R Code and Output Supporting "Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation"

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R Code and Output Supporting "Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation"

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2019-07-22

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Fieberg, John R
Jfieberg@umn.edu

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Abstract

This repository contains data and R code (along with associated output from running the code) for fitting resource-selection functions and step-selection functions with random effects, supporting all results reported in: Muff, S., Signer, J. and Fieberg, J., 2018. Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. bioRxiv, p.411801.

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See readme.txt for a description of the files in this repository.

Referenced by

Muff, S., Signer, J. and Fieberg, J., 2018. Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. bioRxiv, p.411801.
LaPoint, S., Gallery, P., Wikelski, M. & Kays, R. (2013). Animal behavior, cost-based corridor models, and real corridors. Landscape Ecology, 28, 1615-1630
Lele, S.R., Keim, J L., and P. Solymos (2017). ResourceSelection: Resource Selection (Probability) Functions for Use-Availability Data. R package version 0.3-2. https://CRAN.R-project.org/package=ResourceSelection
Weinberger, I. C., S. Muff, A. Kranz, and F. Bontadina (2016). Flexible habitat selection paves the way for a recovery of otter populations in the European Alps. Biological Conservation 199, 88-95.

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The current version replaces version 1:
https://hdl.handle.net/11299/204737.1

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Muff, Stefanie; Signer, Johannes; Fieberg, John R. (2019). R Code and Output Supporting "Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation". Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/8bhv-dz98.

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