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

thetaMean theta estimate
IThe vector of estimated immigration numbers I(k)
Output of the bootstrap procedure, if ci = T:
thetaciConfidence interval for theta
msampleciConfidence intervals for m(k)
thetasamptheta estimates provided by the resampling procedure
IbootBootstrapped values of I(k)
mbootBootstrapped 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

Examples

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

Documentation reproduced from package untb, version 1.6-5, License: GPL

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