Analysis of Diversification with Survival Models
This functions fits survival models to a set of branching times, some of them may be known approximately (censored). Three models are fitted, Model A assuming constant diversification, Model B assuming that diversification follows a Weibull law, and Model C assuming that diversification changes with a breakpoint at time `Tc'. The models are fitted by maximum likelihood.
diversi.time(x, census = NULL, censoring.codes = c(1, 0), Tc = NULL)
- a numeric vector with the branching times.
- a vector of the same length than `x' used as an indicator variable; thus, it must have only two values, one coding for accurately known branching times, and the other for censored branching times. This argument can be of any mode (numeric, cha
- a vector of length two giving the codes used
census: by default 1 (accurately known times) and 0 (censored times). The mode must be the same than the one of
- a single numeric value specifying the break-point time to fit Model C. If none is provided, then it is set arbitrarily to the mean of the analysed branching times.
The principle of the method is to consider each branching time as an event: if the branching time is accurately known, then it is a failure event; if it is approximately knwon then it is a censoring event. An analogy is thus made between the failure (or hazard) rate estimated by the survival models and the diversification rate of the lineage. Time is here considered from present to past.
Model B assumes a monotonically changing diversification rate. The parameter that controls the change of this rate is called beta. If beta is greater than one, then the diversification rate decreases through time; if it is lesser than one, the the rate increases through time. If beta is equal to one, then Model B reduces to Model A.
- A NULL value is returned, the results are simply printed.
Paradis, E. (1997) Assessing temporal variations in diversification rates from phylogenies: estimation and hypothesis testing. Proceedings of the Royal Society of London. Series B. Biological Sciences, 264, 1141--1147.