Compute the standard errors for the Bayes factors estimates.
bfse(pargrid, runs, nbatches, bfsize1 = 0.8, method = c("RL", "MW"),
reference = 1, transf = FALSE, bmmcse.size = "sqroot")A data frame with components "linkp", "phi", "omg", "kappa". Each row gives a combination of the parameters to compute the new standard errors.
An integer scalar or vector of the same length as runs indicating the number of batches to create for computing the variance using the samples of the first stage.
A scalar or vector of the same length as
runs with all integer values or all values in (0, 1]. How
many samples (or what proportion of the sample) to use for
estimating the Bayes factors at the first stage. The remaining
sample will be used for estimating the standard errors in the
second stage. Setting it to 1 will perform only the first stage.
Which method to use to calculate the Bayes factors: Reverse logistic or Meng-Wong.
Which model goes in the denominator.
Whether to use the transformed sample mu for the computations. Otherwise it uses z.
Size for computing the batch means Monte-Carlo variance using the samples of the second stage.
A list with components
pargrid The inputted pargrid augmented with the computed standard
errors.
bfEstimate The estimates of the Bayes factors
bfSigma The covariance matrix for the Bayes factors
estimates.
Uses the batch means method to compute the standard errors for Bayes factors.
Roy, V., Tan, A. and Flegal, J. (2015). Estimating standard errors for importance sampling estimators with multiple Markov chains. Technical report, Iowa State University. http://lib.dr.iastate.edu/stat_las_preprints/34