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(1e04, 1e05, 1e07), maxiter = 1000 * round((1.25)^length(idparsopt)), methode = "lsodes", optimmethod = "subplex", verbose = 0, tolint = c(1e16, 1e10), 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 userspecified 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 diversitydependence: 
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 ODEsolver. Supported Boost 
optimmethod 
Method used in likelihood optimization. Default is
'subplex' (see 'subplex()' for full details).
Alternative is 
verbose 
In simulation and dataprep functions a logical,

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 carryingcapacity 
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 carryingcapacity 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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