LRI: Log-rate, Log-interval (LRI) method of Gingerich

View source: R/rates.R

LRIR Documentation

Log-rate, Log-interval (LRI) method of Gingerich


Gingerich (1993) introduced a method that plots on log-log scale, the rate and interval for each pair of samples in an evolutionary sequence. On this plot, the slope is interpreted as an indicator of evolutionary mode (-1 for stasis, 0.5 for random walk, 0 for directional), and the intercept is interpreted as a measure of the rate of evolution over one generation.


LRI(y, gen.per.t = 1e+06, draw = TRUE)



a paleoTS object


the number of generations per unit time


logical, if TRUE, a plot is produced


Following Gingerich (1993), a robust line is fit through the points by minimizing the sum of absolute deviations. If generations are one year long and time is measured in Myr, gen.per.t= 1e6.


A named vector with three elements: Intercept, slope, and GenerationalRate


This method was important in early attempts to disentangle evolutionary tempo and mode. I view likelihood-based methods as more informative, and in particular the estimation of 'Generational Rates' using LRI is compromised by sampling error; see Hunt (2012) and the example below.


Gingerich, P.D. 1993. Quantification and comparison of evolutionary rates. American Journal of Science 293-A:453–478.

Hunt, G. 2012. Measuring rates of phenotypic evolution and the inseparability of tempo and mode. Paleobiology 38:351–373.

See Also



xFast <- sim.GRW(ns = 20, ms = 0.5, vs = 0.2)  # fast evolution
xSlow <- sim.Stasis(ns = 20, omega = 0)        # strict stasis (zero rates)
lri.Fast <- LRI(xFast, draw = FALSE)
lri.Slow <- LRI(xSlow, draw = FALSE)
print(lri.Fast[3], 4)
print(lri.Slow[3], 4)  # LRI thinks strict stasis rates are faster!

paleoTS documentation built on Aug. 9, 2022, 1:06 a.m.