# etienne

##### Etienne's sampling formula

Function `etienne()`

returns the probability of a given dataset
given `theta`

and `m`

according to the Etienne's sampling
formula. Function `optimal.params()`

returns the maximum likelihood
estimates for `theta`

and `m`

using numerical optimization

- Keywords
- math

##### Usage

```
etienne(theta, m, D, log.kda = NULL, give.log = TRUE, give.like = TRUE)
optimal.params(D, log.kda = NULL, start = NULL, give = FALSE, ...)
```

##### Arguments

- theta
Fundamental biodiversity parameter

- m
Immigration probability

- D
Dataset; a count object

- log.kda
The KDA as defined in equation A11 of Etienne 2005. See details section

- give.log
Boolean, with default

`TRUE`

meaning to return the logarithm of the value- give.like
Boolean, with default

`TRUE`

meaning to return the likelihood and`FALSE`

meaning to return the probability- start
In function

`optimal.params()`

, the start point for the optimization routine \((\theta,m)\).- give
In function

`optimal.params()`

, Boolean, with`TRUE`

meaning to return all output of the optimization routine, and default`FALSE`

meaning to return just the point estimate- ...
In function

`optimal.params()`

, further arguments passed to`optim()`

##### Details

Function `etienne()`

is just Etienne's formula 6:
$$P[D|\theta,m,J]=
\frac{J!}{\prod_{i=1}^Sn_i\prod_{j=1}^J{\Phi_j}!}
\frac{\theta^S}{(\theta)_J}\times
\sum_{A=S}^J\left(K(D,A)
\frac{(\theta)_J}{(\theta)_A}
\frac{I^A}{(I)_J}
\right)$$

where \(\log K(D,A)\) is given by function `logkda()`

(qv). It
might be useful to know the (trivial) identity for the Pochhammer symbol
[written \((z)_n\)] documented in `theta.prob.Rd`

. For
convenience, Etienne's Function `optimal.params()`

uses
`optim()`

to return the maximum likelihood estimate for
\(\theta\) and \(m\).

Compare function `optimal.theta()`

, which is restricted to no
dispersal limitation, ie \(m=1\).

Argument `log.kda`

is optional: this is the \(K(D,A)\) as defined
in equation A11 of Etienne 2005; it is computationally expensive to
calculate. If it is supplied, the functions documented here will not
have to calculate it from scratch: this can save a considerable amount
of time

##### References

R. S. Etienne 2005. “A new sampling formula for
biodiversity”. *Ecology letters* 8:253-260

##### See Also

##### Examples

```
# NOT RUN {
data(butterflies)
# }
# NOT RUN {
optimal.params(butterflies)
# }
# NOT RUN {
#takes too long without PARI/GP
#Now the one from Etienne 2005, supplementary online info:
zoo <- count(c(pigs=1, dogs=1, cats=2, frogs=3, bats=5, slugs=8))
l <- logkda.R(zoo, use.brob=TRUE) # Use logkda() if pari/gp is available
optimal.params(zoo, log.kda=l) #compare his answer of 7.047958 and 0.22635923.
# }
```

*Documentation reproduced from package untb, version 1.7-4, License: GPL*