Description Usage Arguments Details Value Author(s) See Also Examples

This function produces a summary from an `"outference"`

object, with a similar fasion
as `summary.lm`

.

1 2 3 4 5 6 |

`object, ` |
an object of class |

`..., ` |
other arguments. |

`x, ` |
an object of class |

`digits, ` |
the number of significant digits to use when printing. |

`signif.stars, ` |
should the 'significance starts' be printed? |

This function is written in a similar fasion as `summary.lm`

. Users can get access
to the `"summary.lm"`

objects through `$summary.full`

and `$summary.rm`

.

This function returns an object of class `"summary.outference"`

, which is a list containing
the following components:

`call, ` |
the function call. |

`summary.full, ` |
an object of class |

`summary.rm, ` |
an object of class |

`method, ` |
the method used for outlier detection. |

`cutoff, ` |
the cutoff of the method. |

`outlier.det, ` |
indexes of detected outliers. |

`magnitude, ` |
a measure of "outlying-ness". For |

`sigma, ` |
the noise level used in the fit. |

`coefficients, ` |
a data frame summarizing the estimates, standard errors, values of the test statistics and corrected p-values of regression coefficients. |

`truncation.coef, ` |
a list of the truncation sets of the test statistics for each regression coefficient. |

`chisqstatistic, fstatistic, ` |
a list containing the value, the degree(s) of freedom, the truncation set, and the corrected p-value for testing the global null. |

Shuxiao Chen <[email protected]>

`outference`

for model fitting;

`coef.outference`

for extracting coefficients;

`confint.outference`

for confidence intervals of regression coefficients;

`plot.outference`

for plotting the outlying measure;

`predict.outference`

for making predictions.

1 2 3 4 5 6 | ```
## Brownlee’s Stack Loss Plant Data
data("stackloss")
## fit the model
## detect outlier using Cook's distance with cutoff = 4
fit <- outference(stack.loss ~ ., data = stackloss, method = "cook", cutoff = 4)
summary(fit) # extract the corrected p-values after outlier removal
``` |

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