optimal.params.sloss

0th

Percentile

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

See Also

optimal.params, optimal.params.gst

Aliases
  • optimal.params.sloss
Examples
data(ghats)
optimal.params.sloss(ghats)
Documentation reproduced from package untb, version 1.6-5, License: GPL

Community examples

Looks like there are no examples yet.