BIC(object, ...)
## S3 method for class 'coxph':
BIC(object, ...)
AIC(object, ..., k = 2)
## S3 method for class 'coxph':
AIC(object, ..., k = 2)
AICc(object, ...)
## S3 method for class 'coxph':
AICc(object, ..., k = 2)
Arguments
object
An object of class coxph
...
Not implemented
k
The weight of the equivalent degrees of freedom
(edf) of the AIC formula
Value
A named vector with [object Object],[object Object]
Details
Given a set of candidate models for the same data, the
preferred model is the one with the minimum IC value.
The Akaike information criterion, AIC, is given by
$$AIC = k.edf -2 \ln L$$ Where
$edf$ is the equivalent degrees of freedom (i.e.,
equivalent to the number of free parameters in the model)
and $L$ is the model likelihood.
$k$ is a
constant, which is $=2$ for the traditional AIC.
AIC corrected for finite sample size $n$, AICc,
is $$AICc = AIC + \frac{k.edf(edf+1)}{n-edf-1}$$ where $n$ is the
sample size. Thus there is a greater penalty for more
parameters.
The Bayesian information criterion is
$$BIC = \ln n.edf -2 \ln L$$ This penalises models with more parameters to a
greater extent.