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FlexParamCurve: R package for flexible fitting of nonlinear parametric curves
Author(s) -
Oswald Stephen A.,
Nisbet Ian C. T.,
Chiaradia Andre,
Arnold Jennifer M.
Publication year - 2012
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/j.2041-210x.2012.00231.x
Subject(s) - curve fitting , parametric statistics , nonlinear system , growth curve (statistics) , a priori and a posteriori , nonlinear regression , parametric equation , parametric model , computer science , model selection , selection (genetic algorithm) , non linear least squares , mathematics , mathematical optimization , econometrics , algorithm , statistics , machine learning , estimation theory , regression analysis , geometry , philosophy , physics , epistemology , quantum mechanics
Summary 1. Nonlinear, parametric curve‐fitting provides a framework for understanding diverse ecological and evolutionary trends (e.g. growth patterns and seasonal cycles). Currently, parametric curve‐fitting requires a priori assumptions of curve trajectories, restricting their use for exploratory analyses. Furthermore, use of analytical techniques [nonlinear least‐squares (NLS) and nonlinear mixed‐effects models] for complex parametric curves requires efficient choice of starting parameters. 2. We illustrate the new R package FlexParamCurve that automates curve selection and provides tools to analyse nonmonotonic curve data in NLS and nonlinear mixed‐effects models. Examples include empirical and simulated data sets for the growth of seabird chicks. 3. By automating curve selection and parameterization during curve‐fitting, FlexParamCurve extends current possibilities for parametric analysis in ecological and evolutionary studies.