calcAIC: Extract AIC from a Fitted Model
In stepNorm: Stepwise normalization functions for cDNA microarrays

Description Usage Arguments Details Value Author(s) See Also Examples

Computes the Akaike Information Criterion for a fitted parametric model.

1	calcAIC(fit, subset=TRUE, scale = 0, enp, loss.fun = 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.
`loss.fun`	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, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu

AIC, deviance, calcBIC.

## load in swirl data
data(swirl)

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

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

Loading required package: marray
Loading required package: limma
      Dev       enp   penalty Criterion 
 -10147.9       1.0       2.0  -10145.9 
      Dev       enp   penalty Criterion 
 -10147.9       1.0       2.0  -10145.9