optimal.params.sloss
From untb v1.6-5
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
estimateI 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.
See Also
Examples
data(ghats)
optimal.params.sloss(ghats)
Community examples
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