DAMOCLES (version 1.1)

DAMOCLES_ML: Maximization of the loglikelihood under the DAMOCLES model

Description

This function computes the maximum likelihood estimates of the parameters of the DAMOCLES model for a given phylogeny and presence-absence data. It also outputs the corresponding loglikelihood that can be used in model comparisons.

Usage

DAMOCLES_ML(
   phy = rcoal(10),
   pa = matrix(c(phy$tip.label,sample(c(0,1),Ntip(phy),replace = T)),
      nrow = Ntip(phy),ncol = 2),
   initparsopt = c(0.1,0.1),
   idparsopt = 1:length(initparsopt),
   parsfix = 0,
   idparsfix = (1:3)[-idparsopt],
   pars2 = c(1E-3,1E-4,1E-5,1000),
   pchoice = 0
   )

Arguments

phy

phylogeny in phylo format

pa

presence-absence table. The first column contains the labels of the species (corresponding to the tip labels in the phylogeny. The second column contains the presence (1) or absence (0) of species in the local community.

initparsopt

The initial values of the parameters that must be optimized

idparsopt

The ids of the parameters that must be optimized, e.g. 1:2 for extinction rate, and offset of immigration rate The ids are defined as follows: id == 1 corresponds to mu (extinction rate) id == 2 corresponds to gamma_0 (offset of immigration rate) id == 3 corresponds to gamma_1 (parameter controlling decline in immigration rate with time)

parsfix

The values of the parameters that should not be optimized. See idparsfix.

idparsfix

The ids of the parameters that should not be optimized, e.g. c(1,3) if mu and gamma_1 should not be optimized, but only gamma_0. In that case idparsopt must be c(2). The default is to fix all parameters not specified in idparsopt.

pars2

Vector of settings: pars2[1] sets the relative tolerance in the parameters pars2[2] sets the relative tolerance in the function pars2[3] sets the absolute tolerance in the parameters pars2[4] sets the maximum number of iterations

pchoice

sets which p-value to optimize (default 0) pchoice == 0 correspond to the sum of p_0f + p_1f pchoice == 1 correspond to p_0f pchoice == 2 correspond to p_1f

Value

mu

gives the maximum likelihood estimate of mu

gamma_0

gives the maximum likelihood estimate of gamma_0

gamma_1

gives the maximum likelihood estimate of gamma_1

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

Details

The output is a dataframe containing estimated parameters and maximum loglikelihood.

References

Pigot, A.L. & R.S. Etienne (2015). A new dynamic null model for phylogenetic community structure. Ecology Letters 18: 153-163.

See Also

DAMOCLES_loglik DAMOCLES_sim

Examples

# NOT RUN {
data(NWPrimates_data)
out = DAMOCLES_ML(
   phy = NWPrimates_data[[1]],
   pa = NWPrimates_data[[2]],
   initparsopt = c(0.01,1.8),
   idparsopt = c(1,2),
   parsfix = c(0),
   idparsfix = c(3),
   pars2 = c(1E-3,1E-4,1E-5,1000),
   pchoice = 0)
# }