AIC.dfunc | R Documentation |
Computes AICc, AIC, or BIC for estimated distance functions.
## S3 method for class 'dfunc'
AIC(object, ..., criterion = "AICc")
object |
An estimated detection function object. An estimated detection
function object has class 'dfunc', and is usually produced by a call to
|
... |
Required for compatibility with the general |
criterion |
String specifying the criterion to compute. Either "AICc", "AIC", or "BIC". |
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,
.
A scalar. By default, the value of AICc for the
estimated distance function obj
.
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
coef
, dfuncEstim
data(sparrowDetectionData)
dfunc <- dfuncEstim(dist~1,
detectionData=sparrowDetectionData,
w.hi=units::set_units(150, "m"))
# Compute fit statistics
AIC(dfunc) # AICc
AIC(dfunc, criterion="AIC") # AIC
AIC(dfunc, criterion="BIC") # BIC
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