mcmcsae-TMVN-method: Functions for specifying the method and corresponding options...
In mcmcsae: Markov Chain Monte Carlo Small Area Estimation

mcmcsae-TMVN-method

R Documentation

Functions for specifying the method and corresponding options for sampling from a possibly truncated and degenerate multivariate normal distribution

Description

These functions are intended for use in the method argument of create_TMVN_sampler.

Usage

m_direct()

m_Gibbs(slice = FALSE, diagnostic = FALSE, debug = FALSE)

m_HMC(
  Tsim = pi/2,
  max.events = .Machine$integer.max,
  diagnostic = FALSE,
  debug = FALSE
)

m_HMCZigZag(
  Tsim = 1,
  rate = 1,
  prec.eq = NULL,
  diagnostic = FALSE,
  max.events = .Machine$integer.max,
  adapt = FALSE,
  debug = FALSE
)

m_softTMVN(
  sharpness = 100,
  useV = FALSE,
  CG = NULL,
  PG.approx = TRUE,
  PG.approx.m = -2L,
  debug = FALSE
)

Arguments

`slice`	if `TRUE`, a Gibbs within slice sampler is used.
`diagnostic`	whether information about violations of inequalities, bounces off inequality walls (for 'HMC' and 'HMCZigZag' methods) or gradient events (for 'HMCZigZag') is printed to the screen.
`debug`	if `TRUE` a breakpoint is set at the beginning of the TMVN sampling function. Mainly intended for developers.
`Tsim`	the duration of a Hamiltonian Monte Carlo simulated particle trajectory. This can be specified as either a single positive numeric value for a fixed simulation time, or as a function that is applied in each MCMC iteration to generates a simulation time.
`max.events`	maximum number of events (reflections off inequality walls and for method 'HMCZigZag' also gradient events). Default is unlimited. Specifying a finite number may speed up the sampling but may also result in a biased sampling algorithm.
`rate`	vector of Laplace rate parameters for method 'HMCZigZag'. It must be a positive numeric vector of length one or the number of variables.
`prec.eq`	positive numeric vector of length 1 or the number of equality restrictions, to control the precision with which the equality restrictions are imposed; the larger `prec.eq` the more precisely they will be imposed.
`adapt`	experimental feature: if `TRUE` the rate parameter will be adapted in an attempt to make the sampling algorithm more efficient.
`sharpness`	for method 'softTMVN', the sharpness of the soft inequalities; the larger the better the approximation of exact inequalities. It must a positive numeric vector of length one or the number of inequality restrictions.
`useV`	for method 'softTMVN' whether to base computations on variance instead of precision matrices.
`CG`	use a conjugate gradient iterative algorithm instead of Cholesky updates for sampling the model's coefficients. This must be a list with possible components `max.it`, `stop.criterion`, `verbose`. See the help for function `CG_control`, which can be used to specify these options. Currently the preconditioner and scale options cannot be set for this use case.
`PG.approx`	see `sampler_control`.
`PG.approx.m`	see `sampler_control`.