DAISIE_ML4  R Documentation 
Computes MLE for single type species under a clade specific scenario where one parameter may vary over the clades governed by a specific distribution
DAISIE_ML4(
datalist,
initparsopt,
idparsopt,
parsfix,
idparsfix,
res = 100,
ddmodel = 0,
cond = 0,
tol = c(1e04, 1e05, 1e07),
maxiter = 1000 * round((1.25)^length(idparsopt)),
methode = "lsodes",
optimmethod = "subplex",
CS_version = create_CS_version(model = 2, relaxed_par = "cladogenesis", par_sd = 0,
par_upper_bound = Inf),
verbose = 0,
tolint = c(1e16, 1e10),
island_ontogeny = NA,
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. 
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 
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 
CS_version 
a numeric or list. Default is 1 for the standard DAISIE model, for a relaxedrate model a list with the following elements:

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. 
island_ontogeny 
In 
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 
sd 
gives the maximum likelihood estimate of the standard deviation for the parameter which is allowed to vary 
loglik 
gives the maximum loglikelihood 
df 
gives the number of estimated parameters, i.e. degrees of freedom 
conv 
gives a message on convergence of optimization; conv = 0 means convergence 
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