untb (version 1.7-2)

optimal.params.sloss: Estimation of neutral community parameters using a two-stage maximum-likelihood procedure

Description

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.

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

optimal.params, optimal.params.gst

Examples

Run this code
data(ghats)
optimal.params.sloss(ghats)

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