colored_noise

Generates temporally autocorrelated random numbers with a mean,
standard deviation, and autocorrelation you specify.

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

colored_noise function

<dl><dt>timesteps</dt>
<dd>The number of temporally autocorrelated random numbers (one
per timestep) you want.</dd>
<dt>mean</dt>
<dd>The mean of the temporally autocorrelated random numbers.</dd>
<dt>sd</dt>
<dd>The standard deviation of the temporally autocorrelated random
numbers.</dd>
<dt>phi</dt>
<dd>The temporal autocorrelation. 0 is white noise (uncorrelated),
positive values are red noise (directly correlated) and negative values are blue
noise (inversely correlated).</dd></dl>

Arguments

Generate Autocorrelated Noise — colored_noise

<dl>

<dt>timesteps</dt>
<dd>The number of temporally autocorrelated random numbers (one
per timestep) you want.</dd>


<dt>mean</dt>
<dd>The mean of the temporally autocorrelated random numbers.</dd>


<dt>sd</dt>
<dd>The standard deviation of the temporally autocorrelated random
numbers.</dd>


<dt>phi</dt>
<dd>The temporal autocorrelation. 0 is white noise (uncorrelated),
positive values are red noise (directly correlated) and negative values are blue
noise (inversely correlated).</dd>

</dl>

colored_noise: Generate Autocorrelated Noise

Description

Usage

Value

Arguments

Examples