BayesTwin

An R package for Bayesian inference of item-level twin data

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Abstract

BayesTwin is an open-source R package that serves as a pipeline to the MCMC program JAGS to perform Bayesian inference on genetically-informative hierarchical twin data. Simultaneously to the biometric model, an item response theory (IRT) measurement model is estimated, allowing analysis of the raw phenotypic (item-level) data. The integration of such a measurement model is important since earlier research has shown that an analysis based on an aggregated measure (e.g., a sum-score based analysis) can lead to an underestimation of heritability and the spurious finding of genotype-environment interactions. The package includes all common biometric and IRT models as well as functions that help plot relevant information or determine whether the analysis was performed well.
Original languageEnglish
Article number33
JournalJournal of Open Research Software
Volume5
DOIs
Publication statusPublished - 1 Jan 2017

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Biometrics
Measurement theory
Pipelines

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title = "BayesTwin: An R package for Bayesian inference of item-level twin data",
abstract = "BayesTwin is an open-source R package that serves as a pipeline to the MCMC program JAGS to perform Bayesian inference on genetically-informative hierarchical twin data. Simultaneously to the biometric model, an item response theory (IRT) measurement model is estimated, allowing analysis of the raw phenotypic (item-level) data. The integration of such a measurement model is important since earlier research has shown that an analysis based on an aggregated measure (e.g., a sum-score based analysis) can lead to an underestimation of heritability and the spurious finding of genotype-environment interactions. The package includes all common biometric and IRT models as well as functions that help plot relevant information or determine whether the analysis was performed well.",
author = "I. Schwabe",
year = "2017",
month = "1",
day = "1",
doi = "10.5334/jors.185",
language = "English",
volume = "5",
journal = "Journal of Open Research Software",
issn = "2049-9647",
publisher = "Ubiquity Press Ltd.",

}

BayesTwin : An R package for Bayesian inference of item-level twin data. / Schwabe, I.

In: Journal of Open Research Software, Vol. 5, 33, 01.01.2017.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - BayesTwin

T2 - An R package for Bayesian inference of item-level twin data

AU - Schwabe, I.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - BayesTwin is an open-source R package that serves as a pipeline to the MCMC program JAGS to perform Bayesian inference on genetically-informative hierarchical twin data. Simultaneously to the biometric model, an item response theory (IRT) measurement model is estimated, allowing analysis of the raw phenotypic (item-level) data. The integration of such a measurement model is important since earlier research has shown that an analysis based on an aggregated measure (e.g., a sum-score based analysis) can lead to an underestimation of heritability and the spurious finding of genotype-environment interactions. The package includes all common biometric and IRT models as well as functions that help plot relevant information or determine whether the analysis was performed well.

AB - BayesTwin is an open-source R package that serves as a pipeline to the MCMC program JAGS to perform Bayesian inference on genetically-informative hierarchical twin data. Simultaneously to the biometric model, an item response theory (IRT) measurement model is estimated, allowing analysis of the raw phenotypic (item-level) data. The integration of such a measurement model is important since earlier research has shown that an analysis based on an aggregated measure (e.g., a sum-score based analysis) can lead to an underestimation of heritability and the spurious finding of genotype-environment interactions. The package includes all common biometric and IRT models as well as functions that help plot relevant information or determine whether the analysis was performed well.

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DO - 10.5334/jors.185

M3 - Article

VL - 5

JO - Journal of Open Research Software

JF - Journal of Open Research Software

SN - 2049-9647

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