R: Augment a(n) glm object

augment.glm {broom}

R Documentation

Augment a(n) glm object

This augment method wraps augment.lm().

## S3 method for class 'glm'
augment(x, ...)

x

A glm object returned from stats::glm().

...

Arguments passed on to augment.lm

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().

When newdata is not supplied augment.lm returns one row for each observation, with seven columns added to the original data:

`.hat`	Diagonal of the hat matrix
`.sigma`	Estimate of residual standard deviation when corresponding observation is dropped from model
`.cooksd`	Cooks distance, `cooks.distance()`
`.fitted`	Fitted values of model
`.se.fit`	Standard errors of fitted values
`.resid`	Residuals
`.std.resid`	Standardised residuals

Some unusual lm objects, such as rlm from MASS, may omit .cooksd and .std.resid. gam from mgcv omits .sigma.

When newdata is supplied, returns one row for each observation, with three columns added to the new data:

`.fitted`	Fitted values of model
`.se.fit`	Standard errors of fitted values
`.resid`	Residuals of fitted values on the new data