Description Usage Arguments Value Author(s) References Examples
Methodology for analyzing possibly non-stationary time series by adaptively dividing the time series into an unknown but finite number of segments and estimating the corresponding local spectra by smoothing splines.
1 2 3 4 | adaptspec(nloop, nwarmup, nexp_max, x,
tmin, sigmasqalpha, tau_prior_a, tau_prior_b,
tau_up_limit, prob_mm1, step_size_max,
var_inflate, nbasis, nfreq_hat, plotting)
|
nloop |
The total number of MCMC iterations |
nwarmup |
The number of burn-in iterations |
nexp_max |
The maximum number of segments allowed |
x |
The data, a univariate time series, not a time series object |
tmin |
The minimum number of observations per segment. An optional argument defaulted to tmin = 40. |
sigmasqalpha |
An optional argument defaulted to sigmasqalpha = 100. |
tau_prior_a |
An optional argurment defaulted to tau_prior_a = -1. |
tau_prior_b |
An optional argurment defaulted to tau_prior_b = 0. |
tau_up_limit |
An optional argurment defaulted to tau_up_limit = 10000. |
prob_mm1 |
An optional argurment defaulted to prob_mm1 = 0.8. |
step_size_max |
An optional argurment defaulted to step_size_max = 10. |
var_inflate |
An optional argurment defaulted to var_inflate = 1. |
nbasis |
An optional argurment defaulted to nbasis = 7. |
nfreq_hat |
An optional argurment defaulted to nfreq_hat = 50. |
plotting |
An optional argument for displaying output plots defaulted to FALSE. When set to TRUE, this displays the spectral and parition points. |
xi The partition points
log_spec_hat Estimates of the log spectra for all segments
nexp_curr The number of segments in each iteration.
Rosen, O., Wood, S. and Stoffer, D.
Rosen, O., Wood, S. and Stoffer, D. (2012). AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series. J. of the American Statistical Association, 107, 1575-1589
1 2 3 4 5 6 7 | #Running adaptspec with the simulated_piecewise data.
data(simulated_piecewise)
model1 <- adaptspec(nloop = 80, nwarmup = 20,
nexp_max = 5, x = simulated_piecewise[1:100])
str(model1)
summary(model1$nexp_curr)
plot(model1$nexp_curr)
|
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