Builds a GAM model in a step-wise fashion. For each "term"
there is an ordered list of alternatives, and the function traverses
these in a greedy fashion. Note: this is NOT a method for `step`

,
which used to be a generic, so must be invoked with the full name.

1 |

`object` |
An object of class |

`scope` |
defines the range of models examined in the step-wise search. It is a list of formulas, with each formula corresponding to a term in the model. Each of these formulas specifies a "regimen" of candidate forms in which the particular term may enter the model. For example, a term formula might be
As an alternative more convenient for big models, each list can have
instead of a formula a
character vector corresponding to the candidates for that term. Thus we
could have The supplied model |

`scale` |
an optional argument used in the definition of the AIC statistic used to evaluate models for selection. By default, the scaled Chi-squared statistic for the initial model is used, but if forward selection is to be performed, this is not necessarily a sound choice. |

`direction` |
The mode of step-wise search, can be one of |

`trace` |
If |

`keep` |
A filter function whose input is a fitted |

`steps` |
The maximum number of steps to be considered. The default is 1000 (essentially as many as required). It is typically used to stop the process early. |

`parallel` |
If |

`...` |
Additional arguments to be passed on to |

The step-wise-selected model is returned, with up to two additional components.
There is an `"anova"`

component corresponding to the steps taken in the search, as well as a `"keep"`

component if the `keep=`

argument was supplied in the call.

We describe the most general setup, when `direction = "both"`

.
At any stage there is a current model comprising a single term from each of the term formulas supplied in the `scope=`

argument.
A series of models is fitted, each corrresponding to a formula obtained by moving each of the terms one step up or down in its regimen, relative to the formula of the current model.
If the current value for any term is at either of the extreme ends of its regimen, only one rather than two steps can be considered.
So if there are `p`

term formulas, at most `2*p - 1`

models are considered.
A record is kept of all the models ever visited (hence the `-1`

above), to avoid repetition.
Once each of these models has been fit, the "best" model
in terms of the AIC statistic is selected and defines the step.
The entire process is repeated until either the maximum number of steps has been used, or until the AIC criterion can not be decreased by any of the eligible steps.

Written by Trevor Hastie, following closely the design in the "Generalized Additive Models" chapter (Hastie, 1992) in Chambers and Hastie (1992).

Hastie, T. J. (1992)
*Generalized additive models.*
Chapter 7 of *Statistical Models in S*
eds J. M. Chambers and T. J. Hastie, Wadsworth \& Brooks/Cole.

Hastie, T. and Tibshirani, R. (1990)
*Generalized Additive Models.*
London: Chapman and Hall.

`gam.scope`

,`step`

,`glm`

, `gam`

, `drop1`

, `add1`

, `anova.gam`

1 2 3 4 5 6 7 8 9 10 | ```
data(gam.data)
gam.object <- gam(y~x+z, data=gam.data)
step.object <-step.gam(gam.object, scope=list("x"=~1+x+s(x,4)+s(x,6)+s(x,12),"z"=~1+z+s(z,4)))
## Not run:
# Parallel
require(doMC)
registerDoMC(cores=2)
step.gam(gam.object, scope=list("x"=~1+x+s(x,4)+s(x,6)+s(x,12),"z"=~1+z+s(z,4)),parallel=TRUE)
## End(Not run)
``` |

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