# optimal.params.sloss

From untb v1.7-2
by Robin K S Hankin

##### 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

- theta
- Mean
`theta`

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

Output of the bootstrap procedure, if ci = T:
- thetaci
- Confidence interval for
`theta`

- msampleci
- Confidence intervals for
`m(k)`

- thetasamp
- theta estimates provided by the resampling procedure
- Iboot
- Bootstrapped values of
`I(k)`

- mboot
- Bootstrapped values of
`m(k)`

##### 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

```
data(ghats)
optimal.params.sloss(ghats)
```

*Documentation reproduced from package untb, version 1.7-2, License: GPL*

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