nlmeVPC: Visual Model Diagnosis for the Nonlinear Mixed Effect Model
- 주제(기타) Computer Science, Interdisciplinary Applications; Statistics & Probability
- 설명문(일반) [Kang, Eun-Hwa; Ko, Myungji; Lee, Eun-Kyung] Ewha Womans Univ, Dept Stat, Seoul, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 R FOUNDATION STATISTICAL COMPUTING
- 발행년도 2023
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000213519
- 본문언어 영어
초록/요약
A nonlinear mixed effects model is useful when the data are repeatedly measured within the same unit or correlated between units. Such models are widely used in medicine, disease mechanics, pharmacology, ecology, social science, psychology, etc. After fitting the nonlinear mixed effect model, model diagnostics are essential for verifying that the results are reliable. The visual predictive check (VPC) has recently been highlighted as a visual diagnostic tool for pharmacometric models. This method can also be applied to general nonlinear mixed effects models. However, functions for VPCs in existing R packages are specialized for pharmacometric model diagnosis, and are not suitable for general nonlinear mixed effect models. In this paper, we propose nlmeVPC, an R package for the visual diagnosis of various nonlinear mixed effect models. The nlmeVPC package allows for more diverse model diagnostics, including visual diagnostic tools that extend the concept of VPCs along with the capabilities of existing R packages.
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