Description Usage Arguments Details Value Methods Note Examples
Various functions for likelihood-based and information-theoretic model selection of likelihood models
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object |
A |
... |
An optional list of additional |
nobs |
Number of observations (sometimes obtainable as an attribute of the fit or of the log-likelihood) |
k |
penalty parameter (nearly always left at its default value of 2) |
Further arguments to BIC
can be specified
in the ...
list: delta
(logical)
specifies whether to include a column for delta-BIC
in the output.
A table of the BIC values, degrees of freedom, and possibly delta-BIC values relative to the minimum-BIC model
signature(object = "mle2")
: Extract maximized
log-likelihood.
signature(object = "mle2")
: Calculate
Akaike Information Criterion
signature(object = "mle2")
: Calculate
small-sample corrected Akaike Information Criterion
signature(object="mle2")
: Likelihood Ratio Test
comparision of different models
This is implemented in an ugly way and could probably be improved!
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