Heritability of growth strain in Eucalyptus bosistoana: a Bayesian approach with left-censored data§

Davies, Nicholas T.; Apiolaza, Luis A.; Sharma, Monika

doi:10.1186/s40490-017-0086-2

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…In addition to normally distributed data in censored regression models, various types of outcome, including survival data [7], binomial data [41], count data [51] and ranking data [28], can all be modeled by the proposed alternative strategy when censoring occurs. Not only to the medical sciences, the proposed strategy can also be applied to many other fields, such as, in measuring the performance of timing asynchronies using censored normal sensorimotor synchronization data in behavioral science [52], comparing industrial starch grain properties with ordered categorized data in agriculture [53], exploring forest genetics by modeling censored growth strain data for narrow-sense heritability estimation in environmental science [54], determining the importance of influential factors to lower the risk of food contamination for censored microbiological contamination data in food science [55], modeling the interval-censored as well as right-censored time to dental health event in primary school children for public health science [56], and modeling the demand data related to the supply chain management when the distribution of demand could be censored by inventory [57]. In summary, the proposed JAGS model specification can encompass a broad range of popular model structures and be utilized in a wide spectrum of applications.…”

Section: Discussionmentioning

confidence: 99%

On Bayesian modeling of censored data in JAGS

Zhou

Plummer

2022

BMC Bioinformatics

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Background Just Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling. However, the built-in function for censored data misspecifies the default computation of deviance function, which limits likelihood-based Bayesian model comparison. Results To establish an automatic approach to specifying the correct deviance function in JAGS, we propose a simple and generic alternative modeling strategy for the analysis of censored outcomes. The two illustrative examples demonstrate that the alternative strategy not only properly draws posterior samples in JAGS, but also automatically delivers the correct deviance for model assessment. In the survival data application, our proposed method provides the correct value of mean deviance based on the exact likelihood function. In the drug safety data application, the deviance information criterion and penalized expected deviance for seven Bayesian models of censored data are simultaneously computed by our proposed approach and compared to examine the model performance. Conclusions We propose an effective strategy to model censored data in the Bayesian modeling framework in JAGS with the correct deviance specification, which can simplify the calculation of popular Kullback–Leibler based measures for model selection. The proposed approach applies to a broad spectrum of censored data types, such as survival data, and facilitates different censored Bayesian model structures.

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Section: Discussionmentioning

confidence: 99%

On Bayesian modeling of censored data in JAGS

Zhou

Plummer

2022

BMC Bioinformatics

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“…In addition to normally distributed data in censored regression models, various types of outcome, including survival data (Ibrahim et al, 2013), binomial data (Wang et al, 2019), count data (de Oliveira et al, 2017 and ranking data (Johnson and Kuhn, 2013), can all be modeled by the proposed alternative strategy when censoring occurs. Not only to the medical sciences, the proposed strategy can also be applied to many other fields, such as, in measuring the performance of timing asynchronies using censored normal sensorimotor synchronization data in behavioral science (Bååth, 2016), comparing industrial starch grain properties with ordered categorized data in agriculture (Onofri et al, 2019), exploring forest genetics by modeling censored growth strain data for narrow-sense heritability estimation in environmental science (Davies et al, 2017), determining the importance of influential factors to lower the risk of food contamination for censored microbiological contamination data in food science (Busschaert et al, 2011), and modeling the interval-censored as well as right-censored time to dental health event in primary school children for public health science (Wang et al, 2013). In summary, the proposed JAGS Model 2 can encompass a broad range of popular model structures and be utilized in a wide spectrum of applications.…”

Section: Discussionmentioning

confidence: 99%

A Note on Bayesian Modeling Specification of Censored Data in JAGS

Qi,

Zhou,

Plummer

2020

Preprint

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Just Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling. However, the built-in function dinterval() to model censored data misspecifies the computation of deviance function, which may limit its usage to perform likelihood based model comparison.To establish an automatic approach to specify the correct deviance function in JAGS, we propose a simple alternative modeling strategy to implement Bayesian model selection for analysis of censored outcomes. The proposed approach is applicable to a broad spectrum of data types, which include survival data and many other right-, left-and interval-censored Bayesian model structures.

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“…One growth strain measurement by a strain gauge corresponded to 9 Raman spectra. After the strain gauge measurements, the splitting method as described elsewhere 32 was used to estimate the growth strain (EGS) of the stem. Each growth strain measurement by the splitting test corresponded to two measurements with strain gauges.…”

Section: Methodsmentioning

confidence: 99%

Measuring Molecular Strain in Rewetted and Never-Dried Eucalypt Wood with Raman Spectroscopy

Guo

Altaner

2019

Biomacromolecules

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To measure growth strain in wood using Raman spectroscopy, we investigated the Raman spectra of rewetted (water-saturated) Eucalyptus regnans and green Eucalyptus quadrangulata wood during tensile tests. Partial least squares models to predict the tensile strain were built from the Raman spectra. The best model could predict the tensile strain with a root mean square error of 427.5 με. Apart from the widely reported band shift at 1095 cm–1 upon mechanical strain, spectral changes at 1420, 1120, 895, and 456 cm–1 were identified. The assignments of these bands were discussed in relation to the molecular deformation of cellulose. The band shift rates during tensile tests were −3.06 and −2.15 cm–1/% for rewetted E. regnans and green E. quadrangulata wood, respectively. We successfully detected the release of the molecular growth strain in green eucalyptus wood with Raman spectroscopy by observing band shifts of the 1095 cm–1 signal. Further, there was a moderate correlation (r = 0.48) between the growth strain measured with strain gauges and the 1095 cm–1 band position. The precision of the prediction of growth strain using Raman spectroscopy was negatively affected by variation attributed to the inhomogeneity of wood on the millimeter scale and instrumental instability.

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Heritability of growth strain in Eucalyptus bosistoana: a Bayesian approach with left-censored data§

Cited by 13 publications

References 15 publications

On Bayesian modeling of censored data in JAGS

On Bayesian modeling of censored data in JAGS

A Note on Bayesian Modeling Specification of Censored Data in JAGS

Measuring Molecular Strain in Rewetted and Never-Dried Eucalypt Wood with Raman Spectroscopy

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