Description Usage Arguments Value Note Author(s) References See Also Examples

Function `optimal.params.sloss()`

returns maximum likelihood
estimates of `theta`

and `m(k)`

using numerical
optimization.

It differs from `untb`

's `optimal.params()`

function as it
applies to a network of smaller community samples `k`

instead of
to a single large community sample.

Although there is a single, common `theta`

for all communities,
immigration estimates are provided for each local community `k`

,
sharing a same biogeographical background.

1 | ```
optimal.params.sloss(D, nbres = 100, ci = FALSE, cint = c(0.025, 0.975))
``` |

`D` |
Species counts over a network of community samples (species by sample table) |

`nbres` |
Number of resampling rounds for |

`ci` |
Specifies whether bootstraps confidence intervals should be provided for estimates |

`cint` |
Bounds of confidence intervals, if ci = T |

`theta` |
Mean |

`I` |
The vector of estimated immigration numbers |

Output of the bootstrap procedure, if ci = T:

`thetaci` |
Confidence interval for |

`msampleci` |
Confidence intervals for |

`thetasamp` |
theta estimates provided by the resampling procedure |

`Iboot` |
Bootstrapped values of |

`mboot` |
Bootstrapped values of |

The function returns unhelpful output when run with the
`caruso`

dataset as in `optimal.params.sloss(caruso)`

. The
reason for this behaviour is unknown.

Francois Munoz

Francois Munoz, Pierre Couteron, B. R. Ramesh, and Rampal S. Etienne
2007. “Estimating parameters of neutral communities: from one
single large to several small samples”. *Ecology*
88(10):2482-2488

optimal.params, optimal.params.gst

1 2 |

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