A convenience wrapper function that can calls other `paleoTS`

functions to fit the unbiased random walk (URW), general random walk (GRW), Stasis, Strict Stasis, Ornstein-Uhlenbeck (OU) and covariate-tracking (covTrack) models.

1 2 |

`y` |
a |

`model` |
the model to be fit; one of c("GRW", "URW", "Stasis", "OU", "covTrack") |

`method` |
parameterization to use: |

`pool` |
logical indicating whether to pool variances across samples |

`z` |
the covariate variable; only used for the covTrack model |

`hess` |
logical, indicating whether to calculate standard errors from the Hessian matrix |

For the covariate-tracking model, z should be a vector of length *n* when `method="Joint"`

and *n*-1 when `method="AD"`

, where *n* is the number of samples in `y`

.

Note that the AD method has not been implemented for the OU model. The Joint method seems to do rather better for this model, anyway.

`fitMult`

fits these models (excpet for the OU model) over multiple `paleoTS`

objects, under the assumption that the same model applies to all the trait sequences. Parameters other than the stasis mean (`theta`

) and the ancestral state (`anc`

) are also assumed to be shared among sequences.

A `paleoTSfit`

object.

Gene Hunt

Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. *Paleobiology* ** 32**:578–601.

Hunt, G., M. J. Hopkins, and S. L. Lidgard 2015. Simple versus complex models of trait evolution and stasis as a response to environmental change. *PNAS* ** 112**:4885–4890.

`opt.GRW`

, `opt.joint.GRW`

, `opt.covTrack`

, `opt.joint.GRW`

1 2 3 4 5 | ```
x<- sim.Stasis(ns=30, theta=10, omega=1)
s1<- fitSimple(x, model="URW")
s2<- fitSimple(x, model="Stasis")
s3<- fitSimple(x, model="StrictStasis")
compareModels(s1, s2, s3)
``` |

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