calcAIC {stepNorm}R Documentation

Extract AIC from a Fitted Model


Computes the Akaike Information Criterion for a fitted parametric model.


calcAIC(fit, subset=TRUE, scale = 0, enp, = square)


fit fitted model; see details below
scale optional numeric specifying the scale parameter of the model; see scale in step.
subset A "logical" or "numeric" vector indicating the subset of points used to compute the fitted model.
enp equivalent number of parameters in the fitted model. If missing, the enp component from fit will be used. the loss function used to calculate deviance; default uses the squared deviations from the fitted values; one could also use, for example, absolute deviations (abs).


The argument fit can be an object of class marrayFit, in which case the residuals component from the marrayFit object will be extracted to calculate the deviance; the user can also pass in a numeric vector, in which case it will be interpreted as the residuals and the user needs to specify the argument enp.

The criterion used is

AIC = -2*log{L} + k * enp,

where L is the likelihood and enp the equivalent number of parameters of fit. For linear models (as in marrayFit), -2log{L} is computed from the deviance.

k = 2 corresponds to the traditional AIC and is the penalty for the number of parameters.


A numeric vector of length 4, giving

Dev the deviance of the fit.
enp the equivalent number of parameters of the fit.
penalty the penalty for number of parameters.
Criterion the Akaike Information Criterion for fit.


Yuanyuan Xiao,,
Jean Yee Hwa Yang,

See Also

AIC, deviance, calcBIC.


## load in swirl data

## fit a model
fit <- fitWithin(fun="medfit")
## res is an object of class marrayFit
res <- fit(swirl[,1])

## calculate AIC
## or could pass in the residual vector, but then argument "enp" needs to be specified
calcAIC(res$residual, enp=1) 

[Package stepNorm version 1.0.2 Index]