'' Journal of Statistical Software, 44(4), 1--24. URL http://www.jstatsoft.org/v44/i04/. Adelino Ferreira da Silva (2011). cudaBayesregData: Data sets for the examples used in the package cudaBayesreg, R package version 0.3-10. URL http://CRAN.R-project.org/package=cudaBayesregData. Adelino Ferreira da Silva (2011). ``A Bayesian Multilevel Model for fMRI Data Analysis.'', Computer Methods and Programs in Biomedicine, 102,(3), 238--252. Adelino Ferreira da Silva (2010). ``cudaBayesreg: Bayesian Computation in CUDA.'', The R Journal, 2/2, 48-55. URL http://journal.r-project.org/archive/2010-2/RJournal_2010-2_Ferreira~da~Silva.pdf. Rossi, Allenby and McCulloch. Bayesian Statistics and Marketing, Chapter 3. URL http://faculty.chicagogsb.edu/peter.rossi/research/bsm.html. Davies, R.B. (1994). Writing a matrix package in C++. In OON-SKI'94: The second annual object-oriented numerics conference, pp 207-213. Rogue Wave Software, Corvallis. URL http://www.robertnz.net/cpp_site.html. Richard. P. Brent. Some long-period random number generators using shifts and xors, Preprint: 2 July 2007. Brandon Whitcher, Volker Schmid and Andrew Thornton (2011). oro.nifti: Rigorous - NIfTI Input / Output, R package version 0.2.5. URL http://CRAN.R-project.org/package=oro.nifti.

See Also

read.fmrislice, read.Zsegslice, premask, pmeans.hcoef, regpostsim, plot.hcoef.post, post.simul.hist, post.ppm, post.tseries, post.randeff, post.shrinkage.mean

Examples

## Simulation using the visual/auditory test dataset "fmri"  
slicedata <- read.fmrislice(fbase="fmri", slice=3, swap=FALSE)
ymaskdata <- premask(slicedata)
fsave <- paste(tempdir(),"/simultest1",fileext = ".sav", sep="")
out <- cudaMultireg.slice(slicedata, ymaskdata, R=2000, keep=5, nu.e=3,
  fsave=fsave, zprior=FALSE, rng=0 )
## Post-processing simulation
post.ppm(out=out, slicedata=slicedata, ymaskdata=ymaskdata, vreg=2)
post.ppm(out=out, slicedata=slicedata, ymaskdata=ymaskdata, vreg=4)
## "bayesm" summaries 
require("bayesm")
summary(out$betadraw)
summary(out$Deltadraw)
plot(out$Deltadraw)
summary(out$Vbetadraw)
##
## Random effects simulation using the SPM auditory dataset "swrfM*" 
fbase <- "swrfM"
slice <- 21
slicedata <- read.fmrislice(fbase=fbase, slice=slice, swap=FALSE )
ymaskdata <- premask(slicedata)
fsave <- paste(tempdir(),"/simultest3",fileext = ".sav", sep="")
out <- cudaMultireg.slice(slicedata, ymaskdata, R=2000, keep=5, nu.e=3,
  fsave=fsave, zprior=TRUE, rng=1)
post.ppm(out=out, slicedata=slicedata, ymaskdata=ymaskdata, vreg=2)