Description Usage Arguments Details Author(s) References See Also Examples
Plots draws of the random effects distribution, following the specification of cross-sectional units (group information) in the Z matrix of the statistical model.
1 | post.randeff(out, classnames=NULL, climits=TRUE)
|
out |
output of MCMC simulation |
classnames |
default=NULL; concatenation of unit (class member) names used in the Z matrix specification. The argument may be a subvector of all unit names, but the classnames given in the argument must match the order used in the Z matrix specification. If no class names are given (default) only the draws of the mean of the random effects distribution are plotted. |
climits |
logical variable (default = TRUE): if TRUE plots for the class draws use a commom ylim parameter. |
The statistical model allows for the analysis of random effects through the
specification of the Z matrix in the prior,
beta_i ~ N(ZDelta[i,],V_{beta}).
The example included in the package (‘fbase="swrfM"’) defines a partition of the
fMRI dataset in 3 classes, associated with 3 brain regions: CSF, gray matter
and white matter (see examples).
A. Ferreira da Silva, Universidade Nova de Lisboa,
Faculdade de Ciencias e Tecnologia,
afs@fct.unl.pt.
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). “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.
cudaMultireg.slice
,
read.Zsegslice
,
read.fmrislice
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
## Random effects simulation using the SPM auditory dataset "swrfM*"
fbase <- "swrfM"
slice <- 21
slicedata <- read.fmrislice(fbase=fbase, slice=slice)
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)
## show random effects for 3 classes
post.randeff(out, classnames=c("CSF","GRY","WHT"))
## End(Not run)
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