<b>Nonlinear models applied to seed germination of <i>Rhipsalis cereuscula Haw</i> (Cactaceae)
Abstract
The objective of this analysis was to fit germination data of Rhipsalis cereuscula Haw seeds to the Weibull model with three parameters using Frequentist and Bayesian methods. Five parameterizations were compared using the Bayesian analysis to fit a prior distribution. The parameter estimates from the Frequentist method were similar to the Bayesian responses considering the following non-informative a priori distribution for the parameter vectors: gamma (10³, 10³) in the model M1, normal (0, 106) in the model M2, uniform (0, Lsup) in the model M3, exp (μ) in the model M4 and Lnormal (μ, 106) in the model M5. However, to achieve the convergence in the models M4 and M5, we applied the μ from the estimates of the Frequentist approach. The best models fitted by the Bayesian method were the M1 and M3. The adequacy of these models was based on the advantages over the Frequentist method such as the reduced computational efforts and the possibility of comparison.
Downloads
DECLARATION OF ORIGINALITY AND COPYRIGHTS
I Declare that current article is original and has not been submitted for publication, in part or in whole, to any other national or international journal.
The copyrights belong exclusively to the authors. Published content is licensed under Creative Commons Attribution 3.0 (CC BY 3.0) guidelines, which allows sharing (copy and distribution of the material in any medium or format) and adaptation (remix, transform, and build upon the material) for any purpose, even commercially, under the terms of attribution.
Read this link for further information on how to use CC BY 3.0 properly.