PreEst.2017Lee {CovTools} | R Documentation |
Bayesian Estimation of a Banded Precision Matrix (Lee 2017)
Description
PreEst.2017Lee
returns a Bayes estimator of the banded precision matrix,
which is defined in subsection 3.3 of Lee and Lee (2017), using the k-BC prior.
The bandwidth is set at the mode of marginal posterior for the bandwidth parameter.
Usage
PreEst.2017Lee(X, upperK = floor(ncol(X)/2), logpi = function(k) {
-k^4
})
Arguments
X |
an |
upperK |
upper bound of bandwidth |
logpi |
log of prior distribution for bandwidth |
Value
a named list containing:
- C
a
(p\times p)
MAP estimate for precision matrix.
References
Lee K, Lee J (2017). “Estimating Large Precision Matrices via Modified Cholesky Decomposition.” ArXiv e-prints.
Examples
## generate data from multivariate normal with Identity precision.
pdim = 5
data = matrix(rnorm(100*pdim), ncol=pdim)
## compare different K
out1 <- PreEst.2017Lee(data, upperK=1)
out2 <- PreEst.2017Lee(data, upperK=3)
out3 <- PreEst.2017Lee(data, upperK=5)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
image(diag(pdim)[,pdim:1], main="Original Precision")
image(out1$C[,pdim:1], main="banded2::upperK=1")
image(out2$C[,pdim:1], main="banded2::upperK=3")
image(out3$C[,pdim:1], main="banded2::upperK=5")
par(opar)
[Package CovTools version 0.5.4 Index]