R/DAISIE_ML1.R

Defines functions DAISIE_ML1 DAISIE_loglik_all_choosepar

Documented in DAISIE_loglik_all_choosepar DAISIE_ML1

DAISIE_loglik_all_choosepar = function(
  trparsopt,
  trparsfix,
  idparsopt,
  idparsfix,
  idparsnoshift,
  idparseq,
  pars2,
  datalist,
  methode
  )
{
   if(sum(idparsnoshift == (6:10)) != 5)
   {
       trpars1 = rep(0,10)
   } else {
       trpars1 = rep(0,5)
   }
   trpars1[idparsopt] = trparsopt
   if(length(idparsfix) != 0)
   {
      trpars1[idparsfix] = trparsfix
   }
   if(sum(idparsnoshift == (6:10)) != 5)
   {
      trpars1[idparsnoshift] = trpars1[idparsnoshift - 5]
   }
   if(max(trpars1) > 1 | min(trpars1) < 0)
   {
      loglik = -Inf
   } else {
      pars1 = trpars1/(1 - trpars1)
      if(pars2[5] > 0)
      {
         pars1 = DAISIE_eq(datalist,pars1,pars2)
         if(sum(idparsnoshift == (6:10)) != 5)
         {
             pars1[idparsnoshift] = pars1[idparsnoshift - 5]
         }
      }
      if(min(pars1) < 0)
      {
         loglik = -Inf
      } else {
         loglik = DAISIE_loglik_all(pars1,pars2,datalist,methode)
      }
      if(is.nan(loglik) || is.na(loglik))
      {
         cat("There are parameter values used which cause numerical problems.\n")
         loglik = -Inf
      }
   }
   return(loglik)
}

DAISIE_ML1 = function(
  datalist,
  initparsopt,
  idparsopt,
  parsfix,
  idparsfix,
  idparsnoshift = 6:10,
  res = 100,
  ddmodel = 0,
  cond = 0,
  eqmodel = 0,
  x_E = 0.95,
  x_I = 0.98,
  tol = c(1E-4, 1E-5, 1E-7),
  maxiter = 1000 * round((1.25)^length(idparsopt)),
  methode = "lsodes",
  optimmethod = 'subplex'
  )
{
# 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.
# initparsopt, parsfix = optimized and fixed model parameters
# - pars1[1] = lac = (initial) cladogenesis rate
# - pars1[2] = mu = extinction rate
# - pars1[3] = K = maximum number of species possible in the clade
# - pars1[4] = gam = (initial) immigration rate
# - pars1[5] = laa = (initial) anagenesis rate
# - pars1[6]...pars1[10] = same as pars1[1]...pars1[5], but for a second type of immigrant
# - pars1[11] = proportion of type 2 immigrants in species pool
# 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
# - eqmodel = equilibrium model
#  . eqmodel = 0 : no equilibrium is assumed
#  . eqmodel = 1 : equilibrium is assumed on deterministic equation for total number of species
#  . eqmodel = 2 : equilibrium is assumed on total number of species using deterministic equation for endemics and immigrants
#  . eqmodel = 3 : equilibrium is assumed on endemics using deterministic equation for endemics and immigrants
#  . eqmodel = 4 : equilibrium is assumed on immigrants using deterministic equation for endemics and immigrants
#  . eqmodel = 5 : equilibrium is assumed on endemics and immigrants using deterministic equation for endemics and immigrants

options(warn=-1)
idparseq = c()
if(eqmodel == 1 | eqmodel == 3 | eqmodel == 13)
{
   idparseq = 2
}
if(eqmodel == 2 | eqmodel == 4)
{
   idparseq = 4
}
if(eqmodel == 5 | eqmodel == 15)
{
   idparseq = c(2,4)
}

idpars = sort(c(idparsopt,idparsfix,idparsnoshift,idparseq))
#print(idpars)
missnumspec = unlist(lapply(datalist,function(list) {list$missing_species}))
if(sum(missnumspec) > (res - 1))
{
   cat("The number of missing species is too large relative to the resolution of the ODE.\n")
   out2 = data.frame(lambda_c = -1, mu = -1,K = -1, gamma = -1, lambda_a = -1, loglik = -1, df = -1, conv = -1)
} else {
  if((sum(idpars == (1:10)) != 10) || (length(initparsopt) != length(idparsopt)) || (length(parsfix) != length(idparsfix)))
  {
     cat("The parameters to be optimized and/or fixed are incoherent.\n")
     out2 = data.frame(lambda_c = -1, mu = -1,K = -1, gamma = -1, lambda_a = -1, loglik = -1, df = -1, conv = -1)
  } else {
    if(length(idparsopt) > 11)
    {
       cat("The number of parameters to be optimized is too high.\n")
       out2 = data.frame(lambda_c = -1, mu = -1,K = -1, gamma = -1, lambda_a = -1, loglik = -1, df = -1, conv = -1)
    } else {
      namepars = c("lambda_c","mu","K","gamma","lambda_a","lambda_c2","mu2","K2","gamma2","lambda_a2","prop_type2")
      if(length(namepars[idparsopt]) == 0) { optstr = "nothing" } else { optstr = namepars[idparsopt] }
      cat("You are optimizing",optstr,"\n")
      if(length(namepars[idparsfix]) == 0) { fixstr = "nothing" } else { fixstr = namepars[idparsfix] }
      cat("You are fixing",fixstr,"\n")
      if(sum(idparsnoshift == (6:10)) != 5)
      {
         noshiftstring = namepars[idparsnoshift]
         cat("You are not shifting",noshiftstring,"\n")
      }
      if(length(idparseq) == 0)
      {
         #cat("You are not assuming equilibrium\n")
      } else {
         if(ddmodel == 3)
         {
            cat("Equilibrium optimization is not implemented for ddmodel = 3\n")
         } else {
            cat("You are assuming equilibrium. Extinction and/or immigration will be considered a function of the other parameters, the species pool size, the number of endemics, and/or the number of non-endemics\n")
         }
      }
      cat("Calculating the likelihood for the initial parameters.","\n")
      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,eqmodel,tol,maxiter,x_E,x_I)
      optimpars = c(tol,maxiter)
      initloglik = DAISIE_loglik_all_choosepar(trparsopt = trparsopt,trparsfix = trparsfix,idparsopt = idparsopt,idparsfix = idparsfix,idparsnoshift = idparsnoshift,idparseq = idparseq, pars2 = pars2,datalist = datalist,methode)
      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")
         out2 = data.frame(lambda_c = -1, mu = -1,K = -1, gamma = -1, lambda_a = -1, loglik = -1, df = -1, conv = -1)
      } else {
        cat("Optimizing the likelihood - this may take a while.","\n")
        flush.console()
        out = DDD::optimizer(optimmethod = optimmethod,optimpars = optimpars,fun = DAISIE_loglik_all_choosepar,trparsopt = trparsopt,idparsopt = idparsopt,trparsfix = trparsfix,idparsfix = idparsfix,idparsnoshift = idparsnoshift,idparseq = idparseq,pars2 = pars2,datalist = datalist,methode = methode)        
        if(out$conv != 0)
        {
           cat("Optimization has not converged. Try again with different initial values.\n")
           out2 = data.frame(lambda_c = -1, mu = -1,K = -1, gamma = -1, lambda_a = -1, loglik = -1, df = -1, conv = -1)
        } else {
          MLtrpars = as.numeric(unlist(out$par))
          MLpars = MLtrpars/(1-MLtrpars)
          ML = as.numeric(unlist(out$fvalues))
          if(sum(idparsnoshift == (6:10)) != 5)
          {
              MLpars1 = rep(0,10)
          } else {
              MLpars1 = rep(0,5)
          }
          MLpars1[idparsopt] = MLpars
          if(length(idparsfix) != 0) { MLpars1[idparsfix] = parsfix }
          if(eqmodel > 0)
          {
              MLpars1 = DAISIE_eq(datalist,MLpars1,pars2)
          }
          if(MLpars1[3] > 10^7){ MLpars1[3] = Inf }
          if(sum(idparsnoshift == (6:10)) != 5)
          {
              if(length(idparsnoshift) != 0) { MLpars1[idparsnoshift] = MLpars1[idparsnoshift - 5] }
              if(MLpars1[8] > 10^7){ MLpars1[8] = Inf }
              out2 = data.frame(lambda_c = MLpars1[1], mu = MLpars1[2], K = MLpars1[3], gamma = MLpars1[4], lambda_a = MLpars1[5], lambda_c2 = MLpars1[6], mu2 = MLpars1[7], K2 = MLpars1[8], gamma2 = MLpars1[9], lambda_a2 = MLpars1[10], prop_type2 = MLpars1[11], loglik = ML, df = length(initparsopt), conv = unlist(out$conv))
              s1 = sprintf('Maximum likelihood parameter estimates: lambda_c: %f, mu: %f, K: %f, gamma: %f, lambda_a: %f, lambda_c2: %f, mu2: %f, K2: %f, gamma2: %f, lambda_a2: %f, prop_type2: %f',MLpars1[1],MLpars1[2],MLpars1[3],MLpars1[4],MLpars1[5],MLpars1[6],MLpars1[7],MLpars1[8],MLpars1[9],MLpars1[10],MLpars1[11])
          } else {
              out2 = data.frame(lambda_c = MLpars1[1], mu = MLpars1[2], K = MLpars1[3], gamma = MLpars1[4], lambda_a = MLpars1[5], loglik = ML, df = length(initparsopt), conv = unlist(out$conv))
              s1 = sprintf('Maximum likelihood parameter estimates: lambda_c: %f, mu: %f, K: %f, gamma: %f, lambda_a: %f',MLpars1[1],MLpars1[2],MLpars1[3],MLpars1[4],MLpars1[5])
          }
          s2 = sprintf('Maximum loglikelihood: %f',ML)
          cat("\n",s1,"\n",s2,"\n")
          if(eqmodel > 0)
          {
              M = calcMN(datalist,MLpars1)
              ExpEIN = DAISIE_ExpEIN(datalist[[1]]$island_age,MLpars1,M)
              cat("The expected number of endemics, non-endemics, and the total at these parameters is: ", ExpEIN[[1]], ExpEIN[[2]],ExpEIN[[3]])
          }
        }
      }
    }
  }
}
invisible(out2)
}

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DAISIE documentation built on May 29, 2017, 10:57 a.m.