bcpmeta.parameters: Estimate Model Parameters under a Given Configuration
In bcpmeta: Bayesian Multiple Changepoint Detection Using Metadata

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/bcpmeta.parameters.R

Given a changepoint configuration, use Gibbs sampler (or Metropolis-Hastings algorithm within Gibbs) to find posterior mean estimates of model parameters.

bcpmeta.parameters(X, meta, eta, iter = 10000, thin = 10, trend = TRUE, 
                   EB = TRUE, mu0 = NULL, nu0 = 5, phi.lower = -0.99,
                   phi.upper = 0.99, sd.xi = 0.1, start.phi = NULL, 
                   burnin = 0.2, track.time = TRUE, show.summary = TRUE,
                   start.year = 1, meta.year = FALSE, eta.year = FALSE)

`X`	a numerical vector. Observed time series.
`meta`	metadata. Either a vector of 0-1 indicators of the same length as `X`, or a numerical vector of the time indice of the metadata times.
`eta`	the changepoint configuration. Either a vector of 0-1 indicators of the same length as `X`, or a numerical vector of the time indice of the changepoint times.
`iter`	total number of iterations of MCMC.
`thin`	thinning; save one iteration in every `thin` number of iterations.
`trend`	logical indicating whether to allow the linear trend component.
`EB`	logical indicating whether to use the empirical Bayes method for `sigma^2` and `phi`.
`mu0`	prior mean of regime-wise means `mu_j`. If `NULL`, set to the default value `mean(X)`.
`nu0`	constant factor in prior variance of regim-wise means `mu_j`.
`phi.lower`	lower bound of the range of `phi`
`phi.upper`	upper bound of the range of `phi`
`sd.xi`	standard deviation of the jump proposal of `log(phi)` in Metropolis-Hastings updating when the fully Bayes method is used.
`start.phi`	initial value `phi` for the MCMC when the fully Bayes method is used. If `NULL`, generated randomly.
`burnin`	the ratio of burnin length compared with the total length of MCMC. All posterior mean estimates are calculated without burnin periods.
`track.time`	logical indicating whether to show process time.
`show.summary`	logical indicating whether to show the estimates of parameters.
`start.year`	year index of the first time point in the series.
`meta.year`	logical indicating whether `meta` is indexed in year, if it consists of the locations of the metadata times (instead of 0-1 indicators).
`eta.year`	logical indicating whether `eta` is indexed in year, if it consists of the locations of the metadata times (instead of 0-1 indicators).

Conditional on the given changepoint configuration eta, the posterior mean estimates of regime-wise mean mu and trend alphla (if trend == TRUE) is obtained via Gibbs sampler. If EB == TRUE, empirical Bayes estimates of sigma2 and phi are given; otherwise, fully Bayes estimates of them are obtained via Gibbs sampler and Metropolis-Hastings algorithm, under Jeffreys prior and uniform prior respectively.

`Phi`	the empirical Bayes estimate of `phi` if `EB == TRUE`; or a vector of length `(iter/thin)`, the MCMC samples of `phi` if `EB == FALSE`.
`Sigmasq`	the empirical Bayes estimate of `sigma2` if `EB == TRUE`; or a vector of length `(iter/thin)`, the MCMC samples of `sigma2` if `EB == FALSE`.
`Alpha`	a vector of length `(iter/thin)`, the MCMC samples of `alpha` if `trend == TRUE`; or `0` if `trend == FALSE`.
`Mu`	a `(iter/thin)` * `(sum(eta)+1)` matrix. Each row is a MCMC sample of `mu`.
`phi.est`	the empirical Bayes estimate of `phi` if `EB == TRUE`; or the posterior mean if `EB == FALSE`.
`sigmasq.est`	the empirical Bayes estimate of `sigma2` if `EB == TRUE`; or the posterior mean if `EB == FALSE`.
`alpha.est`	posterior mean estimate of `alpha`
`mu.est`	a vector of length `sum(eta)+1`, posterior mean estimate of `mu`
`X`	observed time series, same as the input value.
`meta`	metadata, same as the input value.
`input.parameters`	input parameters. Use command `names` to check its components.
`change.phi`	ratio of accepting a new phi in the MCMC chain, if `EB == FALSE`.

Yingbo Li

Maintainer: Yingbo Li <ybli@clemson.edu>

Li, Y. and Lund, R. (2014) Bayesian Mulitple Changepoint Detection Using Metadata. (submitted)

Function cp.plot uses the output of this function as input.

## Create a time series of length 200 with three mean shifts at 50, 100, 150.
data = simgen(2, 1);
X = data$X[1, ];  ## time series
meta = data$meta;  ## locations of metadata times

## Parameter estimation in the configuration where changepoints are time 50 and 99.
results = bcpmeta.parameters(X, meta = meta, eta = c(50, 99), trend = FALSE);

100% completed.

Time used (in second): 
   user  system elapsed 
  0.111   0.008   0.119 

Estimates of parameters: 
phi: 0.3815
sigma2: 0.0258
mu: 0.0153 0.2461 0.4837