McmcParams-class: An object to specify MCMC options for a later simulation

In CNPBayes: Bayesian mixture models for copy number polymorphisms

Description

An object to specify MCMC options for a later simulation

Slots

thin

A one length numeric to specify thinning. A value of n indicates that every nth sample should be saved. Thinning helps to reduce autocorrelation.

iter

A one length numeric to specify how many MCMC iterations should be sampled.

burnin

A one length numeric to specify burnin. The first $n$ samples will be discarded.

nstarts

A one length numeric to specify the number of chains in a simulation.

param_updates

Indicates whether each parameter should be updated (1) or fixed (0).

min_GR

minimum value of multivariate Gelman Rubin statistic for diagnosing convergence. Default is 1.2.

min_effsize

the minimum mean effective size of the chains. Default is 1/3 * iter.

max_burnin

The maximum number of burnin iterations before we give up and return the existing model.

min_chains

minimum number of independence MCMC chains used for assessing convergence. Default is 3.

Examples

McmcParams()
McmcParams(iter=1000)
mp <- McmcParams()
iter(mp)

CNPBayes documentation built on May 6, 2019, 4:06 a.m.