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 'lm' augment( x, data = model.frame(x), newdata = NULL, se_fit = FALSE, interval = c("none", "confidence", "prediction"), ... )
A base::data.frame or
Logical indicating whether or not a
Character indicating the type of confidence interval columns
to be added to the augmented output. Passed on to
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
When the modeling was performed with
na.action = "na.omit"
(as is the typical default), rows with NA in the initial data are omitted
entirely from the augmented data frame. When the modeling was performed
na.action = "na.exclude", one should provide the original data
as a second argument, at which point the augmented data will contain those
rows (typically with NAs in place of the new columns). If the original data
is not provided to
na.action = "na.exclude", a
warning is raised and the incomplete rows are dropped.
lm objects, such as
rlm from MASS, may omit
gam from mgcv omits
newdata is supplied, only returns
tibble::tibble() with columns:
Fitted or predicted value.
Diagonal of the hat matrix.
Lower bound on interval for fitted values.
The difference between observed and fitted values.
Standard errors of fitted values.
Estimated residual standard deviation when corresponding observation is dropped from model.
Upper bound on interval for fitted values.
Other lm tidiers:
library(ggplot2) library(dplyr) mod <- lm(mpg ~ wt + qsec, data = mtcars) tidy(mod) glance(mod) # coefficient plot d <- tidy(mod, conf.int = TRUE) ggplot(d, aes(estimate, term, xmin = conf.low, xmax = conf.high, height = 0)) + geom_point() + geom_vline(xintercept = 0, lty = 4) + geom_errorbarh() # aside: There are tidy() and glance() methods for lm.summary objects too. # this can be useful when you want to conserve memory by converting large lm # objects into their leaner summary.lm equivalents. s <- summary(mod) tidy(s, conf.int = TRUE) glance(s) augment(mod) augment(mod, mtcars, interval = "confidence") # predict on new data newdata <- mtcars %>% head(6) %>% mutate(wt = wt + 1) augment(mod, newdata = newdata) # ggplot2 example where we also construct 95% prediction interval # simpler bivariate model since we're plotting in 2D mod2 <- lm(mpg ~ wt, data = mtcars) au <- augment(mod2, newdata = newdata, interval = "prediction") ggplot(au, aes(wt, mpg)) + geom_point() + geom_line(aes(y = .fitted)) + geom_ribbon(aes(ymin = .lower, ymax = .upper), col = NA, alpha = 0.3) # predict on new data without outcome variable. Output does not include .resid newdata <- newdata %>% select(-mpg) augment(mod, newdata = newdata) au <- augment(mod, data = mtcars) ggplot(au, aes(.hat, .std.resid)) + geom_vline(size = 2, colour = "white", xintercept = 0) + geom_hline(size = 2, colour = "white", yintercept = 0) + geom_point() + geom_smooth(se = FALSE) plot(mod, which = 6) ggplot(au, aes(.hat, .cooksd)) + geom_vline(xintercept = 0, colour = NA) + geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") + geom_smooth(se = FALSE) + geom_point() # column-wise models a <- matrix(rnorm(20), nrow = 10) b <- a + rnorm(length(a)) result <- lm(b ~ a) tidy(result)
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