DAISIE_ML_IW: Maximization of the loglikelihood under the DAISIE model with...

In xieshu95/Trait_dependent_TraiSIE: Dynamical Assembly of Islands by Speciation, Immigration and Extinction

Description

This function computes the maximum likelihood estimates of the parameters of the DAISIE model with island-wide diversity-dependence for data from lineages colonizing an island. It also outputs the corresponding loglikelihood that can be used in model comparisons.

Usage

DAISIE_ML_IW(datalist, initparsopt, idparsopt, parsfix, idparsfix,
  res = 100, ddmodel = 11, cond = 0, tol = c(1e-04, 1e-05, 1e-07),
  maxiter = 1000 * round((1.25)^length(idparsopt)), methode = "ode45",
  optimmethod = "subplex", verbose = 0, tolint = c(1e-16, 1e-14))

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 three components: $island_age - the island age $not_present - the number of mainland lineages 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 and stem age of the population/species in the case of Non-endemic, Non-endemic_MaxAge and Endemic anagenetic species. For cladogenetic species these should be island age and branching times of the radiation including the stem age of the radiation. $stac - the status of the colonist * Non_endemic_MaxAge: 1 * Endemic: 2 * Endemic&Non_Endemic: 3 * Non_endemic: 4 * Endemic_MaxAge: 5 $missing_species - number of island species that were not sampled for particular clade (only applicable for endemic clades)
initparsopt	The initial values of the parameters that must be optimized
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)
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.
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
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. See package deSolve for details. Default is "lsodes"
optimmethod	Method used in likelihood optimization. Default is "subplex" (see subplex package). Alternative is 'simplex' which was the method in previous versions.
verbose	Specifies whether intermediate output should be provided, because optimizationmay take long. Default is 0, no output. A value of 1 means the parameters and loglikelihood are printed. A value of 2 means also intermediate progress during loglikelihood computation is shown.
tolint	Vector of two elements containing the absolute and relative tolerance of the integration

Details

The result of sort(c(idparsopt, idparsfix)) should be identical to c(1:5). If not, an error is reported that the input is incoherent. The same happens when the length of initparsopt is different from the length of idparsopt, and the length of parsfix is different from the length of idparsfix.

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
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

Author(s)

Rampal S. Etienne

References

Valente, L.M., A.B. Phillimore and R.S. Etienne (2015). Equilibrium and non-equilibrium dynamics simultaneously operate in the Galapagos islands. Ecology Letters 18: 844-852. <DOI:10.1111/ele.12461>.

See Also

DAISIE_loglik_IW, DAISIE_ML_CS DAISIE_sim

xieshu95/Trait_dependent_TraiSIE documentation built on Nov. 22, 2019, 7:51 a.m.