Description Usage Arguments Details Value Author(s) References
Return the information criterion of the estimated model from a scp
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## S4 method for signature 'sssFit'
AIC(object, k, only.criterion)
## S4 method for signature 'sssFit'
BIC(object, only.criterion)
## S4 method for signature 'sssFit'
AICm(object, k, only.criterion)
## S4 method for signature 'sssFit'
AICc(object, k, only.criterion)
## S4 method for signature 'sssFit'
BICc(object, only.criterion)
## S4 method for signature 'sssFit'
BICj(object, k, tol, only.criterion)
## S4 method for signature 'sssFit'
GIC(object, k, only.criterion)
## S4 method for signature 'sssFit'
GIChq(object, k, only.criterion)
## S4 method for signature 'sssFit'
GICpn(object, only.criterion)
## S4 method for signature 'sssFit'
GICb(object, only.criterion)
|
object |
sssFit object from scp. |
k |
numeric. Factor multiplying the number of parameters in each criterion. Default to |
tol |
numeric. Value for the tolerance in some computation of inverse matrices. By default is set to |
only.criterion |
logical. If |
The information criterion for a mixed model is defined as
IC = -2\ell + penalty
where \ell is the log-likelihood \ell(\vartheta) or conditional log-likelihood \ell(\vartheta|r) (see scp). The penalty is expressed as k\times a_0\times ω_{μ_*,V} where ω_{μ_*,V} = ω_{μ_*} + ω_V is the (effective) number of parameters in the mean and variance and k and a_0 are factors that depend on the criterion used. Thus the information criterion can be written as
IC = -2\ell + k\times a_0\times ω_{μ_*,V}.
Note that μ_* depends on the criterion being used so it can be μ_* = μ_m or μ_* = μ. See scp.
If only.criterion=TRUE
returns the value of the criterion. If only.criterion=FALSE
returns a list with the following elements:
numeric. The log-likelihood or conditional log-likelihood (given r) of the model depending of the criterion used.
numeric. The value of the information criterion.
numeric. Factors ka_0 multiplying the number of parameters. Depends on the criterion selected.
numeric. The (effective) number of parameters. Depends on the criterion selected.
numeric. The value of the penalty.
Mario A. Martinez Araya, r@marioma.me
Mueller, Samuel; Scealy, J. L. and Welsh, A. H. (2013) Model Selection in Linear Mixed Models. Statist. Sci. 28, no. 2, 135–167. doi:10.1214/12-STS410. http://projecteuclid.org/euclid.ss/1369147909.
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