Time-Dependent Birth-Death Models
This function fits a used-defined time-dependent birth-death model.
bd.time(phy, birth, death, BIRTH = NULL, DEATH = NULL, ip, lower, upper, fast = FALSE, boot = 0, trace = 0)
- an object of class
- either a numeric (if speciation rate is assumed constant), or a (vectorized) function specifying how the birth (speciation) probability changes through time (see details).
- id. for extinction probability.
- (optional) a vectorized function giving the primitive
- id. for
- a numeric vector used as initial values for the estimation procedure. If missing, these values are guessed.
- lower, upper
- the lower and upper bounds of the parameters. If missing, these values are guessed too.
- a logical value specifying whether to use faster integration (see details).
- the number of bootstrap replicates to assess the confidence intervals of the parameters. Not run by default.
- an integer value. If non-zero, the fitting procedure is
tracesteps. This can be helpful if convergence is particularly slow.
Details on how to specify the birth and death functions and their
primitives can be found in the help page of
The model is fitted by minimizing the least squares deviation between
the observed and predicted distributions of branching times. These
computations rely heavily on numerical integrations. If
FALSE, integrations are done with R's
fast = TRUE, a faster but less accurate function
fast = FALSE.
- A list with the following components:
SS the minimized sum of squares. convergence output convergence criterion from
message id. iterations id. evaluations id.
Paradis, E. (2011) Time-dependent speciation and extinction from phylogenies: a least squares approach. Evolution, 65, 661--672.
set.seed(3) tr <- rbdtree(0.1, 0.02) bd.time(tr, 0, 0) # fits a simple BD model bd.time(tr, 0, 0, ip = c(.1, .01)) # 'ip' is useful here ## the classic logistic: birth.logis <- function(a, b) 1/(1 + exp(-a*t - b)) bd.time(tr, birth.logis, 0, ip = c(0, -2, 0.01)) ## slow to get: ## $par ## a b death ## -0.003486961 -1.995983179 0.016496454 ## ## $SS ##  20.73023