非线性回归的似然区间。

IF 1.1 Q4 PHARMACOLOGY & PHARMACY
Moon Hee Lee, Kyun-Seop Bae
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引用次数: 0

摘要

Wald置信区间是非线性模型参数区间估计的常用方法。由于Wald置信区间是围绕点估计对称的,因此它不能反映非线性回归中似然分布的不对称性。相反,似然区间是直接从似然曲线估计出来的,它确实反映了似然曲线的形状。然而,缺乏估计似然区间和可视化似然曲线的软件,这对在非线性模型中使用似然区间构成了障碍。需要软件实现来处理这些任务。非线性模型的似然区间估计和似然曲线绘制在R软件中尚未实现。本文介绍了在wnl R软件包中实现似然区间估计和似然曲线绘制。为了演示实现函数的用法,给出了一个非线性药代动力学模型拟合浓度-时间数据的例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Likelihood interval for nonlinear regression.

Likelihood interval for nonlinear regression.

Likelihood interval for nonlinear regression.

Likelihood interval for nonlinear regression.

Wald confidence interval has been used as the conventional method of interval estimation for the parameters in nonlinear models. Because Wald confidence interval is symmetric around the point estimate, it does not reflect the asymmetry of the likelihood profile in nonlinear regression. In contrast, a likelihood interval is estimated directly from the likelihood profile and does reflect the shape of the likelihood profile. However, the lack of software for the estimation of likelihood intervals and visualization of likelihood profiles posed an obstacle to the use of likelihood intervals in nonlinear models. There was a need for software implementation to tackle these tasks. Likelihood interval estimation and likelihood profile plotting for nonlinear models had not been previously implemented in R software. This article describes the implementation of likelihood interval estimation and likelihood profile plotting in the wnl R software package. To demonstrate the usage of implemented functions, an example of fitting a nonlinear pharmacokinetic model to concentration-time data is presented.

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来源期刊
Translational and Clinical Pharmacology
Translational and Clinical Pharmacology Medicine-Pharmacology (medical)
CiteScore
1.60
自引率
11.10%
发文量
17
期刊介绍: Translational and Clinical Pharmacology (Transl Clin Pharmacol, TCP) is the official journal of the Korean Society for Clinical Pharmacology and Therapeutics (KSCPT). TCP is an interdisciplinary journal devoted to the dissemination of knowledge relating to all aspects of translational and clinical pharmacology. The categories for publication include pharmacokinetics (PK) and drug disposition, drug metabolism, pharmacodynamics (PD), clinical trials and design issues, pharmacogenomics and pharmacogenetics, pharmacometrics, pharmacoepidemiology, pharmacovigilence, and human pharmacology. Studies involving animal models, pharmacological characterization, and clinical trials are appropriate for consideration.
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