bcpmeta.model: Identify the Optimal Changepoint Configuration
In bcpmeta: Bayesian Multiple Changepoint Detection Using Metadata

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

View source: R/bcpmeta.model.R

Implement a MCMC algorithm to quick search for the optimal changepoint configuration that has the largest posterior probability.

bcpmeta.model(X, meta, iter = 10000, thin = 10, trend = TRUE, 
              EB = TRUE, mu0 = NULL, nu0 = 5, a1 = 1, a2 = 1, 
              b1 = 19, b2 = 3, phi.lower = -0.99, phi.upper = 0.99, 
              start.eta = NULL, track.time = TRUE, show.summary = TRUE, 
              start.year = 1, meta.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.
`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`.
`a1`	the first parameter in the Beta-Binomial prior of non-metadata times.
`a2`	the first parameter in the Beta-Binomial prior of metadata times.
`b1`	the second parameter in the Beta-Binomial prior of non-metadata times.
`b2`	the second parameter in the Beta-Binomial prior of metadata times.
`phi.lower`	lower bound of the range of `phi`
`phi.upper`	upper bound of the range of `phi`
`start.eta`	initial value of the changepoint configuration `eta` for the MCMC. If `NULL`, generated randomly.
`track.time`	logical indicating whether to show process time.
`show.summary`	logical indicating whether to show the top 5 configurations.
`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).

A Metropolis-Hastings algorithm with interwine of two transitions, a component-wise updating and a simple random swapping. See references for details.

`Eta`	a `(iter/thin+1)` * `length(X)` matrix. Each row is a changepoint configuration visited by MCMC, in the format of a vector 0-1 indicators.
`map200`	a `200` * `(length(X) + 3)` matrix. The best 200 changepoint configurations, listed in desecending order. Each row is a vector of 0-1 indicators, followed by `lml` (log likelihood up to a constant), `phi.eb` (if `EB == TRUE`, the empirical Bayes estimate of `phi` under that configuration) and `lpost` (log posterior up to a constant).
`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.

Yingbo Li

Maintainer: Yingbo Li <ybli@clemson.edu>

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

Function marginal.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

## For illustration purpose, number of MCMC iteration is set to a small value.
results = bcpmeta.model(X, meta = meta, iter = 1e3, trend = FALSE);