View source: R/mcmc_samplers.R
btf | R Documentation |
Run the MCMC for Bayesian trend filtering with a penalty on zeroth (D = 0), first (D = 1), or second (D = 2) differences of the conditional expectation. The penalty is determined by the prior on the evolution errors, which include:
the dynamic horseshoe prior ('DHS');
the static horseshoe prior ('HS');
the Bayesian lasso ('BL');
the normal stochastic volatility model ('SV');
the normal-inverse-gamma prior ('NIG').
In each case, the evolution error is a scale mixture of Gaussians. Sampling is accomplished with a (parameter-expanded) Gibbs sampler, mostly relying on a dynamic linear model representation.
btf(
y,
evol_error = "DHS",
D = 2,
useObsSV = FALSE,
nsave = 1000,
nburn = 1000,
nskip = 4,
mcmc_params = list("mu", "yhat", "evol_sigma_t2", "obs_sigma_t2", "dhs_phi",
"dhs_mean"),
computeDIC = TRUE,
verbose = TRUE
)
y |
the |
evol_error |
the evolution error distribution; must be one of 'DHS' (dynamic horseshoe prior), 'HS' (horseshoe prior), 'BL' (Bayesian lasso), or 'NIG' (normal-inverse-gamma prior) |
D |
degree of differencing (D = 0, D = 1, or D = 2) |
useObsSV |
logical; if TRUE, include a (normal) stochastic volatility model for the observation error variance |
nsave |
number of MCMC iterations to record |
nburn |
number of MCMC iterations to discard (burin-in) |
nskip |
number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw |
mcmc_params |
named list of parameters for which we store the MCMC output; must be one or more of:
|
computeDIC |
logical; if TRUE, compute the deviance information criterion |
verbose |
logical; should R report extra information on progress? |
A named list of the nsave
MCMC samples for the parameters named in mcmc_params
The data y
may contain NAs, which will be treated with a simple imputation scheme
via an additional Gibbs sampling step. In general, rescaling y
to have unit standard
deviation is recommended to avoid numerical issues.
# Example 1: Bumps Data
simdata = simUnivariate(signalName = "bumps", T = 128, RSNR = 7, include_plot = TRUE)
y = simdata$y
out = btf(y)
plot_fitted(y, mu = colMeans(out$mu), postY = out$yhat, y_true = simdata$y_true)
## Not run:
# Example 2: Doppler Data; longer series, more noise
simdata = simUnivariate(signalName = "doppler", T = 500, RSNR = 5, include_plot = TRUE)
y = simdata$y
out = btf(y)
plot_fitted(y, mu = colMeans(out$mu), postY = out$yhat, y_true = simdata$y_true)
# And examine the AR(1) parameters for the log-volatility w/ traceplots:
plot(as.ts(out$dhs_phi)) # AR(1) coefficient
plot(as.ts(out$dhs_mean)) # Unconditional mean
# Example 3: Blocks data (locally constant)
simdata = simUnivariate(signalName = "blocks", T = 1000, RSNR = 3, include_plot = TRUE)
y = simdata$y
out = btf(y, D = 1) # try D = 1 to approximate the locally constant behavior
plot_fitted(y, mu = colMeans(out$mu), postY = out$yhat, y_true = simdata$y_true)
## End(Not run)
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