# S3 method for glm
augment(x, data = stats::model.frame(x), newdata = NULL,
type.predict, type.residuals, ...)

## Arguments

x |
A `glm` object returned from `stats::glm()` . |

data |
A `data.frame()` or `tibble::tibble()` containing the original
data that was used to produce the object `x` . Defaults to
`stats::model.frame(x)` so that `augment(my_fit)` returns the augmented
original data. **Do not** pass new data to the `data` argument.
Augment will report information such as influence and cooks distance for
data passed to the `data` argument. These measures are only defined for
the original training data. |

newdata |
A `data.frame()` or `tibble::tibble()` containing all
the original predictors used to create `x` . Defaults to `NULL` , indicating
that nothing has been passed to `newdata` . If `newdata` is specified,
the `data` argument will be ignored. |

type.predict |
Type of predictions to use when `x` is a `glm` object.
Passed to `stats::predict.glm()` . |

type.residuals |
Type of residuals to use when `x` is a `glm` object.
Passed to `stats::residuals.glm()` . |

... |
Additional arguments. Not used. Needed to match generic
signature only. **Cautionary note:** Misspelled arguments will be
absorbed in `...` , where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass `conf.lvel = 0.9` , all computation will
proceed using `conf.level = 0.95` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## Details

Note that if the weights for any of the observations in the model
are 0, then columns ".infl" and ".hat" in the result will be 0
for those observations.

## See also