Sets up and executes a MiSSE model (Missing State Speciation and Extinction) on a phylogeny.
MiSSE(phy, f=1, turnover=c(1,2), eps=c(1,2), condition.on.survival=TRUE,
root.type="madfitz", root.p=NULL, sann=FALSE, sann.its=10000,
bounded.search=TRUE, max.tol=.Machine$double.eps^.50, starting.vals=NULL,
turnover.upper=10000, eps.upper=3, trans.upper=100, restart.obj=NULL,
ode.eps=0)a phylogenetic tree, in ape “phylo” format and
with internal nodes labeled denoting the ancestral selective regimes.
the estimated proportion of extant species included in the phylogeny. A value of 0.50 means that 50 percent of species are contained in the. By default all species are assumed to be sampled.
a numeric vector of length equal to the number of suspected rates in turnover. See 'Details'.
a numeric vector of length equal to the number of suspected rates in extinction fraction. See 'Details'.
a logical indicating whether the likelihood
should be conditioned on the survival of two lineages and the
speciation event subtending them (Nee et al. 1994). The default is TRUE.
indicates whether root summarization follow the procedure described by FitzJohn et al. 2009, “madfitz” or Herrera-Alsina et al. 2018, “herr_als”.
a vector indicating fixed root state probabilities. The
default is NULL.
a logical indicating whether a two-step optimization
procedure is to be used. The first includes a simulate annealing
approach, with the second involving a refinement using
subplex. The default is FALSE.
a numeric indicating the number of times the simulated annealing algorithm should call the objective function.
a logical indicating whether or not bounds should
be enforced during optimization. The default is TRUE.
supplies the relative optimization tolerance to
subplex.
a numeric vector of length 3 with starting values for the model. Position [1] sets turnover, [2] sets extinction fraction, and [3] transition rates between distinct diversification rates.
sets the upper bound for the turnover parameters.
sets the upper bound for the eps parameters.
sets the upper bound for the transition rate parameters.
an object of class that contains everything to restart an optimization.
sets the tolerance for the integration at the end of a branch. Essentially if the sum of compD is less than this tolerance, then it assumes the results are unstable and discards them. The default is set to zero, but in testing a value of 1e-8 can sometimes produce stable solutions for both easy and very difficult optimization problems.
MiSSE returns an object of class misse.fit. This is a list with
elements:
the maximum negative log-likelihood.
Akaike information criterion.
Akaike information criterion corrected for sample-size.
a matrix containing the maximum likelihood estimates of the model parameters.
an index matrix of the parameters being estimated.
user-supplied sampling frequencies.
a logical indicating whether hidden states were included in the model.
a logical indicating whether the likelihood was conditioned on the survival of two lineages and the speciation event subtending them.
indicates the user-specified root prior assumption.
indicates whether the user-specified fixed root probabilities.
user-supplied tree
relative optimization tolerance.
The starting values for the optimization.
the vector of upper limits to the optimization search.
the vector of lower limits to the optimization search.
The ode.eps value used for the estimation.
The turnover vector used.
The eps vector used.
One thing pointed out in the original HiSSE paper (Beaulieu & O'Meara, 2016) is that the trait-independent hisse model is basically a model for traits and a separate model for shifts in diversification parameters, much like BAMM (though without priors, discontinuous inheritance of extinction probability, or other mathematical foibles). The hidden states can drive different diversification processes, and the traits just evolve in a regular trait model. At that point, there is no harm in just dropping the trait (or analyzing separately) and just focusing on diversification driven by unknown factors. That is what this function does. It sets up and executes a completely trait-free version of a HiSSE model.
Thus, all that is required is a tree. The model allows up to 26 possible hidden states in diversification (denoted by A-Z). Transitions among hidden states are governed by a global transition rate, q. A "shift" in diversification denotes a lineage tracking some unobserved, hidden state. An interesting byproduct of this assumption is that distantly related clades can actually share the same discrete set of diversification parameters.
Note that "hidden state" is a shorthand. We do not mean that there is a single, discrete character that is solely driving diversification differences. There is some heritable "thing" that affects rates: but this could be a combination of body size, oxygen concentration, trophic level, and how many other species are competing in an area. This is true for HiSSE, but is especially important to grasp for MiSSE. It could be that there is some single discrete trait that drives everything; it's more likely that a whole range of factors play a role, and we just slice them up into discrete categories, the same way we slice up mammals into carnivore / omnivore / herbivore or plants into woody / herbaceous when the reality is more continuous.
As with hisse, we employ a modified optimization procedure. In other words, rather
than optimizing birth and death separately, MiSSE optimizes orthogonal
transformations of these variables: we let tau = birth+death define "net turnover", and
we let eps = death/birth define the “extinction fraction”. This reparameterization
alleviates problems associated with overfitting when birth and death are highly
correlated, but both matter in explaining the diversity pattern.
For the “root.type” option, we are currently maintaining the previous default of “madfitz”. However, it was recently pointed out by Herrera-Alsina et al. (2018) that at the root, the individual likelihoods for each possible state should be conditioned prior to averaging the individual likelihoods across states. This can be set doing “herr_als”. It is unclear to us which is exactly correct, but it does seem that both “madfitz” and “herr_als” behave exactly as they should in the case of character-independent diversification (i.e., reduces to likelihood of tree + likelihood of trait model). We've also tested the behavior and the likelihood differences are very subtle and the parameter estimates in simulation are nearly indistinguishable from the “madfitz” conditioning scheme. We provide both options and encourage users to try both and let us know conditions in which the result vary dramatically under the two root implementations. We suspect they do not.
Also, note, that in the case of “root.type=user” and “root.type=equal” are no longer explicit “root.type” options. Instead, either “madfitz” or “herr_als” are specified and the “root.p” can be set to allow for custom root options.
Beaulieu, J.M, and B.C. O'Meara. 2016. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65:583-601.
FitzJohn R.G., Maddison W.P., and Otto S.P. 2009. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Syst. Biol. 58:595-611.
Herrera-Alsina L., van Els P., and Etienne R.S. 2018. Detecting the dependence of diversification on multiples traits from phylogenetic trees and trait data. Systematic Biology In press.
Maddison W.P., Midford P.E., and Otto S.P. 2007. Estimating a binary characters effect on speciation and extinction. Syst. Biol. 56:701-710.
Nee S., May R.M., and Harvey P.H. 1994. The reconstructed evolutionary process. Philos. Trans. R. Soc. Lond. B Biol. Sci. 344:305-311.