AIC.dfunc: AIC.dfunc - AIC-related fit statistics for detection...

In Rdistance: Density and Abundance from Distance-Sampling Surveys

AIC.dfunc - AIC-related fit statistics for detection functions

Description

Computes AICc, AIC, or BIC for estimated distance functions.

Usage

## S3 method for class 'dfunc'
AIC(object, ..., criterion = "AICc")

Arguments

object	An Rdistance model frame or fitted distance function, normally produced by a call to dfuncEstim.
...	Included for compatibility with generic predict methods.
criterion	String specifying the criterion to compute. Either "AICc", "AIC", or "BIC".

Details

Regular Akaike's information criterion (https://en.wikipedia.org/wiki/Akaike_information_criterion) (AIC) is

AIC = LL + 2p,

where LL is the maximized value of the log likelihood (the minimized value of the negative log likelihood) and p is the number of coefficients estimated in the detection function. For dfunc objects, AIC = obj$loglik + 2*length(coef(obj)).

A correction for small sample size, AIC_c, is

AIC_c = LL + 2p + \frac{2p(p+1)}{n-p-1},

where n is sample size or number of detected groups for distance analyses. By default, this function computes AIC_c. AIC_c converges quickly to AIC as n increases.

The Bayesian Information Criterion (BIC) is

BIC = LL + log(n)p,

.

Value

A scalar, the requested fit statistic for object.

References

Burnham, K. P., and D. R. Anderson, 2002. Model selection and multi-model inference: A practical information-theoretic approach, Second ed. Springer-Verlag. ISBN 0-387-95364-7.

McQuarrie, A. D. R., and Tsai, C.-L., 1998. Regression and time series model selection. World Scientific. ISBN 981023242X

See Also

coef, dfuncEstim

Examples

data(sparrowDf)
dfunc <- sparrowDf |> dfuncEstim(dist~1)
  
# Fit statistics
AIC(dfunc)  # AICc
AIC(dfunc, criterion="AIC")  # AIC
AIC(dfunc, criterion="BIC")  # BIC

Rdistance documentation built on April 12, 2025, 1:12 a.m.