cudaBayesreg (version 0.3-13)

post.ppm: Posterior Probability Map (PPM) image

Description

post.ppm computes the PPM image of voxel activations in a slice.

Usage

post.ppm(out, slicedata, ymaskdata, vreg=2, swap=FALSE, plot=TRUE,
  col=heat.colors(256))

Value

a list containingppmppm image as matrixnactiven. of active voxels

concept

PPM Image

Details

Use the MCMC draws to estimate the Posterior Probability Map (PPM) image. The number of regression variables used in the simulation is equal to the number of columns specified in the design matrix, plus an intercept term; vreg=1 represents the intercept term in regression.

References

Adelino Ferreira da Silva (2011). ``A Bayesian Multilevel Model for fMRI Data Analysis.'', Computer Methods and Programs in Biomedicine, 102,(3), 238--252.

See Also

cudaMultireg.slice, regpostsim, post.simul.hist, post.tseries

Examples

slicedata <- read.fmrislice(fbase="fmri", slice=3, swap=FALSE)
ymaskdata <- premask(slicedata)
fsave <- paste(tempdir(),"/simultest1",fileext = ".sav", sep="")
answ <- readline("Run MCMC simulation first ? ")
run <- FALSE
if (substr(answ, 1, 1) == "y") { run <- TRUE }
if(run) {
	out <- cudaMultireg.slice(slicedata, ymaskdata, R=2000, keep=5,
    nu.e=3, zprior=FALSE)
} else {
	load(file=fsave)
	cat("loaded",fsave,"\n")
}
post.ppm(out=out, slicedata=slicedata, ymaskdata=ymaskdata, vreg=2)
post.ppm(out=out, slicedata=slicedata, ymaskdata=ymaskdata, vreg=4)