LRI | R Documentation |

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)

`y` |
a |

`gen.per.t` |
the number of generations per unit time |

`draw` |
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.

`lynchD`

set.seed(1) 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!

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