Permutation tests for hypothesis testing with animal social network data : Problems and potential solutions

Lade...
Vorschaubild
Dateien
Farine_2-zmrtuooimgzq8.pdf
Farine_2-zmrtuooimgzq8.pdfGröße: 955.16 KBDownloads: 275
Datum
2022
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Methods in Ecology and Evolution. Wiley. 2022, 13(1), pp. 144-156. ISSN 2041-2096. eISSN 2041-210X. Available under: doi: 10.1111/2041-210X.13741
Zusammenfassung
  1. Permutation tests are widely used to test null hypotheses with animal social network data, but suffer from high rates of type I and II error when the permutations do not properly simulate the intended null hypothesis.

    2. Two common types of permutations each have limitations. Pre-network (or datastream) permutations can be used to control ‘nuisance effects’ like spatial, temporal or sampling biases, but only when the null hypothesis assumes random social structure. Node (or node-label) permutation tests can test null hypotheses that include nonrandom social structure, but only when nuisance effects do not shape the observed network.

    3. We demonstrate one possible solution addressing these limitations: using pre-network permutations to adjust the values for each node or edge before conducting a node permutation test. We conduct a range of simulations to estimate error rates caused by confounding effects of social or non-social structure in the raw data.

    4. Regressions on simulated datasets suggest that this ‘double permutation’ approach is less likely to produce elevated error rates relative to using only node permutations, pre-network permutations or node permutations with simple covariates, which all exhibit elevated type I errors under at least one set of simulated conditions. For example, in scenarios where type I error rates from pre-network permutation tests exceed 30%, the error rates from double permutation remain at 5%.

    5. The double permutation procedure provides one potential solution to issues arising from elevated type I and type II error rates when testing null hypotheses with social network data. We also discuss alternative approaches that can provide robust inference, including fitting mixed effects models, restricted node permutations, testing multiple null hypotheses and splitting large datasets to generate replicated networks. Finally, we highlight ways that uncertainty can be explicitly considered and carried through the analysis.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
570 Biowissenschaften, Biologie
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690FARINE, Damien R., Gerald G. CARTER, 2022. Permutation tests for hypothesis testing with animal social network data : Problems and potential solutions. In: Methods in Ecology and Evolution. Wiley. 2022, 13(1), pp. 144-156. ISSN 2041-2096. eISSN 2041-210X. Available under: doi: 10.1111/2041-210X.13741
BibTex
@article{Farine2022-01Permu-55507,
  year={2022},
  doi={10.1111/2041-210X.13741},
  title={Permutation tests for hypothesis testing with animal social network data : Problems and potential solutions},
  number={1},
  volume={13},
  issn={2041-2096},
  journal={Methods in Ecology and Evolution},
  pages={144--156},
  author={Farine, Damien R. and Carter, Gerald G.}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/55507">
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dc:creator>Farine, Damien R.</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:language>eng</dc:language>
    <dcterms:title>Permutation tests for hypothesis testing with animal social network data : Problems and potential solutions</dcterms:title>
    <dcterms:abstract xml:lang="eng">1. Permutation tests are widely used to test null hypotheses with animal social network data, but suffer from high rates of type I and II error when the permutations do not properly simulate the intended null hypothesis.&lt;br /&gt;&lt;br /&gt;2. Two common types of permutations each have limitations. Pre-network (or datastream) permutations can be used to control ‘nuisance effects’ like spatial, temporal or sampling biases, but only when the null hypothesis assumes random social structure. Node (or node-label) permutation tests can test null hypotheses that include nonrandom social structure, but only when nuisance effects do not shape the observed network.&lt;br /&gt;&lt;br /&gt;3. We demonstrate one possible solution addressing these limitations: using pre-network permutations to adjust the values for each node or edge before conducting a node permutation test. We conduct a range of simulations to estimate error rates caused by confounding effects of social or non-social structure in the raw data.&lt;br /&gt;&lt;br /&gt;4. Regressions on simulated datasets suggest that this ‘double permutation’ approach is less likely to produce elevated error rates relative to using only node permutations, pre-network permutations or node permutations with simple covariates, which all exhibit elevated type I errors under at least one set of simulated conditions. For example, in scenarios where type I error rates from pre-network permutation tests exceed 30%, the error rates from double permutation remain at 5%.&lt;br /&gt;&lt;br /&gt;5. The double permutation procedure provides one potential solution to issues arising from elevated type I and type II error rates when testing null hypotheses with social network data. We also discuss alternative approaches that can provide robust inference, including fitting mixed effects models, restricted node permutations, testing multiple null hypotheses and splitting large datasets to generate replicated networks. Finally, we highlight ways that uncertainty can be explicitly considered and carried through the analysis.</dcterms:abstract>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-11-11T10:21:43Z</dc:date>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dc:contributor>Farine, Damien R.</dc:contributor>
    <dc:contributor>Carter, Gerald G.</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/55507"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55507/1/Farine_2-zmrtuooimgzq8.pdf"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55507/1/Farine_2-zmrtuooimgzq8.pdf"/>
    <dc:creator>Carter, Gerald G.</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-11-11T10:21:43Z</dcterms:available>
    <dcterms:issued>2022-01</dcterms:issued>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Begutachtet
Ja
Diese Publikation teilen