dd_LR: Bootstrap likelihood ratio test of diversity-dependent...

In DDD: Diversity-Dependent Diversification

Bootstrap likelihood ratio test of diversity-dependent diversification model

Description

This function computes the maximum likelihood and the associated estimates of the parameters of a diversity-dependent diversification model for a given set of phylogenetic branching times. It then performs a bootstrap likelihood ratio test of the diversity-dependent (DD) model against the constant-rates (CR) birth-death model. Finally, it computes the power of this test.

Usage

dd_LR(
  brts,
  initparsoptDD,
  initparsoptCR,
  missnumspec,
  outputfilename = NULL,
  seed = 42,
  endmc = 1000,
  alpha = 0.05,
  plotit = TRUE,
  res = 10 * (1 + length(brts) + missnumspec),
  ddmodel = 1,
  cond = 1,
  btorph = 1,
  soc = 2,
  tol = c(0.001, 1e-04, 1e-06),
  maxiter = 2000,
  changeloglikifnoconv = FALSE,
  optimmethod = "subplex",
  methode = "analytical"
)

Arguments

brts	A set of branching times of a phylogeny, all positive
initparsoptDD	The initial values of the parameters that must be optimized for the diversity-dependent (DD) model: lambda_0, mu and K
initparsoptCR	The initial values of the parameters that must be optimized for the constant-rates (CR) model: lambda and mu
missnumspec	The number of species that are in the clade but missing in the phylogeny
outputfilename	The name (and location) of the file where the output will be saved. Default is no save.
seed	The seed for the pseudo random number generator for simulating the bootstrap data
endmc	The number of bootstraps
alpha	The significance level of the test
plotit	Boolean to plot results or not
res	Sets the maximum number of species for which a probability must be computed, must be larger than 1 + length(brts)
ddmodel	Sets the model of diversity-dependence: ddmodel == 1 : linear dependence in speciation rate with parameter K (= diversity where speciation = extinction) ddmodel == 1.3 : linear dependence in speciation rate with parameter K' (= diversity where speciation = 0) ddmodel == 2 : exponential dependence in speciation rate with parameter K (= diversity where speciation = extinction) ddmodel == 2.1 : variant of exponential dependence in speciation rate with offset at infinity ddmodel == 2.2 : 1/n dependence in speciation rate ddmodel == 2.3 : exponential dependence in speciation rate with parameter x (= exponent) ddmodel == 3 : linear dependence in extinction rate ddmodel == 4 : exponential dependence in extinction rate ddmodel == 4.1 : variant of exponential dependence in extinction rate with offset at infinity ddmodel == 4.2 : 1/n dependence in extinction rate with offset at infinity ddmodel == 5 : linear dependence in speciation and extinction rate
cond	Conditioning: cond == 0 : conditioning on stem or crown age cond == 1 : conditioning on stem or crown age and non-extinction of the phylogeny cond == 2 : conditioning on stem or crown age and on the total number of extant taxa (including missing species) cond == 3 : conditioning on the total number of extant taxa (including missing species) Note: cond == 3 assumes a uniform prior on stem age, as is the standard in constant-rate birth-death models, see e.g. D. Aldous & L. Popovic 2004. Adv. Appl. Prob. 37: 1094-1115 and T. Stadler 2009. J. Theor. Biol. 261: 58-66.
btorph	Sets whether the likelihood is for the branching times (0) or the phylogeny (1)
soc	Sets whether stem or crown age should be used (1 or 2)
tol	Sets the tolerances in the optimization. Consists of: reltolx = relative tolerance of parameter values in optimization reltolf = relative tolerance of function value in optimization abstolx = absolute tolerance of parameter values in optimization
maxiter	Sets the maximum number of iterations in the optimization
changeloglikifnoconv	if TRUE the loglik will be set to -Inf if ML does not converge
optimmethod	Method used in optimization of the likelihood. Current default is 'subplex'. Alternative is 'simplex' (default of previous versions)
methode	The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1.

Details

The output is a list with 3 elements:

Value

treeCR	a list of trees generated under the constant-rates model using the ML parameters under the CR model
treeDD	a list of trees generated under the diversity-dependent model using the ML parameters under the diversity-dependent model
out	a dataframe with the parameter estimates and maximum likelihoods for diversity-dependent and constant-rates models $model - the model used to generate the data. 0 = unknown (for real data), 1 = CR, 2 = DD $mc - the simulation number for each model $lambda_CR - speciation rate estimated under CR $mu_CR - extinction rate estimated under CR $LL_CR - maximum likelihood estimated under CR $conv_CR - convergence code for likelihood optimization; conv = 0 means convergence $lambda_DD1 - initial speciation rate estimated under DD for first set of initial values $mu_DD1 - extinction rate estimated under DD for first set of initial values $K_DD1 - clade-wide carrying-capacity estimated under DD for first set of initial values $LL_DD1 - maximum likelihood estimated under DD for first set of initial values $conv_DD1 - convergence code for likelihood optimization for first set of initial values; conv = 0 means convergence $lambda_DD2 - initial speciation rate estimated under DD for second set of initial values $mu_DD2 - extinction rate estimated under DD for second set of initial values $K_DD2 - clade-wide carrying-capacity estimated under DD for second set of initial values $LL_DD2 - maximum likelihood estimated under DD for second set of initial values $conv_DD2 - convergence code for likelihood optimization for second set of initial values; conv = 0 means convergence $LR - likelihood ratio between DD and CR
pvalue	p-value of the test
LRalpha	Likelihood ratio at the signifiance level alpha
poweroftest	power of the test for significance level alpha

Author(s)

Rampal S. Etienne & Bart Haegeman

References

- Etienne, R.S. et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, doi: 10.1111/2041-210X.12565
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat. 180: E75-E89, doi: 10.1086/667574

See Also

dd_loglik, dd_ML

DDD documentation built on July 26, 2023, 5:25 p.m.