augment.mfx  R Documentation 
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. If a predictor enters the model as part
of a matrix of covariates, such as when the model formula uses
splines::ns()
, stats::poly()
, or 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 'mfx'
augment(
x,
data = model.frame(x$fit),
newdata = NULL,
type.predict = c("link", "response", "terms"),
type.residuals = c("deviance", "pearson"),
se_fit = FALSE,
...
)
## S3 method for class 'logitmfx'
augment(
x,
data = model.frame(x$fit),
newdata = NULL,
type.predict = c("link", "response", "terms"),
type.residuals = c("deviance", "pearson"),
se_fit = FALSE,
...
)
## S3 method for class 'negbinmfx'
augment(
x,
data = model.frame(x$fit),
newdata = NULL,
type.predict = c("link", "response", "terms"),
type.residuals = c("deviance", "pearson"),
se_fit = FALSE,
...
)
## S3 method for class 'poissonmfx'
augment(
x,
data = model.frame(x$fit),
newdata = NULL,
type.predict = c("link", "response", "terms"),
type.residuals = c("deviance", "pearson"),
se_fit = FALSE,
...
)
## S3 method for class '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. Cautionary note: Misspelled arguments will be
absorbed in

This generic augment method wraps augment.glm()
for applicable
objects from the mfx
package.
A tibble::tibble()
with columns:
.cooksd 
Cooks distance. 
.fitted 
Fitted or predicted value. 
.hat 
Diagonal of the hat matrix. 
.resid 
The difference between observed and fitted values. 
.se.fit 
Standard errors of fitted values. 
.sigma 
Estimated residual standard deviation when corresponding observation is dropped from model. 
.std.resid 
Standardised residuals. 
augment.glm()
, mfx::logitmfx()
, mfx::negbinmfx()
,
mfx::poissonmfx()
, mfx::probitmfx()
Other mfx tidiers:
augment.betamfx()
,
glance.betamfx()
,
glance.mfx()
,
tidy.betamfx()
,
tidy.mfx()
# load libraries for models and data
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
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)
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