bf1skel | R Documentation |
Function to compute the Bayes factors from MCMC samples.
bf1skel(
runs,
bfsize1 = 0.8,
method = c("RL", "MW"),
reference = 1,
transf = c("no", "mu", "wo")
)
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 Meng-Wong. |
reference |
Which model goes in the denominator. |
transf |
Whether to use a transformed sample for the
computations. If |
Computes the Bayes factors using method
with respect to
reference
.
A list with components
logbf
A vector containing logarithm of the Bayes factors.
logLik1
logLik2
Matrices with the values of
the log-likelihood 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 in
bf2new
and bf2optim
.
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 in
bf2new
and bf2optim
.
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
and bf2optim
.
pnts
A list containing the skeleton points. Used
internally in bf2new
and bf2optim
.
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, 831-860.
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, 72(1), 289-298.
## Not run:
data(rhizoctonia)
### Define the model
corrf <- "spherical"
kappa <- 0
ssqdf <- 1
ssqsc <- 1
betm0 <- 0
betQ0 <- .01
family <- "binomial.probit"
### Skeleton points
philist <- c(100, 140, 180)
omglist <- c(.5, 1)
parlist <- expand.grid(linkp=0, phi=philist, 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, family, rhizoctonia, weights = Total,
atsample = ~ Xcoord + Ycoord,
Nout = Nout, Nthin = Nthin, Nbi = Nbi,
betm0 = betm0, betQ0 = betQ0,
ssqdf = ssqdf, ssqsc = ssqsc,
phi = parlist$phi[i], omg = parlist$omg[i],
linkp = parlist$linkp[i], kappa = parlist$kappa[i],
corrfcn = corrf,
corrtuning=list(phi = 0, omg = 0, kappa = 0))
}
bf <- bf1skel(runs)
bf$logbf
## End(Not run)
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