# dbd

##### Probability Density Under Birth--Death Models

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).

- Keywords
- utilities

##### 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).

##### References

Kendall, D. G. (1948) On the generalized ``birth-and-death''
process. *Annals of Mathematical Statistics*, **19**, 1--15.

##### See Also

##### Examples

```
# NOT RUN {
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)))
# }
```

*Documentation reproduced from package ape, version 4.1, License: GPL (>= 2)*