calcBIC {stepNorm} | R Documentation |
Computes the Bayesian Information Criterion for a fitted parametric model.
calcBIC(fit, subset=TRUE, scale = 0, enp, loss.fun = 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. |
loss.fun |
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, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu
## 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 BIC calcBIC(res) ## or could pass in the residual vector, but then argument "enp" needs to be specified calcBIC(res$residual, enp=1)