DAISIE_ML2 | R Documentation |

Computes MLE for multiple islands under a clade specific scenario

```
DAISIE_ML2(
datalist,
initparsopt,
idparsopt,
parsfix,
idparsfix,
idparsmat,
res = 100,
ddmodel = 0,
cond = 0,
island_ontogeny = NA,
tol = c(1e-04, 1e-05, 1e-07),
maxiter = 1000 * round((1.25)^length(idparsopt)),
methode = "lsodes",
optimmethod = "subplex",
verbose = 0,
tolint = c(1e-16, 1e-10),
jitter = 0,
num_cycles = 1
)
```

`datalist` |
Data object containing information on colonisation and
branching times. This object can be generated using the DAISIE_dataprep
function, which converts a user-specified data table into a data object,
but the object can of course also be entered directly.
It is an R list object with the following elements. |

`initparsopt` |
The initial values of the parameters that must be optimized, they are all positive. |

`idparsopt` |
The ids of the parameters that must be optimized. The ids
are defined as follows: |

`parsfix` |
The values of the parameters that should not be optimized. |

`idparsfix` |
The ids of the parameters that should not be optimized, e.g. c(1,3) if lambda^c and K should not be optimized. |

`idparsmat` |
For datatype = 'multiple' only: Matrix containing the ids
of the parameters, linking them to initparsopt and parsfix. Per island
system we use the following order: |

`res` |
Sets the maximum number of species for which a probability must be computed, must be larger than the size of the largest clade. |

`ddmodel` |
Sets the model of diversity-dependence: |

`cond` |
cond = 0 : conditioning on island age |

`island_ontogeny` |
In |

`tol` |
Sets the tolerances in the optimization. Consists of: |

`maxiter` |
Sets the maximum number of iterations in the optimization. |

`methode` |
Method of the ODE-solver. Supported Boost |

`optimmethod` |
Method used in likelihood optimization. Default is
'subplex' (see 'subplex()' for full details).
Alternative is |

`verbose` |
A numeric vector of length 1, which in simulations and 'DAISIEdataprep()' can be '1' or '0', where '1' gives intermediate output should be printed. For ML functions a numeric determining if intermediate output should be printed. The default: '0' does not print, '1' prints the initial likelihood and the settings that were selected (which parameters are to be optimised, fixed or shifted), '2' prints the same as '1 and also the intermediate output of the parameters and loglikelihood, while '3' the same as '2' and prints intermediate progress during likelihood computation. |

`tolint` |
Vector of two elements containing the absolute and relative tolerance of the integration. |

`jitter` |
Numeric for |

`num_cycles` |
The number of cycles the optimizer will go through. Default is 1. |

The output is a dataframe containing estimated parameters and maximum loglikelihood.

`lambda_c` |
gives the maximum likelihood estimate of lambda^c, the rate of cladogenesis |

`mu` |
gives the maximum likelihood estimate of mu, the extinction rate |

`K` |
gives the maximum likelihood estimate of K, the carrying-capacity |

`gamma` |
gives the maximum likelihood estimate of gamma, the immigration rate |

`lambda_a` |
gives the maximum likelihood estimate of lambda^a, the rate of anagenesis |

`lambda_c2` |
gives the maximum likelihood estimate of lambda^c2, the rate of cladogenesis for the second group of species |

`mu2` |
gives the maximum likelihood estimate of mu2, the extinction rate for the second group of species |

`K2` |
gives the maximum likelihood estimate of K2, the carrying-capacity for the second group of species |

`gamma2` |
gives the maximum likelihood estimate of gamma2, the immigration rate for the second group of species |

`lambda_a2` |
gives the maximum likelihood estimate of lambda^a2, the rate of anagenesis for the second group of species |

`loglik` |
gives the maximum loglikelihood |

`df` |
gives the number of estimated parameters, i.e. degrees of feedom |

`conv` |
gives a message on convergence of optimization; conv = 0 means convergence |

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