R/DAISIE_ML2.R

In xieshu95/Test-Trasie: Dynamical Assembly of Islands by Speciation, Immigration and Extinction

DAISIE_loglik_all_choosepar2 = function(
  trparsopt,
  trparsfix,
  idparsopt,
  idparsfix,
  idparsmat,
  pars2,
  datalist,
  methode,
  abstolint = 1E-16,
  reltolint = 1E-10
  )
{
   trpars1 = 0 * idparsmat
   trpars1[idparsopt] = trparsopt
   if(length(idparsfix) != 0)
   {
     for(i in 1:length(idparsfix))
     {
       trpars1[which(idparsmat == idparsfix[i])] = trparsfix[i]
     }
   }
   for(i in 1:length(idparsopt))
   {
     trpars1[which(idparsmat == idparsopt[i])] = trparsopt[i]
   }
   if(max(trpars1) > 1 || min(trpars1) < 0)
   {
      loglik = -Inf
   } else
   {
      pars1 = trpars1/(1 - trpars1)
      loglik = 0
      for(i in 1:length(datalist))
      {
        loglik = loglik + DAISIE_loglik_all(pars1 = pars1[i,],pars2 = pars2,datalist = datalist[[i]],methode = methode,abstolint = abstolint,reltolint = reltolint)
      }
   }
   if(is.nan(loglik) || is.na(loglik))
   {
      cat("There are parameter values used which cause numerical problems.\n")
      loglik = -Inf
   }
   return(loglik)
}

DAISIE_ML2 = function(
  datalist,
  initparsopt,
  idparsopt,
  parsfix,
  idparsfix,
  idparsmat,
  res = 100,
  ddmodel = 0,
  cond = 0,
  island_ontogeny = NA,
  tol = c(1E-4, 1E-5, 1E-7),
  maxiter = 1000 * round((1.25)^length(idparsopt)),
  methode = 'lsodes',
  optimmethod = 'subplex',
  verbose = 0,
  tolint = c(1E-16,1E-10)
  )
{
# datalist = list of all data: branching times, status of clade, and numnber of missing species
# datalist[[,]][1] = list of branching times (positive, from present to past)
# - max(brts) = age of the island
# - next largest brts = stem age / time of divergence from the mainland
# The interpretation of this depends on stac (see below)
# For stac = 0, this needs to be specified only once.
# For stac = 1, this is the time since divergence from the immigrant's sister on the mainland.
# The immigrant must have immigrated at some point since then.
# For stac = 2 and stac = 3, this is the time since divergence from the mainland.
# The immigrant that established the clade on the island must have immigrated precisely at this point.
# For stac = 3, it must have reimmigrated, but only after the first immigrant had undergone speciation.
# - min(brts) = most recent branching time (only for stac = 2, or stac = 3)
# datalist[[,]][2] = list of status of the clades formed by the immigrant
#  . stac == 0 : immigrant is not present and has not formed an extant clade
# Instead of a list of zeros, here a number must be given with the number of clades having stac = 0
#  . stac == 1 : immigrant is present but has not formed an extant clade
#  . stac == 2 : immigrant is not present but has formed an extant clade
#  . stac == 3 : immigrant is present and has formed an extant clade
#  . stac == 4 : immigrant is present but has not formed an extant clade, and it is known when it immigrated.
#  . stac == 5 : immigrant is not present and has not formed an extent clade, but only an endemic species
# datalist[[,]][3] = list with number of missing species in clades for stac = 2 and stac = 3;
# for stac = 0 and stac = 1, this number equals 0.
# idparsmat = matrix containing the ids of the parameters, linking them to initparsopt and parsfix.
# Per island system we use the following order
# - lac = (initial) cladogenesis rate
# - mu = extinction rate
# - K = maximum number of species possible in the clade
# - gam = (initial) immigration rate
# - laa = (initial) anagenesis rate
# Example: idparsmat = rbind(c(1,2,3,4,5),c(1,2,3,6,7)) has different rates of colonization and anagenesis for the two islands.    
# initparsopt, parsfix = values of optimized and fixed model parameters
# idparsopt, idparsfix = ids of optimized and fixed model parameters
# - res = pars2[1] = lx = length of ODE variable x
# - ddmodel = pars2[2] = diversity-dependent model,mode of diversity-dependence
#  . ddmodel == 0 : no diversity-dependence
#  . ddmodel == 1 : linear dependence in speciation rate (anagenesis and cladogenesis)
#  . ddmodel == 11 : linear dependence in speciation rate and immigration rate
#  . ddmodel == 3 : linear dependence in extinction rate
# - cond = conditioning; currently only cond = 0 is possible
#  . cond == 0 : no conditioning
#  . cond == 1 : conditioning on presence on the island

  options(warn=-1)
  out2err = data.frame(lambda_c = NA, mu = NA,K = NA, gamma = NA, lambda_a = NA, loglik = NA, df = NA, conv = NA)
  out2err = invisible(out2err)
  numisl = length(datalist)
  missnumspec = 0
  for(i in 1:numisl)
  { 
    missnumspec = missnumspec + sum(unlist(lapply(datalist[[i]],function(list) {list$missing_species})))
  }
  if(missnumspec > (res - 1))
  {
    cat("The number of missing species is too large relative to the resolution of the ODE.\n")
    return(out2err)
  } 
  if((sort(unique(as.vector(idparsmat))) != sort(c(idparsopt,idparsfix))) || (length(initparsopt) != length(idparsopt)) || (length(parsfix) != length(idparsfix)))
  {
    cat("The parameters to be optimized and/or fixed are incoherent.\n")
    return(out2err)
  }
  cat("Calculating the likelihood for the initial parameters.","\n")
  utils::flush.console()
  trparsopt = initparsopt/(1 + initparsopt)
  trparsopt[which(initparsopt == Inf)] = 1
  trparsfix = parsfix/(1 + parsfix)
  trparsfix[which(parsfix == Inf)] = 1
  pars2 = c(res,ddmodel,cond,0,island_ontogeny)
  optimpars = c(tol,maxiter)
  initloglik = DAISIE_loglik_all_choosepar2(trparsopt = trparsopt,trparsfix = trparsfix,idparsopt = idparsopt,idparsfix = idparsfix,idparsmat = idparsmat, pars2 = pars2,datalist = datalist,methode,abstolint = tolint[1],reltolint = tolint[2])
  cat("The loglikelihood for the initial parameter values is",initloglik,"\n")
  if(initloglik == -Inf)
  {
    cat("The initial parameter values have a likelihood that is equal to 0 or below machine precision. Try again with different initial values.\n")
    return(out2err)
  }
  cat("Optimizing the likelihood - this may take a while.","\n")
  utils::flush.console()
  out = DDD::optimizer(optimmethod = optimmethod,optimpars = optimpars,fun = DAISIE_loglik_all_choosepar2,trparsopt = trparsopt,idparsopt = idparsopt,trparsfix = trparsfix,idparsfix = idparsfix,idparsmat = idparsmat,pars2 = pars2,datalist = datalist,methode = methode,abstolint = tolint[1],reltolint = tolint[2])
  if(out$conv != 0)
  {
    cat("Optimization has not converged. Try again with different initial values.\n")
    out2 = out2err
    out2$conv = out$conv
    return(out2err)
  }
  MLtrpars = as.numeric(unlist(out$par))
  MLpars = MLtrpars/(1 - MLtrpars)
  ML = as.numeric(unlist(out$fvalues))
  MLpars1 = 0 * idparsmat
  if(length(idparsfix) != 0)
  {
    for(i in 1:length(idparsfix))
    {
      MLpars1[which(idparsmat == idparsfix[i])] = parsfix[i]
    }
  }
  for(i in 1:length(idparsopt))
  {
    MLpars1[which(idparsmat == idparsopt[i])] = MLpars[i]
  }
  for(i in 1:numisl)
  {
    if(MLpars1[i,3] > 10^7)
    {
      MLpars1[i,3] = Inf
    }
  }
  out2 = data.frame(lambda_c = MLpars1[,1], mu = MLpars1[,2], K = MLpars1[,3], gamma = MLpars1[,4], lambda_a = MLpars1[,5], loglik = ML, df = length(initparsopt), conv = unlist(out$conv))
  s1 = sprintf('Maximum likelihood parameter estimates: %f', MLpars1)
  s2 = sprintf('Maximum loglikelihood: %f',ML)
  cat("\n",s1,"\n",s2,"\n")
  return(invisible(out2))
}