fitGpunc | R Documentation |

Fit trait evolution model with punctuations estimated from the data

fitGpunc( y, ng = 2, minb = 7, pool = TRUE, oshare = TRUE, method = c("Joint", "AD"), silent = FALSE, hess = FALSE, parallel = FALSE, ... )

`y` |
a |

`ng` |
number of groups (segments) in the sequence |

`minb` |
minimum number of populations within each segment |

`pool` |
if TRUE, sample variances are substituted with their pooled estimate |

`oshare` |
logical, if TRUE, variance assumed to be shared (equal) across segments |

`method` |
parameterization to use: |

`silent` |
logical, if TRUE, progress updates are suppressed |

`hess` |
if TRUE, standard errors computed from the Hessian matrix are returned |

`parallel` |
logical, if TRUE, the analysis is done in parallel |

`...` |
other arguments, passed to optimization functions |

This function tests all possible shift points for punctuations, subject to the
constraint that the number of populations in each segment is always >= `minb`

. The
shiftpoint yielding the highest log-likelihood is returned as the solution, along with
the log-likelihoods (`all.logl`

) of all tested shift points (`GG`

).

The function uses `opt.punc`

(if `method = "AD"`

) or `opt.joint.punc`

(if `method = "Joint"`

) to do the fitting.

a `paleoTSfit`

object with the results of the model-fitting.

Calculations can be sped up by setting `parallel = TRUE`

, which uses functions from
the `doParallel`

package to run the bootstrap replicates in parallel, using
one fewer than the number of detected cores.

`fit9models`

, `sim.punc`

x <- sim.punc(ns = c(15, 15), theta = c(0,3), omega = c(0.1, 0.1)) w.punc <- fitGpunc(x, oshare = TRUE) plot(x, modelFit = w.punc)

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