ApxCI {mvLSW} | R Documentation |
Evaluate the Approximate Confidence Interval of a mvEWS Estimate
Description
Evaluate the approximate confidence interval of a multivariate evolutionary wavelet spectrum.
Usage
ApxCI(object, var = NULL, alpha = 0.05, ...)
Arguments
object |
A |
var |
A |
alpha |
Type I error, a single numerical value within (0,0.5]. |
... |
Additional arguments to be passed to the
|
Details
The command evaluates the approximate Gaussian confidence intervals for the elements of the mvEWS estimate.
Value
Invisibly returns a list containing two mvLSW
classed
objects with names "L" and "U" that respectively identify the
lower and upper interval estimates.
References
Taylor, S.A.C., Park, T.A. and Eckley, I. (2019) Multivariate locally stationary wavelet analysis with the mvLSW R package. Journal of statistical software 90(11) pp. 1–16, doi: 10.18637/jss.v090.i11.
Park, T. (2014) Wavelet Methods for Multivariate Nonstationary Time Series, PhD thesis, Lancaster University, pp. 91-111.
See Also
Examples
## Define evolutionary wavelet spectrum, structure only on level 2
Spec <- array(0, dim = c(3, 3, 8, 256))
Spec[1, 1, 2, ] <- 10
Spec[2, 2, 2, ] <- c(rep(5, 64), rep(0.6, 64), rep(5, 128))
Spec[3, 3, 2, ] <- c(rep(2, 128), rep(8, 128))
Spec[2, 1, 2, ] <- Spec[1, 2, 2, ] <- punif(1:256, 65, 192)
Spec[3, 1, 2, ] <- Spec[1, 3, 2, ] <- c(rep(-1, 128), rep(5, 128))
Spec[3, 2, 2, ] <- Spec[2, 3, 2, ] <- -0.5
EWS <- as.mvLSW(x = Spec, filter.number = 1, family = "DaubExPhase",
min.eig.val = NA)
## Sample time series and estimate the EWS.
set.seed(10)
X <- rmvLSW(Spectrum = EWS)
EWS_X <- mvEWS(X, kernel.name = "daniell", kernel.param = 20)
## Evaluate asymptotic spectral variance
SpecVar <- varEWS(EWS_X)
## Plot Estimate & 95% confidence interval
CI <- ApxCI(object = EWS_X, var = SpecVar, alpha = 0.05)
plot(x = EWS_X, style = 2, info = 2, Interval = CI)