R Code and Output Supporting: Resampling-Based Methods for Biologists

Fieberg, John R
Vitense, Kelsey
Johnson, Douglas H
2020-03-02
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https://doi.org/10.13020/wn56-9y75
 https://hdl.handle.net/11299/211873
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R Code and Output Supporting: Resampling-Based Methods for Biologists

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2020-03-02

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Fieberg, John R
Jfieberg@umn.edu

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Observational Data
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Abstract

This repository contains data, R code, and associated output from running R code supporting results reported in: Fieberg, J., K. Vitense, and D. H. Johnson 2020. Resampling-Based Methods for Biologists. PeerJ [In Revision]

Description

See readme file.

Referenced by

Fieberg, J., K. Vitense, and D. H. Johnson 2020. Resampling-Based Methods for Biologists. PeerJ.
https://doi.org/10.7717/peerj.9089

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Attribution-NonCommercial-ShareAlike 3.0 United States
 http://creativecommons.org/licenses/by-nc-sa/3.0/us/

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Data Repository for U of M (DRUM)

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Fieberg, John R; Vitense, Kelsey; Johnson, Douglas H. (2020). R Code and Output Supporting: Resampling-Based Methods for Biologists. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/wn56-9y75.

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Description
Size
bears.csv
Data from Stapleton et al. (2014), containing counts of polar bears
(898 B)

costeff.csv
Data from Zicus et al. (2006), evaluating relative cost-effectiveness of nesting structures for mallard (Anas platyrhynchos) ducks
(18.12 KB)

longnosedace.csv
Abundance data of longnose dace (Rhinichthys cataractae) and in-stream variables collected from the Maryland Biological Stream Survey
(2.88 KB)

Pikedata.csv
Data collected by the Minnesota Department of Natural Resources (MN DNR), exploring size distribution of northern pike (Esox lucius)
(1.93 KB)

RIKZdat.csv
Marine benthic data collected by the Dutch institute RIKZ from inter-tidal areas along the Dutch coast
(484 B)

CaseStudyI.R
R code demonstrating how: 1) a cluster-level bootstrap can be used for repeated measures data with equal-sized clusters and 2) how to use functions in the boot package to calculate different bootstrap confidence intervals, including the BCa interval
(8.93 KB)

CaseStudyI.html
Output from running CaseStudyI R code
(709.39 KB)

CaseStudyII.R
R code demonstrating how: 1) the bootstrap can be used in applications that involve multiple response measures from the same set of cases; 2) the bootstrap can provide estimates of uncertainty for non-linear functions of model parameters.
(10.7 KB)

CaseStudyII.html
Output from running CaseStudyII R code
(952.08 KB)

CaseStudyIII.R
R code demonstrating how the bootstrap can be used to explore model uncertainty.
(3.28 KB)

CaseStudyIII.html
Output from running CaseStudyIII R code
(631.9 KB)

MultipleLinearRegression.R
R code demonstrating how to conduct a permutation-based test for a partial regression coefficient in a multiple linear regression model.
(3.07 KB)

MultipleLinearRegression.html
Output from running MultipleLinearRegression R code
(640.51 KB)

Readme.txt
Readme file with information about the repository
(8.64 KB)

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