Maximization of the loglikelihood under the Guilds Model, conditioned on guild size.
Description
This function computes the maximum likelihood estimates of the parameters of the guilds model, conditioned on guild size.
Usage
1  maxLikelihood.Guilds.Conditional(init_vals, model, method, sadx, sady, verbose = TRUE)

Arguments
init_vals 

model 
The chosen model to calculate the maximum likelihood for, please note that the vector of parameters should contain the corresponding parameters in the right order. The user can pick one of these models: 
method 
Optimization of the Likelihood can be done using two different methods: 
sadx 
The Species Abundance Distribution of guild X 
sady 
The Species Abundance Distribution of guild Y 
verbose 
TRUE/FALSE flag, indicates whether intermediate output is shown on screen 
Value
if the method used was "simplex", the output is a list containing the following:
par 
a vector containing the parameter values at the maximum likelihood 
fvalues 
the likelihood at the corresponding parameter values 
conv 
gives a message on convergence of optimization; conv = 0 means convergence 
if the method used was "subplex", the output is a list containing the following:
par 
a vector containing the parameter values at the maximum likelihood 
value 
the likelihood at the corresponding parameter values 
counts 
Number of function evaluations required 
convergence 
2: invalid input 
message 
A character string giving a diagnostic message from the optimizer, 
hessian 
Hessian matrix (not implemented for this package) 
Author(s)
Thijs Janzen
Examples
1 2 3  initParams < c(20,0.1); #Initial parameters for the D0 model, c(theta,alpha)
maxLikelihood.Guilds.Conditional(initParams,model="D0", method="simplex",
sadx = 1:20, sady = 1:20, verbose = TRUE)

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