| AIC.secr {secr} | R Documentation |
Terse report on the fit of one or more spatially explicit capture–recapture models. Models with smaller values of AIC (Akaike's Information Criterion) are preferred.
## S3 method for class 'secr': AIC(object, ..., sort = TRUE, k = 2, dmax = 10)
object |
secr object output from the function secr.fit |
... |
other secr objects |
sort |
logical for whether rows should be sorted by ascending AICc |
k |
numeric, the penalty per parameter to be used; always k = 2 in this method |
dmax |
numeric, the maximum AIC difference for inclusion in confidence set |
Models to be compared must have been fitted to the same data and use the same likelihood method (full vs conditional).
AIC with small sample adjustment is given by
AICc = -2log(L(theta-hat)) + 2K + 2K(K+1)/(n-K-1)
where K is the number of 'beta' parameters estimated. The sample size
n is the number of individuals observed at least once (i.e. the
number of rows in capthist).
Model weights are calculated as
w_i = exp(-dAICc_i / 2) / sum{ exp(-dAICc_i / 2) }
Models for which dAICc > dmax are given a weight of zero and are
excluded from the summation. Model weights may be used to form
model-averaged estimates of real or beta parameters with
model.average (see also Buckland et al. 1997, Burnham and
Anderson 2002).
The argument k is included for consistency with the generic method AIC.
A data frame with one row per model. By default, rows are sorted by ascending AICc.
model |
character string describing the fitted model |
detectfn |
shape of detection function fitted (halfnormal vs hazard-rate) |
npar |
number of parameters estimated |
logLik |
maximized log likelihood |
AIC |
Akaike's Information Criterion |
AICc |
AIC with small-sample adjustment of Hurvich & Tsai (1989) |
dAICc |
difference between AICc of this model and the one with smallest AICc |
AICwt |
AICc model weight |
The issue of goodness-of-fit and possible adjustment of AIC for overdispersion has yet to be addressed (cf QAIC in MARK).
Murray Efford murray.efford@otago.ac.nz
Buckland S. T., Burnham K. P. and Augustin, N. H. (1997) Model selection: an integral part of inference. Biometrics 53, 603–618.
Burnham, K. P. and Anderson, D. R. (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Second edition. New York: Springer-Verlag.
Hurvich, C. M. and Tsai, C. L. (1989) Regression and time series model selection in small samples. Biometrika 76, 297–307.
model.average, AIC, secr.fit, print.secr, score.test, LR.test, deviance.secr
## Compare two models fitted previously ## secrdemo.0 is a null model ## secrdemo.b has a learned trap response data(secrdemo) AIC(secrdemo.0, secrdemo.b)