| TLSWlacf {TrendLSW} | R Documentation |
Compute Localised Autocovariance Estimate of a TLSW Object
Description
Computes the local autocovariance and autocorrelation estimates, given an
input of an object of class TLSW containing the estimated spectrum. Provides the same functionality as the
function lacf from the locits package, but user provides an object of
class TLSW as the main argument.
Usage
TLSWlacf(x.TLSW, lag.max = NULL)
Arguments
x.TLSW |
a |
lag.max |
The maximum lag of acf required. If NULL then the same default as in the regular acf function is used. |
Value
An object of class lacf which contains the following components:
-
lacf: a matrix containing the estimate of the local autocovariance. Columns represent lags (beginning at lag 0), and rows represent time points. -
lacr: a matrix containing the estimate of the local autocorrelation. Columns represent lags (beginning at lag 0), and rows represent time points. -
name: the name of the time series (if applicable). -
date: the date the function was executed. -
SmoothWP: The smoothed, un-corrected raw wavelet periodogram of the input data. -
S: the spectral estimate used to compute the local autocovariance. -
J: the number of total wavelet scales.
References
McGonigle, E. T., Killick, R., and Nunes, M. (2022). Trend locally stationary wavelet processes. Journal of Time Series Analysis, 43(6), 895-917.
Nason, G. P. (2013). A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(5), 879–904.
Nason, G. P. (2016). locits: Tests of stationarity and localized autocovariance. R package version 1.7.3.
See Also
Examples
## ---- computes estimate of local autocovariance function
spec <- matrix(0, nrow = 9, ncol = 512)
spec[2, ] <- 1 + sin(seq(from = 0, to = 2 * pi, length = 512))^2
trend <- seq(from = 0, to = 10, length = 512)
set.seed(123)
x <- TLSWsim(trend = trend, spec = spec)
## ---- first estimate the spectrum:
x.TLSW <- TLSW(x)
#---- estimate the lacf:
lacf.est <- TLSWlacf(x.TLSW)
#---- plot the variance (lag 0 lacf) over time:
plot.ts(lacf.est$lacf[, 1], ylab = "Variance")