mixed.frequency | R Documentation |
Fit a structured time series to mixed frequncy data.
bsts.mixed(target.series, predictors, which.coarse.interval, membership.fraction, contains.end, state.specification, regression.prior, niter, ping = niter / 10, seed = NULL, truth = NULL, ...)
target.series |
A vector object of class |
predictors |
A matrix of class |
which.coarse.interval |
A numeric vector of length
|
membership.fraction |
A numeric vector of length
|
contains.end |
A logical vector of length |
state.specification |
A state specification like that required
by |
regression.prior |
A prior distribution created by
|
niter |
The desired number of MCMC iterations. |
ping |
An integer indicating the frequency with which progress
reports get printed. E.g. setting |
seed |
An integer to use as the random seed for the underlying
C++ code. If |
truth |
For debugging purposes only. A list containing one or more of the following elements. If any are present then corresponding values will be held fixed in the MCMC algorithm.
|
... |
Extra arguments passed to SpikeSlabPrior |
An object of class bsts.mixed
, which is a list with the
following elements. Many of these are arrays, in which case the first
index of the array corresponds to the MCMC iteration number.
coefficients |
A matrix containing the MCMC draws of the regression coefficients. Rows correspond to MCMC draws, and columns correspond to variables. |
sigma.obs |
The standard deviation of the weekly latent observations. |
state.contributions |
A three-dimensional array containing the MCMC draws of each state model's contributions to the state of the weekly model. The three dimensions are MCMC iteration, state model, and week number. |
weekly |
A matrix of MCMC draws of the weekly latent observations. Rows are MCMC iterations, and columns are weekly time points. |
cumulator |
A matrix of MCMC draws of the cumulator variable. |
The returned object also contains MCMC draws for the parameters of the
state models supplied as part of state.specification
, relevant
information passed to the function call, and other supplemental
information.
Steven L. Scott steve.the.bayesian@gmail.com
Harvey (1990), "Forecasting, structural time series, and the Kalman filter", Cambridge University Press.
Durbin and Koopman (2001), "Time series analysis by state space methods", Oxford University Press.
bsts
,
AddLocalLevel
,
AddLocalLinearTrend
,
AddSemilocalLinearTrend
,
SpikeSlabPrior
,
SdPrior
.
## Not run: data <- SimulateFakeMixedFrequencyData(nweeks = 104, xdim = 20) ## Setting an upper limit on the standard deviations can help keep the ## MCMC from flying off to infinity. sd.limit <- sd(data$coarse.target) state.specification <- AddLocalLinearTrend(list(), data$coarse.target, level.sigma.prior = SdPrior(1.0, 5, upper.limit = sd.limit), slope.sigma.prior = SdPrior(.5, 5, upper.limit = sd.limit)) weeks <- index(data$predictor) months <- index(data$coarse.target) which.month <- MatchWeekToMonth(weeks, months[1]) membership.fraction <- GetFractionOfDaysInInitialMonth(weeks) contains.end <- WeekEndsMonth(weeks) model <- bsts.mixed(target.series = data$coarse.target, predictors = data$predictors, membership.fraction = membership.fraction, contains.end = contains.end, which.coarse = which.month, state.specification = state.specification, niter = 500, expected.r2 = .999, prior.df = 1) plot(model, "state") plot(model, "components") ## End(Not run)
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