Description Usage Arguments Details Value References See Also Examples

Stepwise elimination of the non significant regression parameters. Possibility to assign a fixed value ` shape1`

to the overall shape parameter.

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`obj0` |
object of class regSGB, see |

`d` |
data matrix of explanatory variables (without constant vector) |

`u` |
data matrix of compositions (independent variables) |

`weight` |
vector of length |

`shape10` |
positive number, initial value of the overall shape parameter, default obj0[["par"]][1]. |

`bound` |
inequality constraints on the estimates of shapes: |

`shape1` |
fixed value of the overall shape parameter. Default is NULL (no fixed value). |

`Mean2` |
logical, if TRUE (default), the initial shape2 parameters are each replaced by their average. See |

`maxiter` |
maximum number of iterations, i.e. attempts to set a parameter to 0. |

`control.optim` |
list of control parameters for optim, see |

`control.outer` |
list of control parameters to be used by the outer loop in constrOptim.nl, see |

This is an experimental procedure for searching a set of non-significant parameters that will be set to zero. The shape parameters are excluded from the elimination procedure. The algorithm starts with `obj0`

, output of regSGB. The p-values for the regression parameters in `summary(obj0)`

are taken in decreasing order. The parameter with the largest p-value is set to zero and `regSGB`

computes the regression with this constraint. If the AIC value is smaller than the AIC in `obj0`

, the parameter with the next largest p-value in `obj0`

is set to zero and the regression with the two constraints is computed. The process iterates until either a larger AIC is found or `maxiter`

is attained.

The initial value of the overall shape parameter is set to the estimated value in the full model `obj0`

. The other initial values are computed as in `regSGB`

.

There is the possibility to fix the value of the overvall shape parameter, if `shape1`

is given a positive number *a_0* (default NULL, no fixed value).

If `regSGB`

was called without `Formula`

, the data-frame with auxiliary variables for `stepSGB`

follows the same rules as for the initial regSGB object, see Example 1 in `regSGB`

.

A list of class 'stepSGB' with the following 5 components:

`reg ` |
A list with the following components: |

`Formula ` |
The original formula, or NULL |

`iter ` |
Value of k, the last iteration. |

`tab ` |
Data frame with |

`call ` |
Arguments for calling |

`vignette("SGB regression", package = "SGB")`

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