maxLikelihood.Guilds.Conditional: Maximization of the loglikelihood under the Guilds Model,...

In GUILDS: Implementation of Sampling Formulas for the Unified Neutral Model of Biodiversity and Biogeography, with or without Guild Structure

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

maxLikelihood.Guilds.Conditional(init_vals, model, sadx, sady, verbose = TRUE)

Arguments

init_vals	init_vals corresponds to a vector of parameter values in which to start the Maxmimum Likelihood algorithm, depending on the provided model: - model: "D0" parameters = c(theta, alpha) - model: "D1" parameters = c(theta, alpha X, alpha Y)
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: - "D0" - "D1"
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

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 -1: number of maximum function evaluations exceeded 0: success: convergence 1: limit of machine precision reached
message	A character string giving a diagnostic message from the optimizer,
hessian	Hessian matrix (not implemented for this package)

Author(s)

Thijs Janzen

Examples

theta = 20
alpha = 0.1
initParams <- c(theta, alpha)
maxLikelihood.Guilds.Conditional(initParams,
                                 model = "D0",
                                 sadx  = 1:20,
                                 sady  = 1:20,
                                 verbose = TRUE)

GUILDS documentation built on Aug. 21, 2023, 5:10 p.m.