# getSdNaive-LagEstimator: Get estimates for the standard deviation of the lagEstimator... In quantspec: Quantile-Based Spectral Analysis of Time Series

## Description

Determines and returns an array of dimension `[J,K1,K2]`, where `J=length(frequencies)`, `K1=length(levels.1)`, and `K2=length(levels.2))`. At position `(j,k1,k2)` the returned value is the standard deviation estimated corresponding to `frequencies[j]`, `levels.1[k1]` and `levels.2[k2]` that are closest to the `frequencies`, `levels.1` and `levels.2` available in `object`; `closest.pos` is used to determine what closest to means.

## Usage

 ```1 2 3 4``` ```## S4 method for signature 'LagEstimator' getSdNaive(object, frequencies = 2 * pi * (0:(length(object@Y) - 1))/length(object@Y), levels.1 = getLevels(object, 1), levels.2 = getLevels(object, 2)) ```

## Arguments

 `object` `LagEstimator` of which to get the estimates for the standard deviation. `frequencies` a vector of frequencies for which to get the result `levels.1` the first vector of levels for which to get the result `levels.2` the second vector of levels for which to get the result

## Details

Requires that the `LagEstimator` is available at all Fourier frequencies from (0,pi]. If this is not the case the missing values are imputed by taking one that is available and has a frequency that is closest to the missing Fourier frequency; `closest.pos` is used to determine which one this is.

Note the “standard deviation” estimated here is not the square root of the complex-valued variance. It's real part is the square root of the variance of the real part of the estimator and the imaginary part is the square root of the imaginary part of the variance of the estimator.

## Value

Returns the estimate described above.

quantspec documentation built on May 29, 2017, 3:34 p.m.