getAICc: Calculate AICc

In BioGeoBEARS: BioGeography with Bayesian (and Likelihood) Evolutionary Analysis in R Scripts

Description

Calculate AICc (Akaike Information Criterion). Lower values of AICc indicate some combination of better fit to the data and more parsimony in the model (fewer free parameters).

Usage

  getAICc(LnL, numparams, samplesize)

Arguments

LnL	The log-likelihood (typically negative, but may not be for continuous data).
numparams	The number of parameters for each model.
samplesize	The number of data on which the model conferred likelihood.

Details

See Burnham et al. (2002) and http://www.brianomeara.info/tutorials/AICc for discussion of AICc and its uses.

Value

AICcval A vector of AICc results.

Note

Go BEARS!

Author(s)

Nicholas J. Matzke [e-mail]

References

http://phylo.wikidot.com/matzke-2013-international-biogeography-society-poster http://www.brianomeara.info/tutorials/AICc

Burnham_Anderson_2002

Matzke_2012_IBS

See Also

calc_AICc_column, calc_AICc_column

Examples

LnL = -34.5
numparams = 2
samplesize = 20
getAICc(LnL, numparams, samplesize)

LnL = -20.9
numparams = 3
samplesize = 20
getAICc(LnL, numparams, samplesize)

LnL = -34.5
numparams = 2
samplesize = 5
getAICc(LnL, numparams, samplesize)

LnL = -20.9
numparams = 3
samplesize = 5
getAICc(LnL, numparams, samplesize)

Example output

Loading required package: rexpokit
Loading required package: cladoRcpp
Loading required package: ape
Loading required package: phylobase

Attaching package: 'phylobase'

The following object is masked from 'package:ape':

    edges

[1] 73.70588
[1] 49.3
[1] 79
[1] 71.8

BioGeoBEARS documentation built on May 29, 2017, 8:36 p.m.