...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Seed Science Research >Modelling seed germination in response to continuous variables: use and limitations of probit analysis and alternative approaches.
【24h】

Modelling seed germination in response to continuous variables: use and limitations of probit analysis and alternative approaches.

机译：响应连续变量对种子发芽进行建模：概率分析和替代方法的使用和局限性。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Probit-based models relating a proportional response variable to a temporal explanatory variable, assuming that the times to response are normally distributed within the population, have been used in seed biology for describing the rate of loss of viability during seed ageing and the progress of germination over time in response to environmental signals (e.g. water, temperature). These models may be expressed as generalized linear models (GLMs) with a probit (cumulative normal distribution) link function, and, using GLM fitting procedures in current statistical software, parameters of these models are efficiently estimated while taking into account the binomial error distribution of the dependent variable. The fitted parameters can then be used to calculate the 'traditional' model parameters, such as the hydro- or hydrothermal time constant, the mean or median response of the seeds (e.g. mean time to death, median base water potential), and the standard deviation of the normal distribution of that response. Furthermore, through consideration of the deviance and residuals, performing model evaluation and modification can lead to improved understanding of the underlying physiological/ecological processes. However, fitting a binomial GLM is not appropriate for the cumulative count data often collected from germination studies, as successive observations are not independent, and time-to-event/survival analysis should be considered instead. This review discusses well-known probit-based models, providing advice on how to collect appropriate data and fit the models to those data, and gives an overview of alternative analysis approaches to improve understanding of the underlying mechanisms of seed dormancy and germination behaviour.
机译：假设响应时间在人群中呈正态分布，则基于比例的模型将比例响应变量与时间解释变量相关联，该模型已用于种子生物学中，用于描述种子老化过程中活力丧失的速率和发芽过程。随着时间的推移响应环境信号（例如水，温度）。这些模型可以表示为具有概率（累积正态分布）链接函数的广义线性模型（GLM），并且使用当前统计软件中的GLM拟合程序，可以在考虑到模型的二项式误差分布的情况下有效地估计这些模型的参数。因变量。然后，拟合的参数可用于计算“传统”模型参数，例如水热或水热时间常数，种子的平均或中值响应（例如，平均死亡时间，中位基础水势）和标准该响应的正态分布偏差。此外，通过考虑偏差和残差，执行模型评估和修改可以提高对基本生理/生态过程的理解。然而，由于连续观察并不是独立的，因此拟合二项式GLM不适用于通常从发芽研究中收集的累积计数数据，而应考虑事件发生/生存时间分析。这篇综述讨论了众所周知的基于概率的模型，为如何收集适当的数据以及如何使模型适合这些数据提供了建议，并概述了替代分析方法，以增进对种子休眠和萌发行为的潜在机制的理解。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号