Estimate Growth Rates from Experimental Data.
R package growthrates
Estimate Growth Rates from Experimental Data
The population growth rate is the main indicator of population fitness. This R package provides a collection of methods to determine growth rates from experimental data, in particular from batch experiments and microwell plate reader trials.
News
- Release of version 0.8.4 to CRAN
- improved robustness and error checking of the "easylinear"" method
- Corrected parametrization of Gompertz models (0.8.2)
- Simplified handling of log-transformed parametric models (v. 0.8.1)
- Several small changes and improvements
- Added predict-methods
- Presentation at the useR!2017 conference in Brussels
Overview
The package contains basically three methods:
fit a linear regression to a subset of data with the steepest log-linear increase (a method, similar to Hall et al., 2014),
fit parametric nonlinear models to the complete data set, where the model functions can be given either in closed form or as numerically solved (system of) differential equation(s),
use maximum of the 1st derivative of a smoothing spline with log-transformed y-values (similar to Kahm et al., 2010).
The package can fit data sets of single experiments or complete series containing multiple data sets. Included are functions for extracting estimates and for plotting. The package supports growth models given as numerically solved differential equations. Multi-core computation is used to speed up fitting of parametric models.
Download and Installation of the release version (recommended)
Install package from within R or RStudio like any other package, or with:
install.packages("growthrates")
Development version
Install with package devtools:
install.packages("devtools")
library(devtools)
install_github("tpetzoldt/growthrates")
Introduction to the main functions
- html: https://tpetzoldt.github.io/growthrates/doc/Introduction.html
Writing user defined functions
- html: https://tpetzoldt.github.io/growthrates/doc/User_models.html
References
Hall, B. G., H. Acar, A. Nandipati, and M. Barlow. 2014. Growth Rates Made Easy. Mol. Biol. Evol. 31: 232-38. https://dx.doi.org/10.1093/molbev/mst187
Kahm, Matthias, Guido Hasenbrink, Hella Lichtenberg-Frate, Jost Ludwig, and Maik Kschischo. 2010. grofit: Fitting Biological Growth Curves with R. Journal of Statistical Software 33 (7): 1-21. https://dx.doi.org/10.18637/jss.v033.i07
R Core Team. 2015. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/
Soetaert, Karline, and Thomas Petzoldt. 2010. Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME. Journal of Statistical Software 33 (3): 1-28. https://dx.doi.org/10.18637/jss.v033.i03
Soetaert, Karline, Thomas Petzoldt, and R. Woodrow Setzer. 2010. Solving Differential Equations in R: Package deSolve. Journal of Statistical Software 33 (9): 1-25. https://dx.doi.org/10.18637/jss.v033.i09