These functions are all methods
for class glmbb or summary.glmbb objects.
# S3 method for glmbb
summary(object, cutoff, …)# S3 method for summary.glmbb
print(x, digits = max(3, getOption("digits") - 3),
…)
an object of class "glmbb", usually, a result of a
call to glmbb.
a nonnegative real number. Only report on models having
criterion value no larger than the minimum value plus cutoff.
This argument may be omitted, in which case object$cutoff is
used.
an object of class "summary.glmbb", usually, a result of a
call to summary.glmbb.
the number of significant digits to use when printing.
not used. Required by their generics.
summary.glmbb returns an object of class "summary.glmbb", a
list with components
a data frame having variables
criterionthe vector criterion described
in the Details section, in sorted order.
weightthe corresponding Akaike weights.
formulathe corresponding formulas describing the corresponding models.
the cutoff argument to the call to glmbb
that produced object.
the cutoff argument to the call to
summary.glmbb.
a character variable giving the name of the criterion
(AIC, BIC, or AICc). Not to be confused with results$criterion.
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
w <- exp(- criterion / 2) weight <- w / sum(w)
except that it is calculated differently to avoid overflow. These are so-called 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).
Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. Springer-Verlag, New York.
# NOT RUN {
## For examples see those in help(glmbb)
# }
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