DAISIE_ML2: Computes MLE for multiple islands under a clade specific...
In DAISIE: Dynamical Assembly of Islands by Speciation, Immigration and Extinction

DAISIE_ML2

R Documentation

Computes MLE for multiple islands under a clade specific scenario

Description

Computes MLE for multiple islands under a clade specific scenario

Usage

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
)

Arguments

`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. The first element of the list has two or three components: `$island_age` - the island age Then, depending on whether a distinction between types is made, we have: `$not_present` - the number of mainland lineages that are not present on the island or: `$not_present_type1` - the number of mainland lineages of type 1 that are not present on the island `$not_present_type2` - the number of mainland lineages of type 2 that are not present on the island The remaining elements of the list each contains information on a single colonist lineage on the island and has 5 components: `$colonist_name` - the name of the species or clade that colonized the island `$branching_times` - island age followed by stem age of the population/species in the case of Non-endemic, Non-endemic_MaxAge species and Endemic species with no close relatives on the island. For endemic clades with more than one species on the island (cladogenetic clades/ radiations) these should be island age followed by the branching times of the island clade including the stem age of the clade `$stac` - the status of the colonist * Non_endemic_MaxAge: 1 * Endemic: 2 * Endemic&Non_Endemic: 3 * Non_Endemic: 4 * Endemic_Singleton_MaxAge: 5 * Endemic_Clade_MaxAge: 6 * Endemic&Non_Endemic_Clade_MaxAge: 7 `$missing_species` - number of island species that were not sampled for particular clade (only applicable for endemic clades) `$type1or2` - whether the colonist belongs to type 1 or type 2
`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: id = 1 corresponds to lambda^c (cladogenesis rate) id = 2 corresponds to mu (extinction rate) id = 3 corresponds to K (clade-level carrying capacity) id = 4 corresponds to gamma (immigration rate) id = 5 corresponds to lambda^a (anagenesis rate) id = 6 corresponds to lambda^c (cladogenesis rate) for an optional subset of the species id = 7 corresponds to mu (extinction rate) for an optional subset of the species id = 8 corresponds to K (clade-level carrying capacity) for an optional subset of the species id = 9 corresponds to gamma (immigration rate) for an optional subset of the species id = 10 corresponds to lambda^a (anagenesis rate) for an optional subset of the species id = 11 corresponds to p_f (fraction of mainland species that belongs to the second subset of species.
`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: * lac = (initial) cladogenesis rate * mu = extinction rate * K = maximum number of species possible in the clade * gam = (initial) immigration rate * laa = (initial) anagenesis rate Example: `idparsmat = rbind(c(1, 2, 3, 4, 5), c(1, 2, 3, 6, 7))` has different rates of immigration and anagenesis for the two islands.
`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: ddmodel = 0 : no diversity dependence ddmodel = 1 : linear dependence in speciation rate ddmodel = 11: linear dependence in speciation rate and in immigration rate ddmodel = 2 : exponential dependence in speciation rate ddmodel = 21: exponential dependence in speciation rate and in immigration rate
`cond`	cond = 0 : conditioning on island age cond = 1 : conditioning on island age and non-extinction of the island biota . cond > 1 : conditioning on island age and having at least cond colonizations on the island. This last option is not yet available for the IW model
`island_ontogeny`	In `DAISIE_sim_time_dep()`, `DAISIE_ML_CS` and plotting a string describing the type of island ontogeny. Can be `"const"`, `"beta"` for a beta function describing area through time. In all other functions a numeric describing the type of island ontogeny. Can be `0` for constant, `1` for a beta function describing area through time. In ML functions `island_ontogeny = NA` assumes constant ontogeny. Time dependent estimation is not yet available as development is still ongoing. Will return an error if called in that case.
`tol`	Sets the tolerances in the optimization. Consists of: reltolx = relative tolerance of parameter values in optimization reltolf = relative tolerance of function value in optimization abstolx = absolute tolerance of parameter values in optimization.
`maxiter`	Sets the maximum number of iterations in the optimization.
`methode`	Method of the ODE-solver. Supported Boost `ODEINT` solvers (steppers) are: `"odeint::runge_kutta_cash_karp54"` `"odeint::runge_kutta_fehlberg78"` `"odeint::runge_kutta_dopri5"` `"odeint::bulirsch_stoer"` without `odeint::`-prefix, `deSolve{ode}` method is assumed. The default method overall is `"lsodes"` for `DAISIE_ML_CS()` and `"ode45"` from `ode()` for `DAISIE_ML_IW()`.
`optimmethod`	Method used in likelihood optimization. Default is 'subplex' (see 'subplex()' for full details). Alternative is `"simplex"` which was the method in previous versions.
`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 `optimizer()`. Jitters the parameters being optimized by the specified amount which should be very small, e.g. 1e-5. Jitter when `link{subplex}{subplex}()` produces incorrect output due to parameter transformation.
`num_cycles`	The number of cycles the optimizer will go through. Default is 1.

Value

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