TMVN-methods {mcmcsae} | 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 |
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 |
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
|
adapt |
experimental feature: if |
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 |
PG.approx |
see |
PG.approx.m |
see |
Value
A method object, for internal use only.