Computation of Bayes factors at the skeleton points
Description
Function to compute the Bayes factors from MCMC samples.
Usage
1 2 
Arguments
runs 
A list with outputs from the function

bfsize1 
A scalar or vector of the same length as

method 
Which method to use to calculate the Bayes factors: Reverse logistic or MengWong. 
reference 
Which model goes in the denominator. 
transf 
Whether to use the transformed sample mu for the computations. Otherwise it uses z. 
binwo 
For the binomial family, if use workaround when the untransformed sample is used. 
Details
Computes the Bayes factors using method
with respect to
reference
.
Value
A list with components

logbf
A vector containing logarithm of the Bayes factors. 
logLik1
logLik2
Matrices with the values of the loglikelihood computed from the samples for each model at the first and second stages. 
isweights
A vector with the importance sampling weights for computing the Bayes factors at new points that will be used at the second stage. Used internally inbf2new
andbf2optim
. 
controlvar
A matrix with the control variates computed at the samples that will be used in the second stage. 
sample2
The MCMC sample for mu or z that will be used in the second stage. Used internally inbf2new
andbf2optim
. 
N1
,N2
Vectors containing the sample sizes used in the first and second stages. 
distmat
Matrix of distances between locations. 
betm0
,betQ0
,ssqdf
,ssqsc
,tsqdf
,tsqsc
,dispersion
,response
,weights
,modelmatrix
,locations
,family
,corrfcn
,transf
Model parameters used internally in.bf2new
andbf2optim
. 
pnts
A list containing the skeleton points. Used internally inbf2new
andbf2optim
.
References
Geyer, C. J. (1994). Estimating normalizing constants and reweighting mixtures. Technical report, University of Minnesota.
Meng, X. L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistica Sinica, 6, 831860.
Roy, V., Evangelou, E., and Zhu, Z. (2015). Efficient estimation and prediction for the Bayesian spatial generalized linear mixed model with flexible link functions. Biometrics. http://dx.doi.org/10.1111/biom.12371
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  ## Not run:
data(rhizoctonia)
### Define the model
corrf < "spherical"
kappa < 0
ssqdf < 1
ssqsc < 1
betm0 < 0
betQ0 < .01
linkp < "probit"
### Skeleton points
philist < c(100, 140, 180)
omglist < c(.5, 1)
parlist < expand.grid(phi=philist, linkp=linkp, omg=omglist, kappa = kappa)
### MCMC sizes
Nout < 100
Nthin < 1
Nbi < 0
### Take MCMC samples
runs < list()
for (i in 1:NROW(parlist)) {
runs[[i]] < mcsglmm(Infected ~ 1, 'binomial', rhizoctonia, weights = Total,
atsample = ~ Xcoord + Ycoord,
Nout = Nout, Nthin = Nthin, Nbi = Nbi,
betm0 = betm0, betQ0 = betQ0,
ssqdf = ssqdf, ssqsc = ssqsc,
phistart = parlist$phi[i], omgstart = parlist$omg[i],
linkp = parlist$linkp[i], kappa = parlist$kappa[i],
corrfcn = corrf, phisc = 0, omgsc = 0)
}
bf < bf1skel(runs)
bf$logbf
## End(Not run)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.