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 sarlm
augment(x, data = x\$X, ...)

## Arguments

x An object returned from spatialreg::lagsarlm() or spatialreg::errorsarlm(). Ignored, but included for internal consistency. See the details below. 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

The predict method for sarlm objects assumes that the response is known. See ?predict.sarlm for more discussion. As a result, since the original data can be recovered from the fit object, this method currently does not take in data or newdata arguments.

augment()

Other spatialreg tidiers: glance.sarlm(), tidy.sarlm()

## Value

A tibble::tibble() with columns:

.fitted

Fitted or predicted value.

.resid

The difference between observed and fitted values.

## Examples

if (FALSE) {
library(spatialreg)
data(oldcol, package = "spdep")
listw <- spdep::nb2listw(COL.nb, style = "W")

crime_sar <-
lagsarlm(CRIME ~ INC + HOVAL,
data = COL.OLD,
listw = listw,
method = "eigen")

tidy(crime_sar)
tidy(crime_sar, conf.int = TRUE)
glance(crime_sar)
augment(crime_sar)

crime_sem <- errorsarlm(CRIME ~ INC + HOVAL, data = COL.OLD, listw)

tidy(crime_sem)
tidy(crime_sem, conf.int = TRUE)
glance(crime_sem)
augment(crime_sem)

crime_sac <- sacsarlm(CRIME ~ INC + HOVAL, data = COL.OLD, listw)

tidy(crime_sac)
tidy(crime_sac, conf.int = TRUE)
glance(crime_sac)
augment(crime_sac)
}