post.shrinkage.minmax: Computes shrinkage of fitted estimates over regressions

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/post.shrinkage.R

Description

post.shrinkage.minmax computes the maximum and minimum fitted estimates, as a function of the mean regression coefficient estimates over all regressions.

Usage

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post.shrinkage.minmax(out, X, vreg, plot=T)

Arguments

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)

Details

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.

Value

a list containing

yrecmin

minimum values of fitted values

yrecmax

maximum values of fitted values

beta

mean of estimated coefficients over all regressions

Author(s)

A. Ferreira da Silva, Universidade Nova de Lisboa, Faculdade de Ciencias e Tecnologia,
afs@fct.unl.pt.

See Also

cudaMultireg.slice, read.fmrislice

Examples

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## 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)

cudaBayesreg documentation built on May 29, 2017, 6:19 p.m.