FlexParamCurve: R package for flexible fitting of nonlinear parametric curves

Abstract

Summary1. 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 mixedeffects 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.

Weather conditions, brood size and hatching order affect Common SwiftApus apusnestlings’ survival and growth

“…We were interested in investigating potential differences in the entire pattern of nestling growth because the entire growth trajectory, and not just the final mass nestlings achieved, may affect their future survival (Gebhardt-Heinrich & Richner 1998). Because Common Swift nestlings experience a period of considerable body mass recession (up to 20% of their weight) before fledging, we used double-Richards (or positive-negative Richards) growth curves, which are able to fully describe both nestlings growth and their following mass recession (Oswald et al 2012). Thirdly and finally, we investigated the possible effects of weather conditions on nestling growth rate and their potentially differential influence on nestlings of different sex, age and hatching order.…”

“…The widespread application of these curves in the ornithological literature has been hampered by computational difficulties in fitting non-linear regression models. Recently, a new package called FlexParamCurve (Oswald et al 2012) has been released for the software R (R Core Team 2013). This package allows fitting 32 possible growth curves of the doubleRichards family (all possible reductions in the second curve, fixing A ′ , k ′ , i ′ or m ′ both when m is fixed or estimated).…”

“…This package allows fitting 32 possible growth curves of the doubleRichards family (all possible reductions in the second curve, fixing A ′ , k ′ , i ′ or m ′ both when m is fixed or estimated). This package also provides automatized routines for assessing which curve, among the 32 alternative parameterizations of the double-Richard curve, best fits the data at hand (Oswald et al 2012).…”

Construction and analysis of the Brassica oleracea pangenome

“…Least square fits were performed for both reduced and compete models. The resulting fits were compared using extraF.nls() function in the FlexParamCurve (Oswald et al, 2012) package.…”

Freeze–thaw cycles have minimal effect on the mineralisation of low molecular weight, dissolved organic carbon in Arctic soils

“…This model has been shown to be a good fit for the mineralisation over 2 weeks of the LMW DOC substrates used in this study and in a number of environments including temperate grasslands and Arctic tundra (Boddy et al 2007; Farrell et al 2011;Glanville et al 2012). The double firstorder exponential decay model was found to generally be a significantly better fit than the single first-order exponential decay model using the extraF.nls function in the FlexParamCurve library of R to perform an F test (Oswald et al 2012). The parameters of the double first-order exponential decay model were also used to assess carbon use efficiency for the temperate soil as described by Farrell et al (2011), assuming a 2 represents C used for biosynthesis.…”