Summarize GLM Model Selection via Branch and Bound
Description
These functions are all methods
for class glmbb
or summary.glmbb
objects.
Usage
1 2 3 4 5 6 
Arguments
object 
an object of class 
cutoff 
a nonnegative real number. Only report on models having
criterion value no larger than the minimum value plus 
x 
an object of class 
digits 
the number of significant digits to use when printing. 
... 
not used. Required by their generics. 
Details
Let criterion
denote the vector of criterion (AIC, BIC, or AICc)
values for all of the models evaluated in the search. Those with
criterion value greater than min(criterion) + cutoff
are tossed.
We also define a vector weight
by
1 2 
except that it is calculated differently to avoid overflow. These are socalled Akaike weights. They may or may not provide some guide as to how to deal with these models. For more see Burnham and Anderson (2002).
Value
summary.glmbb
returns an object of class "summary.glmbb"
, a
list with components
results 
a data frame having variables

cutoff.search 
the 
cutoff.summary 
the 
criterion 
a character variable giving the name of the criterion
(AIC, BIC, or AICc). Not to be confused with 
References
Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical InformationTheoretic Approach, 2nd ed. SpringerVerlag, New York.
Examples
1  ## For examples see those in help(glmbb)
