wvcov: Calculate the Asymptotic Covariance Matrix

In gmwm: Generalized Method of Wavelet Moments

Description

Places the Asymptotic Covariance Matrix in print form.

Usage

wvcov(signal.modwt, signal.wvar, compute.v = "diag")

Arguments

signal.modwt	A modwt object that contains the modwt decomposition.
signal.wvar	A wvar object that contains the wavelet variance.
compute.v	A string that indicates the type of covariance matrix to compute. Supports: "diag"

Value

A list with the structure:

"V"

Covariance Matrix

"V.r"

Covariance Matrix

"nlevels"

Level of decomposition J

"compute.v"

Type of Covariance Matrix

"robust"

Robust active

"eff"

Efficiency level for Robust

"scales"

Tau scales (2^(1:J))

"wv.empir"

Empirical Wavelet Variance

Author(s)

JJB

Examples

set.seed(999)
x = rnorm(100)
decomp = modwt(x)
wv = wvar(x)
out = wvcov(decomp, wv, compute.v="diag")

# Robust
decomp = modwt(x)
wv = wvar(x, robust = TRUE)
out = wvcov(decomp, wv, compute.v="diag")

gmwm documentation built on April 14, 2017, 4:38 p.m.