Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/post.shrinkage.R
post.shrinkage.minmax
computes the maximum and minimum fitted estimates, as a function of the mean regression coefficient estimates over all regressions.
1 | post.shrinkage.minmax(out, X, vreg, plot=T)
|
out |
output of MCMC simulation |
X |
regression matrix used in the simulation |
vreg |
number of the regression coefficient |
plot |
{T,F} output plot (default=T) |
The plot helps visualizing shrinkage by analyzing the influence of the hyperparameter nu on the dispersion of the fitted maximum and minimum estimates. Different shrinkage plots may be compared for simulations with different nu values.
a list containing
yrecmin |
minimum values of fitted values |
yrecmax |
maximum values of fitted values |
beta |
mean of estimated coefficients over all regressions |
A. Ferreira da Silva, Universidade Nova de Lisboa,
Faculdade de Ciencias e Tecnologia,
afs@fct.unl.pt.
cudaMultireg.slice
,
read.fmrislice
1 2 3 4 5 6 7 8 9 10 11 | ## Not run:
slicedata <- read.fmrislice(fbase="fmri", slice=3, swap=FALSE)
ymaskdata <- premask(slicedata)
fsave <- paste(tempdir(),"/simultest1",fileext = ".sav", sep="")
nu1 <- 3
out <- cudaMultireg.slice(slicedata, ymaskdata, R=2000, keep=5, nu.e=nu1,
fsave=fsave1, zprior=FALSE, rng=1)
vreg <- 2
post.shrinkage.minmax(out, slicedata$X, vreg=vreg)
## End(Not run)
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