find_regimes: Fit multiple models with differing numbers of regimes to...

In bayesdfa: Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

Fit multiple models with differing numbers of regimes to trend data

Description

Fit multiple models with differing numbers of regimes to trend data

Usage

find_regimes(
  y,
  sds = NULL,
  min_regimes = 1,
  max_regimes = 3,
  iter = 2000,
  thin = 1,
  chains = 1,
  ...
)

Arguments

y	Data, time series or trend from fitted DFA model.
sds	Optional time series of standard deviations of estimates. If passed in, residual variance not estimated.
min_regimes	Smallest of regimes to evaluate, defaults to 1.
max_regimes	Biggest of regimes to evaluate, defaults to 3.
iter	MCMC iterations, defaults to 2000.
thin	MCMC thinning rate, defaults to 1.
chains	MCMC chains; defaults to 1 (note that running multiple chains may result in a "label switching" problem where the regimes are identified with different IDs across chains).
...	Other parameters to pass to rstan::sampling().

Examples

data(Nile)
find_regimes(log(Nile), iter = 50, chains = 1, max_regimes = 2)

bayesdfa documentation built on Oct. 11, 2023, 5:14 p.m.