# optimal.params.sloss

##### Estimation of neutral community parameters using a two-stage maximum-likelihood procedure

Function `optimal.params.sloss()`

returns maximum likelihood
estimates of `theta`

and `m(k)`

using numerical
optimization.

It differs from `untb`

's `optimal.params()`

function as it
applies to a network of smaller community samples `k`

instead of
to a single large community sample.

Although there is a single, common `theta`

for all communities,
immigration estimates are provided for each local community `k`

,
sharing a same biogeographical background.

- Keywords
- optimize

##### Usage

`optimal.params.sloss(D, nbres = 100, ci = FALSE, cint = c(0.025, 0.975))`

##### Arguments

- D
Species counts over a network of community samples (species by sample table)

- nbres
Number of resampling rounds for

`theta`

estimation- ci
Specifies whether bootstraps confidence intervals should be provided for estimates

- cint
Bounds of confidence intervals, if ci = T

##### Value

Mean `theta`

estimate

The vector of estimated immigration numbers `I(k)`

Confidence interval for `theta`

Confidence intervals for `m(k)`

theta estimates provided by the resampling procedure

Bootstrapped values of `I(k)`

Bootstrapped values of `m(k)`

##### Note

The function returns unhelpful output when run with the
`caruso`

dataset as in `optimal.params.sloss(caruso)`

. The
reason for this behaviour is unknown.

##### References

Francois Munoz, Pierre Couteron, B. R. Ramesh, and Rampal S. Etienne
2007. “Estimating parameters of neutral communities: from one
single large to several small samples”. *Ecology*
88(10):2482-2488

##### See Also

##### Examples

```
# NOT RUN {
data(ghats)
optimal.params.sloss(ghats)
# }
```

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