Supplement 1. R code for simulation implementation and parameter estimation.
Kevin E. See
Elizabeth E. Holmes
10.6084/m9.figshare.3520919.v1
https://wiley.figshare.com/articles/Supplement_1_R_code_for_simulation_implementation_and_parameter_estimation_/3520919
<h2>File List</h2><div>
<p><a href="EstimationComparison.R">EstimationComparison.R</a> (MD5: 5055e25ebc61fe8dbf012b48198eadda)</p>
<p><a href="Simulations.R">Simulations.R</a> (MD5: 56eef6b34cb59a85d5072b0b20e645ec)</p>
<p><a href="CreateFigures.R">CreateFigures.R</a> (MD5: 2c19a14d1ac5887ba544c2937d8639ca)</p>
</div><h2>Description</h2><div>
<p>EstimationComparison.R - This code was used to simulate time-series of length 30 time-step and either 1, 2, or 3 replicated observations at each time-step. Parameters were then estimated using three different methodologies: restricted maximum likelihood (REML) based on first differences, REML based on second differences and maximum likelihood using the Kalman filter, as implemented in the MARSS R package.</p>
<p>Simulations.R - This code was used to simulate time-series of varying length, with varying numbers of replicated observations, under two different rates of decline and several different process and non-process error variances. Estimates of trend, process and non-process error variances were then made with the MARSS R package.</p>
<p>CreateFigures.R - This code was used to create the figures that appear in the manuscript and appendices.</p>
</div>
2016-08-04 21:44:47
sampling design
PVA
repeated measures
population viability analysis
state-space model
measurement error
process error
monitoring design
quasi-extinction risk