AIC {rAverage} | R Documentation |
Functions to extract or recalculate the Akaike Information Criterion and the Bayesian Information
Criterion of an averaging model fitted by the rav
function.
AIC(object, ..., k = 2)
BIC(object, ...)
object |
An object of class |
... |
Optionally more fitted model objects (see details). |
k |
Numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC. |
The functions AIC and BIC are used, respectively, to extract the Akaike Information Criterion and the
Bayesian Information Criterion of a model fitted by the function rav
.
AIC is calculated as:
AIC = n \ln \left( \frac{RSS}{n} \right) + k p
where n
is the number of data available, k
is the penalty per parameter ()usually equal to 2),
p
is the number of parameters and RSS
is the residual sum of squares.
BIC is calculated as:
BIC = n \ln \left( \frac{RSS}{n} \right) + \ln(n) p
As default, when n / p < 40
, AIC and BIC are corrected in AICc and BICc:
AICc = AIC + \frac{2 (p+1) p}{n-p-1}
BICc = BIC + \frac{\ln(n) (p+1) p}{n-p-1}
to avoid the correction, set correct = FALSE
. On the contrary, if you want the correction, set
correct = TRUE
. When the argument correct
is not specified, the rule n / p < 40
is
applied.
As default, the functions extract the indices of the (first) best model. The optional argument
whichModel
can be specified to extract the indices of another model. Options are:
"null"
: null model
"ESM"
: equal scale values model
"SAM"
: simple averaging model
"EAM"
: equal-weights averaging model
"DAM"
: differential-weight averaging model
"IC"
: information criteria model
A numeric value representing the information criterion of the selected model.
rav
,
rAverage-package
AIC
,
BIC
## Not run:
data(fmdata1)
fm1 <- rav(fmdata1, lev=c(3,3))
AIC(fm1)
BIC(fm1)
## End(Not run)