BDiagTest1.mxPBF {CovTools} | R Documentation |
One-Sample Diagonality Test by Maximum Pairwise Bayes Factor
Description
One-sample diagonality test can be stated with the null hypothesis
and alternative hypothesis
with
. Let
be the
-th column of data matrix. Under the maximum pairwise bayes factor framework, we have following hypothesis,
The model is
Under , the prior is set as
and under , priors are
Usage
BDiagTest1.mxPBF(data, a0 = 2, b0 = 2, gamma = 1)
Arguments
data |
an |
a0 |
shape parameter for inverse-gamma prior. |
b0 |
scale parameter for inverse-gamma prior. |
gamma |
non-negative number. See the equation above. |
Value
a named list containing:
- log.BF.mat
-
matrix of pairwise log Bayes factors.
References
Lee K, Lin L, Dunson D (2018). “Maximum Pairwise Bayes Factors for Covariance Structure Testing.” arXiv preprint. https://arxiv.org/abs/1809.03105.
Examples
## Not run:
## generate data from multivariate normal with trivial covariance.
pdim = 10
data = matrix(rnorm(100*pdim), nrow=100)
## run test
## run mxPBF-based test
out1 = BDiagTest1.mxPBF(data)
out2 = BDiagTest1.mxPBF(data, a0=5.0, b0=5.0) # change some params
## visualize two Bayes Factor matrices
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2), pty="s")
image(exp(out1$log.BF.mat)[,pdim:1], main="default")
image(exp(out2$log.BF.mat)[,pdim:1], main="a0=b0=5.0")
par(opar)
## End(Not run)
[Package CovTools version 0.5.4 Index]