untb (version 1.6-2)

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

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