summary.glmbb: Summarize GLM Model Selection via Branch and Bound
In glmbb: All Hierarchical or Graphical Models for Generalized Linear Model

Description Usage Arguments Details Value References Examples

View source: R/glmbb.R

These functions are all methods for class glmbb or summary.glmbb objects.

## S3 method for class 'glmbb'
summary(object, cutoff, ...)

## S3 method for class 'summary.glmbb'
print(x, digits = max(3, getOption("digits") - 3),
    ...)

`object`	an object of class `"glmbb"`, usually, a result of a call to `glmbb`.
`cutoff`	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.
`x`	an object of class `"summary.glmbb"`, usually, a result of a call to `summary.glmbb`.
`digits`	the number of significant digits to use when printing.
`...`	not used. Required by their generics.

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	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).

summary.glmbb returns an object of class "summary.glmbb", a list with components

`results`	a data frame having variables `criterion` the vector `criterion` described in the Details section, in sorted order. `weight` the corresponding Akaike weights. `formula` the corresponding formulas describing the corresponding models.
`cutoff.search`	the `cutoff` argument to the call to `glmbb` that produced `object`.
`cutoff.summary`	the `cutoff` argument to the call to `summary.glmbb`.
`criterion`	a character variable giving the name of the criterion (AIC, BIC, or AICc). Not to be confused with `results$criterion`.

Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. Springer-Verlag, New York.