Implements combinatorial (exhaustive) search algorithm, aka all-possible-subsets regression. As opposed to the sequential approach (stepwise, forward addition, or backward elimination) that has a potential bias resulting from considering only one variable for selection at a time, all possible combinations of the independent variables are examined, and sets satisfying designated conditions are returned.

1 2 |

`dv` |
Dependent variable ( |

`iv` |
Independent variable(s) ( |

`min` |
Minimum number of independent variable to explore (>= |

`max` |
Maximum number of independent variable to explore (<= |

`mad` |
Returns mean absolute deviation when |

`aic` |
Returns Akaike's information criterion when |

`bic` |
Returns Bayesian information criterion when |

`model.sig` |
Returns models statistically significant only when |

`coeff.sig` |
Returns models with statistically significant coefficients only when |

`coeff.vif` |
Returns models with allowable level of multicollinearity only when |

`coeff.cor` |
Returns models without suppression effects only when |

Dong-Joon Lim, PhD

Hair, Joseph F., et al. Multivariate data analysis. Vol. 7. *Upper Saddle River*, NJ: Pearson Prentice Hall, 2006.

1 2 3 4 5 6 7 8 9 | ```
# Load airplane dataset
df <- dataset.airplane.2017
# ready
dv <- subset(df, select = 2)
iv <- subset(df, select = 3 : 7)
# go
ma.aps.reg(dv, iv, 1, 3, mad = TRUE, coeff.cor = TRUE)
``` |

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