MASSExtra: step_AIC – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

step_AIC

Stepwise model construction and inspection

Description

Front-ends to stepAIC and dropterm with changed defaults. step_BIC implements a stepwise selection with BIC as the criterion and step_GIC uses an experimental criterion with a penalty midway between AIC and BIC: the "Goldilocks" criterion.

Usage

step_AIC(object, ..., trace = 0, k = 2)

step_BIC(object, ..., trace = 0, k = max(2, log(nobs(object))))

step_GIC(object, ..., trace = 0, k = (2 + log(nobs(object)))/2)

drop_term(
  object,
  ...,
  test = default_test(object),
  k,
  sorted = TRUE,
  decreasing = TRUE,
  delta = TRUE
)

add_term(
  object,
  ...,
  test = default_test(object),
  k,
  sorted = TRUE,
  decreasing = TRUE,
  delta = TRUE
)

Arguments

`object`	as for `stepAIC`
`...`	additional arguments passed on to main function in `MASS`
`trace, k`	as for `stepAIC`
`sorted, test`	as for `dropterm` and `addterm`
`decreasing`	in `drop_term` should the rows be displayed in decreasing order, that is best to worst terms, from that of `dropterm`?
`delta`	Should the criterion be displayed (FALSE) or the change in the in the criterion relative to the present model (TRUE)?

Value

A fitted model object after stepwise refinement, or a data frame with extra class membership for single term functions.

Examples

fm <- glm.nb(Days ~ .^3, quine)
drop_term(fm_aic <- step_AIC(fm))
drop_term(fm_bic <- step_BIC(fm))

MASSExtra

Some 'MASS' Enhancements

v1.1.2

GPL-2 | GPL-3

Authors

Bill Venables

Initial release