calcBIC {stepNorm}R Documentation

Extract BIC from a Fitted Model


Computes the Bayesian Information Criterion for a fitted parametric model.


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


fit fitted model; see details below
subset A "logical" or "numeric" vector indicating the subset of points used to compute the fitted model.
scale optional numeric specifying the scale parameter of the model; see scale in step.
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; the default uses the squared deviation from the fitted values; one could also use abosulate 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

BIC = -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 = log(n) corresponds to the BIC 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, calcAIC.


## load in swirl data

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

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

[Package stepNorm version 1.0.2 Index]