hzar.AIC.default: Calculate the AIC score.

In hzar: Hybrid Zone Analysis using R

Description

Calculate the AIC or the corrected AIC (AICc) for the given likelihood, number of parameters and number of observations.

Extracts the parameters as needed when passed the correct hzar object.

Usage

hzar.AIC.default(maxLL, param.count)
hzar.AICc.default(maxLL, param.count, nObs)
hzar.AIC.hzar.cline(cline)
hzar.AICc.hzar.cline(cline,nObs)
hzar.AIC.hzar.dataGroup(dataGroup)
hzar.AICc.hzar.dataGroup(dataGroup)

Arguments

maxLL	The maximum log likelihood value.
param.count	The number of free parameters, also known as the number of degrees of freedom.
nObs	The number of samples observed.
cline	A hzar.cline object.
dataGroup	A hzar.dataGroup object.

Details

The formula for AIC used is 2 * (param.count - maxLL).

The formula for AICc used is: AIC + 2 * param.count * (param.count + 1) / (nObs - param.count - 1)

Value

The AIC or AICc score calculated.

Author(s)

Graham Derryberry [e-mail]

See Also

AIC hzar.AIC.hzar.obsDataGroup

Examples

print(hzar.AIC.default(-8,3))
print(hzar.AICc.default(-8,3,30))

data(manakinMolecular);
mknAdaA <-
  hzar.doMolecularData1DPops(manakinMolecular$distance,
                             manakinMolecular$ada.A,
                             manakinMolecular$ada.nSamples);
hzar.plot.obsData(mknAdaA);
mknAdaAmodel <-
  hzar.makeCline1DFreq(mknAdaA, scaling="fixed",tails="none");
mknAdaAmodel <-
  hzar.model.addBoxReq(mknAdaAmodel,-30,600);
mknAdaAmodelFitR <-
   hzar.first.fitRequest.old.ML(model=mknAdaAmodel ,
                                mknAdaA,
                                verbose=FALSE);
print(hzar.AIC.hzar.dataGroup(hzar.fit2DataGroup(mknAdaAmodelFitR)))

mknAdaAcline <- hzar.gen.cline(list(center=300,width=10),
                               mknAdaAmodelFitR);

print(hzar.AIC.hzar.cline(mknAdaAcline));

hzar documentation built on May 2, 2019, 7 a.m.