Description Usage Arguments See Also Examples

Takes a model previously estimated by `estimate_shift_configuration`

,
including one or more traits and a configuration of evolutionary shifts, and detect which of these regime shifts
are convergent.

1 2 | ```
estimate_convergent_regimes(model, criterion = c("AICc", "pBIC", "BIC"),
method = c("backward", "rr"), fixed.alpha = FALSE, nCores = 1)
``` |

`model` |
fitted object of class l1ou returned by |

`criterion` |
information criterion for model selection (see Details in |

`method` |
search method for finding convergent regimes. “rr” is based on genlasso,
a regularized linear regression estimation. Currenly, this method can only accept a single trait.
The default “backward” method is a heuristic similar to |

`fixed.alpha` |
indicates if the alpha parameters should be optimized while phylolm optimize the likelihood function. |

`nCores` |
number of processes to be created for parallel computing. If nCores=1 then it will run sequentially. Otherwise, it creates nCores processes by using mclapply function. For parallel computing it, requires parallel package. |

1 2 3 4 5 6 7 8 9 10 | ```
library(l1ou)
data("lizard.traits", "lizard.tree")
Y <- lizard.traits[, 1:1]
## first fit a model to find individual shifts (no convergence assumed):
fit_ind <- estimate_shift_configuration(lizard.tree, Y, criterion="AICc")
fit_ind
## then detect which of these shifts are convergent:
fit_conv <- estimate_convergent_regimes(fit_ind, criterion="AICc")
fit_conv
plot(fit_conv)
``` |

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