dbd {ape} | R Documentation

## Probability Density Under Birth–Death Models

### Description

These functions compute the probability density under some birth–death models, that is the probability of obtaining x species after a time t giving how speciation and extinction probabilities vary through time (these may be constant, or even equal to zero for extinction).

### Usage

    dyule(x, lambda = 0.1, t = 1, log = FALSE)
    dbd(x, lambda, mu, t, conditional = FALSE, log = FALSE)
    dbdTime(x, birth, death, t, conditional = FALSE,
            BIRTH = NULL, DEATH = NULL, fast = FALSE)

### Arguments

`x` | a numeric vector of species numbers (see Details).
`lambda` | a numerical value giving the probability of speciation; can be a vector with several values for `dyule`.
`mu` | id. for extinction.
`t` | id. for the time(s).
`log` | a logical value specifying whether the probabilities should be returned log-transformed; the default is `FALSE`.
`conditional` | a logical specifying whether the probabilities should be computed conditional under the assumption of no extinction after time `t`.
`birth, death` | a (vectorized) function specifying how the speciation or extinction probability changes through time (see `yule.time` and below).
`BIRTH, DEATH` | a (vectorized) function giving the primitive of `birth` or `death`.
`fast` | a logical value specifying whether to use faster integration (see `bd.time`).

### Details

These three functions compute the probabilities to observe `x` species starting from a single one after time `t` (assumed to be continuous). The first function is a short-cut for the second one with `mu = 0` and with default values for the two other arguments. `dbdTime` is for time-varying `lambda` and `mu` specified as R functions.

`dyule` is vectorized simultaneously on its three arguments `x`, `lambda`, and `t`, according to R's rules of recycling arguments. `dbd` is vectorized simultaneously `x` and `t` (to make likelihood calculations easy), and `dbdTime` is vectorized only on `x`; the other arguments are eventually shortened with a warning if necessary.

The returned value is, logically, zero for values of `x` out of range, i.e., negative or zero for `dyule` or if `conditional = TRUE`. However, it is not checked if the values of `x` are positive non-integers and the probabilities are computed and returned.

The details on the form of the arguments `birth`, `death`, `BIRTH`, `DEATH`, and `fast` can be found in the links below.

### Value

a numeric vector.

### Note

If you use these functions to calculate a likelihood function, it is strongly recommended to compute the log-likelihood with, for instance in the case of a Yule process, `sum(dyule( , log = TRUE))` (see examples).

### Author(s)

Emmanuel Paradis

### References

Kendall, D. G. (1948) On the generalized “birth-and-death” process. Annals of Mathematical Statistics, 19, 1–15.

### See Also

`bd.time`, `yule.time`

### Examples

    x <- 0:10
    plot(x, dyule(x), type = "h", main = "Density of the Yule process")
    text(7, 0.85, expression(list(lambda == 0.1, t == 1)))

    y <- dbd(x, 0.1, 0.05, 10)
    z <- dbd(x, 0.1, 0.05, 10, conditional = TRUE)
    d <- rbind(y, z)
    colnames(d) <- x
    barplot(d, beside = TRUE, ylab = "Density", xlab = "Number of species",
            legend = c("unconditional", "conditional on\nno extinction"),
            args.legend = list(bty = "n"))
    title("Density of the birth-death process")
    text(17, 0.4, expression(list(lambda == 0.1, mu == 0.05, t == 10)))

    ## Not run:
    ### generate 1000 values from a Yule process with lambda = 0.05
    x <- replicate(1e3, Ntip(rlineage(0.05, 0)))

    ### the correct way to calculate the log-likelihood...:
    sum(dyule(x, 0.05, 50, log = TRUE))

    ### ... and the wrong way:
    log(prod(dyule(x, 0.05, 50)))

    ### a third, less preferred, way:
    sum(log(dyule(x, 0.05, 50)))

    ## End(Not run)

[Package ape version 5.0 Index]