Augment accepts a model object and a dataset and adds
information about each observation in the dataset. Most commonly, this
includes predicted values in the `.fitted`

column, residuals in the
`.resid`

column, and standard errors for the fitted values in a `.se.fit`

column. New columns always begin with a `.`

prefix to avoid overwriting
columns in the original dataset.

Users may pass data to augment via either the `data`

argument or the
`newdata`

argument. If the user passes data to the `data`

argument,
it **must** be exactly the data that was used to fit the model
object. Pass datasets to `newdata`

to augment data that was not used
during model fitting. This still requires that at least all predictor
variable columns used to fit the model are present. If the original outcome
variable used to fit the model is not included in `newdata`

, then no
`.resid`

column will be included in the output.

Augment will often behave differently depending on whether `data`

or
`newdata`

is given. This is because there is often information
associated with training observations (such as influences or related)
measures that is not meaningfully defined for new observations.

For convenience, many augment methods provide default `data`

arguments,
so that `augment(fit)`

will return the augmented training data. In these
cases, augment tries to reconstruct the original data based on the model
object with varying degrees of success.

The augmented dataset is always returned as a tibble::tibble with the
**same number of rows** as the passed dataset. This means that the
passed data must be coercible to a tibble. At this time, tibbles do not
support matrix-columns. This means you should not specify a matrix
of covariates in a model formula during the original model fitting
process, and that `splines::ns()`

, `stats::poly()`

and
`survival::Surv()`

objects are not supported in input data. If you
encounter errors, try explicitly passing a tibble, or fitting the original
model on data in a tibble.

We are in the process of defining behaviors for models fit with various
`na.action`

arguments, but make no guarantees about behavior when data is
missing at this time.

# S3 method for mfx augment( x, data = model.frame(x$fit), newdata = NULL, type.predict = c("link", "response", "terms"), type.residuals = c("deviance", "pearson"), se_fit = FALSE, ... ) # S3 method for logitmfx augment( x, data = model.frame(x$fit), newdata = NULL, type.predict = c("link", "response", "terms"), type.residuals = c("deviance", "pearson"), se_fit = FALSE, ... ) # S3 method for negbinmfx augment( x, data = model.frame(x$fit), newdata = NULL, type.predict = c("link", "response", "terms"), type.residuals = c("deviance", "pearson"), se_fit = FALSE, ... ) # S3 method for poissonmfx augment( x, data = model.frame(x$fit), newdata = NULL, type.predict = c("link", "response", "terms"), type.residuals = c("deviance", "pearson"), se_fit = FALSE, ... ) # S3 method for probitmfx augment( x, data = model.frame(x$fit), newdata = NULL, type.predict = c("link", "response", "terms"), type.residuals = c("deviance", "pearson"), se_fit = FALSE, ... )

x | A |
---|---|

data | A base::data.frame or |

newdata | A |

type.predict | Passed to |

type.residuals | Passed to |

se_fit | Logical indicating whether or not a |

... | Additional arguments. Not used. Needed to match generic
signature only. |

This generic augment method wraps `augment.glm()`

for applicable
objects from the `mfx`

package.

`augment.glm()`

, `mfx::logitmfx()`

, `mfx::negbinmfx()`

,
`mfx::poissonmfx()`

, `mfx::probitmfx()`

Other mfx tidiers:
`augment.betamfx()`

,
`glance.betamfx()`

,
`glance.mfx()`

,
`tidy.betamfx()`

,
`tidy.mfx()`

A `tibble::tibble()`

with columns:

Cooks distance.

Fitted or predicted value.

Diagonal of the hat matrix.

The difference between observed and fitted values.

Standard errors of fitted values.

Estimated residual standard deviation when corresponding observation is dropped from model.

Standardised residuals.

if (FALSE) { library(mfx) ## Get the marginal effects from a logit regression mod_logmfx <- logitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars) tidy(mod_logmfx, conf.int = TRUE) ## Compare with the naive model coefficients of the same logit call (not run) # tidy(glm(am ~ cyl + hp + wt, family = binomial, data = mtcars), conf.int = TRUE) augment(mod_logmfx) glance(mod_logmfx) ## Another example, this time using probit regression mod_probmfx <- probitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars) tidy(mod_probmfx, conf.int = TRUE) augment(mod_probmfx) glance(mod_probmfx) }