# avar_to_cpp: Compute Tau-Overlap Allan Variance In gmwm: Generalized Method of Wavelet Moments

## Description

Computation of Tau-Overlap Allan Variance

## Usage

 1 avar_to_cpp(x) 

## Arguments

 x A vector with dimensions N x 1.

## Details

Given N equally spaced samples with averaging time tau = n*tau_0, where n is an integer such that 1<= n <= N/2. Therefore, n is able to be selected from {n|n< floor(log2(N))} Then, a sampling of m = ≤ft\lfloor {\frac{{N - 1}}{n}} \right\rfloor - 1 samples exist. The tau-overlap estimator is given by:

where {{\bar y}_t}≤ft( τ \right) = \frac{1}{τ }∑\limits_{i = 0}^{τ - 1} {{{\bar y}_{t - i}}} .

## Value

av A matrix that contains:

• Col 1The size of the cluster

• Col 2The Allan variance

• Col 3The error associated with the variance estimation.

JJB

## References

Long-Memory Processes, the Allan Variance and Wavelets, D. B. Percival and P. Guttorp

## Examples

 1 2 3 4 5 6 7 8 9 set.seed(999) # Simulate white noise (P 1) with sigma^2 = 4 N = 100000 white.noise = rnorm(N, 0, 2) #plot(white.noise,ylab="Simulated white noise process",xlab="Time",type="o") #Simulate random walk (P 4) random.walk = cumsum(0.1*rnorm(N, 0, 2)) combined.ts = white.noise+random.walk av_mat = avar_to_cpp(combined.ts) 

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