Abstract
Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linearand noisy timeseries data using linear regression and adhoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, aflexible toolkit to implement local linear regression techniques to objectively and reproduciblyestimatemonotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences.
Original language | English |
---|---|
Pages (from-to) | 759-764 |
Number of pages | 6 |
Journal | Journal of Experimental Biology |
Volume | 220 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1 Mar 2017 |
Externally published | Yes |