autocorrelation

A wrapper for the <code><a href="/link/acf?package=colorednoise&version=1.1.1" data-mini-rdoc="colorednoise::acf">acf</a></code> function from the stats package that
extracts only the temporal autocorrelation at a lag of one timestep (which is
the type of temporal autocorrelation that this package simulates). The function omits
NA values in the time series.

Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one.
This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.

July Pilowsky

colorednoise

Simulate Temporally Autocorrelated Populations

autocorrelation function

<dl><dt>x</dt>
<dd>A numeric vector.</dd>
<dt>biasCorrection</dt>
<dd>Autocorrelation estimates are biased for short time series. The function can
correct for this bias in the manner proposed by Quenouille (1949). Set to TRUE by default.</dd></dl>

Arguments

A wrapper for the <code><a href='https://rdrr.io/r/stats/acf.html'>acf</a></code> function from the stats package that
extracts only the temporal autocorrelation at a lag of one timestep (which is
the type of temporal autocorrelation that this package simulates). The function omits
NA values in the time series.

Estimate the Temporal Autocorrelation of a Numeric Vector — autocorrelation

<dl>

<dt>x</dt>
<dd>A numeric vector.</dd>


<dt>biasCorrection</dt>
<dd>Autocorrelation estimates are biased for short time series. The function can
correct for this bias in the manner proposed by Quenouille (1949). Set to TRUE by default.</dd>

</dl>

autocorrelation: Estimate the Temporal Autocorrelation of a Numeric Vector

Description

Usage

Value

Arguments

Examples