Description Usage Arguments Details Value See Also Examples

Performs stepwise model selection on a robustly fitted linear model. Presently only the backward stepwise procedure is implemented.

1 2 3 |

`object` |
an |

`scope` |
either a formula or a list with elements |

`scale` |
a single numeric value containing a residual scale estimate. If missing, the scale estimate in |

`direction` |
a character value specifying the mode of stepwise search. The possibilities are "both", "backward", and "forward", with a default of "backward". Presently only "backward" stepwise searches are implemented. |

`trace` |
a logical value. If |

`keep` |
a filter function whose input is a fitted model object and the associated AIC statistic, and whose output is arbitrary. Typically keep will select a subset of the components of the object and return them. The default is not to keep anything. |

`steps` |
an integer value specifying the 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. |

`fast` |
a logical value. If |

`...` |
additional arguments required by the generic step function. |

Presently only backward stepwise selection is supported. During each step the Robust Final Prediction Error (as computed by the function `lmRob.RFPE`

) is calculated for the current model and for each sub-model achievable by deleting a single term. The function then either steps to the sub-model with the lowest Robust Final Prediction Error or, if the current model has the lowest Robust Final Prediction Error, terminates. The scale estimate from `object`

is used to compute the Robust Final Prediction Error throughout the procedure unless the `scale`

argument is provided in which case the user specified value is used.

the model with the lowest Robust Final Prediction Error encountered during the stepwise procedure is returned. Additionally, an `anova`

element corresponding to the steps taken in the search is appended to the returned object. If a `keep`

function was provided then the kept values can be found in the `keep`

element of the returned object.

`lmRob`

,
`lmRob.RFPE`

,
`drop1.lmRob`

.

1 2 3 4 5 6 7 8 9 | ```
data(stack.dat)
stack.rob <- lmRob(Loss ~ ., data = stack.dat)
## The default behavior is to try dropping all terms ##
step.lmRob(stack.rob)
## Keep Water.Temp in the model ##
my.scope <- list(lower = . ~ Water.Temp, upper = . ~ .)
step.lmRob(stack.rob, scope = my.scope)
``` |

```
Loading required package: fit.models
Start: RFPE= 16.032
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
Single term deletions
Model:
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
scale: 1.837073
Df RFPE
<none> 16.032
Air.Flow 1 36.213
Water.Temp 1 20.829
Acid.Conc. 1 16.049
Call:
lmRob(formula = Loss ~ ., data = stack.dat)
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-37.65246 0.79769 0.57734 -0.06706
Start: RFPE= 16.032
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
Single term deletions
Model:
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
scale: 1.837073
Df RFPE
<none> 16.032
Air.Flow 1 36.213
Acid.Conc. 1 16.049
Call:
lmRob(formula = Loss ~ ., data = stack.dat)
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-37.65246 0.79769 0.57734 -0.06706
```

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