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
)The output is a dataframe containing estimated parameters and maximum loglikelihood.
gives the maximum likelihood estimate of lambda^c, the rate of cladogenesis
gives the maximum likelihood estimate of mu, the extinction rate
gives the maximum likelihood estimate of K, the carrying-capacity
gives the maximum likelihood estimate of gamma, the immigration rate
gives the maximum likelihood estimate of lambda^a, the rate of anagenesis
gives the maximum likelihood estimate of lambda^c2, the rate of cladogenesis for the second group of species
gives the maximum likelihood estimate of mu2, the extinction rate for the second group of species
gives the maximum likelihood estimate of K2, the carrying-capacity for the second group of species
gives the maximum likelihood estimate of gamma2, the immigration rate for the second group of species
gives the maximum likelihood estimate of lambda^a2, the rate of anagenesis for the second group of species
gives the maximum loglikelihood
gives the number of estimated parameters, i.e. degrees of feedom
gives a message on convergence of optimization; conv = 0 means convergence
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
The initial values of the parameters that must be optimized, they are all positive.
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.
The values of the parameters that should not be optimized.
The ids of the parameters that should not be optimized, e.g. c(1,3) if lambda^c and K should not be optimized.
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.
Sets the maximum number of species for which a probability must be computed, must be larger than the size of the largest clade.
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 = 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
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.
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.
Sets the maximum number of iterations in the optimization.
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, ode method is
assumed. The default method overall is
"lsodes" for DAISIE_ML_CS()
and "ode45" from ode() for
DAISIE_ML_IW().
Method used in likelihood optimization. Default is
`subplex` (see `subplex()` for full details).
Alternative is "simplex" which was the method in previous versions.
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.
Vector of two elements containing the absolute and relative tolerance of the integration.
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.
The number of cycles the optimizer will go through. Default is 1.