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Additional file 1 of Bayesian modeling of plant drought resistance pathway

Figshare2019-03-14 更新2026-04-29 收录

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https://figshare.com/articles/dataset/Additional_file_1_of_Bayesian_modeling_of_plant_drought_resistance_pathway/7842680 数据链接链接失效反馈

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资源简介：

R code for generating synthetic data and estimating parameters. Also serves as the main execution file. (R 5.02 kb)

应用场景：

创建时间：

2019-03-14

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We consider a set of minimal identification conditions for dynamic factor models. These conditions have economic interpretations and require fewer restrictions than the static factor framework. Under

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Mean parameter estimates, standard deviation (SD), Monte Carlo error (MC error), odds ratios (OR), and 95% credible intervals for best-fitting model investigating the effect of covariates on the probability of bovine tuberculosis infection from 2005–2010 in Modified Accredited Zone in Michigan, USA.

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“HET” represents the random effect capturing region-wide.

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Appendix B. A table presenting parameter estimates (mean posterior values and 95% credible interval) for fixed-effects coefficients, β parameters, for each study species.

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A table presenting parameter estimates (mean posterior values and 95% credible interval) for fixed-effects coefficients, β parameters, for each study species.

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S2. Fitting model candidates to high-fidelity D 1j datasets from Anchoring the mean generation time in the SEIR to mitigate biases in ℜ 0 estimates due to uncertainty in the distribution of the epidemiological delays

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This appendix illustrates the process of fitting various parameterisations of the SE1IjR model (M1j) to high-fidelity D1j incidence reports. We mean by parameterisation the decision of categorising mo

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Posterior standard deviation estimates of the 4D SIRD model parameters.

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Posterior standard deviation estimates of the 4D SIRD model parameters.

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