Brian Dennis, José Miguel Ponciano, and Mark L. Taper. 2010. Replicated sampling increases efficiency in monitoring biological populations. Ecology 91:610–620.


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R program to calculate maximum-likelihood or restricted maximum-likelihood estimates of model parameters, as well as to calculate and plot profile log-likelihoods, for the Gompertz state-space model with replicated sampling.
Ecological Archives E091-044-S1.

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Author(s)

Brian Dennis
Department of Fish and Wildlfe Resources and Department of Statistics
University of Idaho
Moscow, Idaho 83844-1136 USA
E-mail: brian@uidaho.edu

José Miguel Ponciano
Centro de Investigación en Matemáticas, CIMAT A. C. Calle Jalisco s/n
Col. Valenciana, A.P. 402
C.P. 36240 Guanajuato, Guanajuato, México

Mark L. Taper
Department of Ecology
Montana State University
301 Lewis Hall
Bozeman, Montana 59717-3460


File list

Dennis_etal_Gompertz_state_space_model_with_ replicated_sampling.R

Description

The computer program, in the open-source R language (R Core Development Team. 2006. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria), uses numerical maximization of a multivariate-normal log-likelihood to compute maximum-likelihood and restricted maximum-likelihood parameter estimates for the Gompertz state-space model of density dependent population growth, for time series abundance data with replicated samples at each observation time. The program also computes and plots profile log-likelihoods for the four model parameters. The data included in the program as an example are the simulated observations appearing in the top panel of Fig. 1 of the main article, and the program was used to produce Fig. 2 in the same paper.

Dennis_etal_Gompertz_state_space_model_with_replicated_sampling.R contains annotated code for fitting a stochastic, density-dependent population growth model to time series abundance data in which samples have been replicated at each sampling time.


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