Bayesian multilevel models for repeated measures data: a conceptual and practical introduction in R
This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models...
Gespeichert in:
Hauptverfasser: | , |
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Format: | Buch |
Sprache: | English |
Veröffentlicht: |
London ; New York
Routledge, Taylor & Francis Group
2023
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Schlagworte: | |
Zusammenfassung: | This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many ‘random effects’. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to ‘translate’ their skills with more traditional models to a Bayesian framework will benefit greatly from the lessons in this text |
Beschreibung: | xxii, 460 Seiten Diagramme |
ISBN: | 9781032259635 9781032259628 |
Internformat
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Datensatz im Suchindex
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author | Barreda, Santiago Silbert, Noah |
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discipline | Psychologie |
discipline_str_mv | Psychologie |
format | Book |
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id | DE-604.BV048987866 |
illustrated | Not Illustrated |
index_date | 2024-07-03T22:06:36Z |
indexdate | 2024-07-10T09:52:06Z |
institution | BVB |
isbn | 9781032259635 9781032259628 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034251252 |
oclc_num | 1378561327 |
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owner_facet | DE-29 DE-19 DE-BY-UBM |
physical | xxii, 460 Seiten Diagramme |
publishDate | 2023 |
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publisher | Routledge, Taylor & Francis Group |
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spelling | Barreda, Santiago Verfasser aut Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R Santiago Barreda and Noah Silbert London ; New York Routledge, Taylor & Francis Group 2023 xxii, 460 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many ‘random effects’. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to ‘translate’ their skills with more traditional models to a Bayesian framework will benefit greatly from the lessons in this text Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 s R Programm (DE-588)4705956-4 s Bayes-Entscheidungstheorie (DE-588)4144220-9 s DE-604 Silbert, Noah Verfasser aut Erscheint auch als Online-Ausgabe 978-1-003-28587-8 |
spellingShingle | Barreda, Santiago Silbert, Noah Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Multivariate Analyse (DE-588)4040708-1 gnd R Programm (DE-588)4705956-4 gnd |
subject_GND | (DE-588)4144220-9 (DE-588)4040708-1 (DE-588)4705956-4 |
title | Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R |
title_auth | Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R |
title_exact_search | Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R |
title_exact_search_txtP | Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R |
title_full | Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R Santiago Barreda and Noah Silbert |
title_fullStr | Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R Santiago Barreda and Noah Silbert |
title_full_unstemmed | Bayesian multilevel models for repeated measures data a conceptual and practical introduction in R Santiago Barreda and Noah Silbert |
title_short | Bayesian multilevel models for repeated measures data |
title_sort | bayesian multilevel models for repeated measures data a conceptual and practical introduction in r |
title_sub | a conceptual and practical introduction in R |
topic | Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Multivariate Analyse (DE-588)4040708-1 gnd R Programm (DE-588)4705956-4 gnd |
topic_facet | Bayes-Entscheidungstheorie Multivariate Analyse R Programm |
work_keys_str_mv | AT barredasantiago bayesianmultilevelmodelsforrepeatedmeasuresdataaconceptualandpracticalintroductioninr AT silbertnoah bayesianmultilevelmodelsforrepeatedmeasuresdataaconceptualandpracticalintroductioninr |