sdproc_abstract

Function for estimating stochastic variation in linar process x as a function of relative growth rate and disturbance regime standard deviation.

Stochastic

linear

system

Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) <doi:10.1002/ecs2.4328> in Ecosphere.
These can be used to estimate the parameters of a stochastic state space model (i.e. a model where
a time series is observed with error). The goal of this package is to estimate the variability
around a deterministic process, both in terms of observation error - i.e. variability due to
imperfect observations that does not influence system state - and in terms of process noise - i.e.
stochastic variation in the actual state of the process. Unlike classical methods for estimating
variability, this package does not necessarily assume that the deterministic state is fixed (i.e.
a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated
(e.g. stochastic fluctuations during predator-prey dynamics).

Adam Clark

pttstability

Particle-Takens Stability

sdproc_abstract function

<dl><dt>sd_proc</dt>
<dd>standard deviation of the (Gaussian) disturbance process</dd>
<dt>rgr</dt>
<dd>relative growth rate of the linear process</dd>
<dt>waiting_time</dt>
<dd>average waiting time between (random exponentially distributed through time) disturbance events</dd></dl>

Arguments

calculate estimated total variance — sdproc_abstract

<dl>

<dt>sd_proc</dt>
<dd>standard deviation of the (Gaussian) disturbance process</dd>


<dt>rgr</dt>
<dd>relative growth rate of the linear process</dd>


<dt>waiting_time</dt>
<dd>average waiting time between (random exponentially distributed through time) disturbance events</dd>

</dl>

sdproc_abstract: calculate estimated total variance

Description

Usage

Value

Arguments