nse.spec0: The spectral density at zero.
In AmurG/nse: Numerical Standard Errors Computation in R

Description Usage Arguments Details Value References Examples

Calculate the variance of the mean with the spectrum at zero estimator.

1 2	nse.spec0(x, method = c("AR", "bartlett", "wosa", "tukey"), prewhite = FALSE, max.length = 200)

`x`	A numeric vector.
`method`	A character string denoting the method to use in estimating the spectral density function
`prewhite`	Prewhite the serie before analysis
`max.length`	maximum sample size for aggregation

This is a wrapper around spectrum0.ar from the CODA package, SDF from the sapa package, psdcore from the PSD package and mcsefrom the mcmcse package.

The variance estimator.

Plummer, Martyn, et al. "CODA: Convergence diagnosis and output analysis for MCMC." R news 6.1 (2006): 7-11.

D.B. Percival and A. Walden "Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques". Cambridge University Press (1993).

Barbour, A. J. and R. L. Parker (2014), "psd: Adaptive, sine multitaper power spectral density estimation for R", Computers & Geosciences Volume 63 February 2014 : 1-8

James M. Flegal, John Hughes and Dootika Vats. (17-08-2015). mcmcse: Monte Carlo Standard Errors for MCMC. R package version 1.1-2. Riverside, CA and Minneapolis, MN.

n = 1000
ar = c(0.9)
mean = c(1)
sd = c(10)
 
Ts1 = as.vector(arima.sim(n = n, list(ar = ar), sd = sd) + mean)
 
nse::nse.spec0(x = Ts1, method = "AR", prewhite = FALSE)