Description Usage Arguments Value Acknowledgement Author(s) References See Also Examples

View source: R/Exploratory.Phase.R

This function performs the exploratory phase on the variables retained through the reduction phase, returning any significant squared and interaction terms.

1 2 |

`X` |
Design matrix. |

`Y` |
Response vector. |

`list.reduction` |
Indices of retained variables from the reduction phase. |

`family` |
A description of the error distribution and link function to be used in the model. For glm this can be a character string naming a family function, a family function or the result of a call to a family function. See |

`signif` |
Significance level for the assessment of squared and interaction terms. The default is 0.01. |

`silent` |
By default, silent=TRUE. If silent=FALSE the user can decide upon the exclusion of individual interaction terms. |

`Cox.Hazard` |
If TRUE fits proportional hazards regression model. The family argument will be ignored if Cox.Hazard=TRUE. |

`mat.select.SQ` |
Indices of variables with significant squared terms. |

`mat.select.INTER` |
Indices of the pairs of variables with significant interaction terms. |

The work was supported by the UK Engineering and Physical Sciences Research Council under grant number EP/P002757/1.

Hoeltgebaum, H. H.

Cox, D. R., and Battey, H. S. (2017). Large numbers of explanatory variables, a semi-descriptive analysis. *Proceedings of the National Academy of Sciences*, 114(32), 8592-8595.

Battey, H. S. and Cox, D. R. (2018). Large numbers of explanatory variables: a probabilistic assessment. *Proceedings of the Royal Society of London, A.*, 474(2215), 20170631.

Hoeltgebaum, H., & Battey, H. S. (2019). HCmodelSets: An R Package for Specifying Sets of Well-fitting Models in High Dimensions. *The R Journal*, 11(2), 370-379.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## Generates a random DGP
dgp = DGP(s=5, a=3, sigStrength=1, rho=0.9, n=100, intercept=5, noise=1,
var=1, d=1000, DGP.seed = 2018)
#Reduction Phase using only the first 70 observations
outcome.Reduction.Phase = Reduction.Phase(X=dgp$X[1:70,],Y=dgp$Y[1:70],
family=gaussian, seed.HC = 1012)
# Exploratory Phase using only the first 70 observations, choosing the variables which
# were selected at least two times in the third dimension reduction
idxs = outcome.Reduction.Phase$List.Selection$`Hypercube with dim 2`$numSelected1
outcome.Exploratory.Phase = Exploratory.Phase(X=dgp$X[1:70,],Y=dgp$Y[1:70],
list.reduction = idxs,
family=gaussian, signif=0.01)
``` |

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