# ESD: Compute Expected Squared Divergence (ESD) for Evolutionary... In paleoTS: Analyze Paleontological Time-Series

 ESD R Documentation

## Compute Expected Squared Divergence (ESD) for Evolutionary Models

### Description

Computes for a specified model and duration of time the expected squared divergence (ESD), which is a useful measure of the magnitude or rate of change across different models.

### Usage

```ESD(
y,
dt,
model = c("GRW", "URW", "Stasis", "allThree"),
pool = TRUE,
...
)
```

### Arguments

 `y` a `paleoTS` object `dt` the time interval to evaluate ESD `model` the model of evolution to assume; see Details `method` Joint or AD parameterization `pool` logical, if TRUE, variances are averaged (pooled) across samples `...` other arguments to the model-fitting functions

### Details

Hunt (2012) argued that rate metrics make sense only in the context of specific models of evolution. It is thus difficult to meaningfully compare rates across sequences generated by different evolutionary processes. ESD values can be used for a specified model and duration as a comparable measure of the amount of evolutionary change that is expected. Acceptable values for the model argument can be "GRW" for the general random walk (directional change), "URW" for the unbiased random walk, and "Stasis." In addition, one can also specify "allThree", in which case all these models will be fit and the resulting ESD will be the weighted average of them, using model support (Akaike weights) for the weighting (see Hunt [2012], p. 370)

the ESD value

### References

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

### Examples

```  x<- sim.GRW(ns=20)
esd.urw<- ESD(x, dt=10, model="URW")
esd.all<- ESD(x, dt=10, model="allThree")
```

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