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 threedimensional 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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