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mcmcsae (version 0.7.4)

set_opts: Set global options relating to computational details

Description

Set global options relating to computational details

Usage

set_opts(
  auto.order.block = TRUE,
  chol.inplace = TRUE,
  chol.ordering = 0L,
  PG.approx = TRUE,
  PG.approx.m = -2L,
  CRT.approx.m = 20L,
  max.size.cps.template = 100L
)

Value

This function sets or resets options in the option environment .opts.

Arguments

auto.order.block

whether Gibbs blocks should be ordered automatically in such a way that those with the most sparse design matrices come first. This way of ordering can make Cholesky updates more efficient.

chol.inplace

whether sparse Cholesky updates should re-use the same memory location.

chol.ordering

an integer passed to CHOLMOD routines determining which reordering schemes are tried to limit sparse Cholesky fill-in.

PG.approx

whether Polya-Gamma draws for logistic binomial models are approximated by a hybrid gamma convolution approach. If not, BayesLogit::rpg is used, which is exact for some values of the shape parameter.

PG.approx.m

if PG.approx=TRUE, the number of explicit gamma draws in the sum-of-gammas representation of the Polya-Gamma distribution. The remainder (infinite) convolution is approximated by a single moment-matching gamma draw. Special values are: -2L for a default choice depending on the value of the shape parameter balancing performance and accuracy, -1L for a moment-matching normal approximation, and 0L for a moment-matching gamma approximation.

CRT.approx.m

scalar integer specifying the degree of approximation to sampling from a Chinese Restaurant Table distribution. The approximation is based on Le Cam's theorem. Larger values yield a slower but more accurate sampler.

max.size.cps.template

maximum allowed size in MB of the sparse matrix serving as a template for the sparse symmetric crossproduct X'QX of a dgCMatrix X, where Q is a diagonal matrix subject to change.

References

D. Bates, M. Maechler, B. Bolker and S.C. Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67(1), 1-48.

Y. Chen, T.A. Davis, W.W. Hager and S. Rajamanickam (2008). Algorithm 887: CHOLMOD, supernodal sparse Cholesky factorization and update/downdate. ACM Transactions on Mathematical Software 35(3), 1-14.