sie: Small Island Effect (SIE) via Breakpoint Regression

In sharx: Models and Data Sets for the Study of Species-Area Relationships

Description

Fit a breakpoint regression model to data to find threshold for the small island effect (SIE) as described in Lomolino...

Usage

sie(S, A, method = "Nelder-Mead", ...)
sieplot(x, add = FALSE, ...)

Arguments

S	untransformed species richness, vector.
A	untransformed area, vector.
x	a fitted model object of class 'sie'.
method	optimization method.
add	logical, if lines should be added to existing plot (TRUE), or a new plot is to be drawn (FALSE, default).
...	graphical arguments passed to plot.

Details

sie fits the breakpoint regression to the data, richness is log(S+0.5) transformed, area is log(A) transformed before analysis. There is a coef, summary, print method for fitted objects.

sieplot plots the observed (transformed) data and the fitted line.

Value

An S4 object of class 'sie' inheriting from class 'mle'.

Author(s)

Peter Solymos

References

Lomolino, M. V., and M. D. Weiser. 2001. Towards a more general species-area relationship: diversity on all islands, great and small. Journal of Biogeography, 28, 431–445.

Examples

data(sardata)
DAT <- sardata$islands[sardata$islands$study=="abbott1978plant",]
(x <- sie(DAT$S, DAT$A))
coef(x)
summary(x)
sieplot(x)

Example output

Loading required package: stats4
Loading required package: Formula
Loading required package: dcmle
Loading required package: dclone
Loading required package: coda
Loading required package: parallel
Loading required package: Matrix
dclone 2.3-0 	 2019-03-21
dcmle 0.3-1 	 2016-03-11

Attaching package: 'dcmle'

The following objects are masked from 'package:coda':

    chanames, crosscorr.plot, gelman.diag, gelman.plot, geweke.diag,
    heidel.diag, raftery.diag, varnames


Call:
sie(S = DAT$S, A = DAT$A)

Coefficients:
      logc          z          T 
 4.0307738  0.2422049 -0.6689568 
      logc          z          T 
 4.0307738  0.2422049 -0.6689568 
Maximum likelihood estimation

Call:
sie(S = DAT$S, A = DAT$A)

Coefficients:
       Estimate Std. Error
logc  4.0307738 0.12988186
z     0.2422049 0.04543904
T    -0.6689568 0.75653106

-2 log L: 10.94713 

sharx documentation built on May 2, 2019, 9:43 a.m.