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
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
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
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
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. If a predictor enters the model as part
of a matrix of covariates, such as when the model formula uses
survival::Surv(), it is represented
as a matrix column.
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 class 'felm' augment(x, data = model.frame(x), ...)
A base::data.frame or
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
tibble::tibble() with columns:
Fitted or predicted value.
The difference between observed and fitted values.
Other felm tidiers:
# load libraries for models and data library(lfe) # use built-in `airquality` dataset head(airquality) # no FEs; same as lm() est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality) # summarize model fit with tidiers tidy(est0) augment(est0) # add month fixed effects est1 <- felm(Ozone ~ Temp + Wind + Solar.R | Month, airquality) # summarize model fit with tidiers tidy(est1) tidy(est1, fe = TRUE) augment(est1) glance(est1) # the "se.type" argument can be used to switch out different standard errors # types on the fly. In turn, this can be useful exploring the effect of # different error structures on model inference. tidy(est1, se.type = "iid") tidy(est1, se.type = "robust") # add clustered SEs (also by month) est2 <- felm(Ozone ~ Temp + Wind + Solar.R | Month | 0 | Month, airquality) # summarize model fit with tidiers tidy(est2, conf.int = TRUE) tidy(est2, conf.int = TRUE, se.type = "cluster") tidy(est2, conf.int = TRUE, se.type = "robust") tidy(est2, conf.int = TRUE, se.type = "iid")
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