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 all columns used to fit the model are present.

Augment will often behavior different depending on whether data or newdata is specified. 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 some 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 lm
augment(x, data = stats::model.frame(x), newdata = NULL,
  type.predict, type.residuals, ...)

Arguments

x

An lm object created by stats::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().

...

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

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 with 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 augment() and na.action = "na.exclude", a warning is raised and the incomplete rows are dropped.

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

When newdata is supplied, only returns .fitted, .resid and .se.fit columns.

See also

Value

A tibble::tibble() with columns:

.cooksd

Cooks distance. See [stats::cooks.distance()] for additional details.

.fitted

Fitted / predicted value.

.hat

Diagonal of the hat matrix. TODO -- add interpretation.

.resid

Residuals of fitted values. TODO -- document when present. Residuals on which data set?

.se.fit

Standard errors of fitted values. TODO -- prediction or mean interval?

.sigma

Estimate of residual standard deviation when corresponding observation is dropped from model. Same as LOO-CV estimate?.

.std.resid

Standardised residuals Some unusual `lm` objects, such as `rlm` from MASS, may omit `.cooksd` and `.std.resid`.

Examples

library(ggplot2) library(dplyr) mod <- lm(mpg ~ wt + qsec, data = mtcars) tidy(mod)
#> # A tibble: 3 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 19.7 5.25 3.76 7.65e- 4 #> 2 wt -5.05 0.484 -10.4 2.52e-11 #> 3 qsec 0.929 0.265 3.51 1.50e- 3
glance(mod)
#> # A tibble: 1 x 11 #> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC #> * <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> #> 1 0.826 0.814 2.60 69.0 9.39e-12 3 -74.4 157. 163. #> # ... with 2 more variables: deviance <dbl>, df.residual <int>
# coefficient plot d <- tidy(mod) %>% mutate( low = estimate - std.error, high = estimate + std.error ) ggplot(d, aes(estimate, term, xmin = low, xmax = high, height = 0)) + geom_point() + geom_vline(xintercept = 0) + geom_errorbarh()
augment(mod)
#> # A tibble: 32 x 11 #> .rownames mpg wt qsec .fitted .se.fit .resid .hat .sigma .cooksd #> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 2.62 16.5 21.8 0.683 -0.815 0.0693 2.64 2.63e-3 #> 2 Mazda RX… 21 2.88 17.0 21.0 0.547 -0.0482 0.0444 2.64 5.59e-6 #> 3 Datsun 7… 22.8 2.32 18.6 25.3 0.640 -2.53 0.0607 2.60 2.17e-2 #> 4 Hornet 4… 21.4 3.22 19.4 21.6 0.623 -0.181 0.0576 2.64 1.05e-4 #> 5 Hornet S… 18.7 3.44 17.0 18.2 0.512 0.504 0.0389 2.64 5.29e-4 #> 6 Valiant 18.1 3.46 20.2 21.1 0.803 -2.97 0.0957 2.58 5.10e-2 #> 7 Duster 3… 14.3 3.57 15.8 16.4 0.701 -2.14 0.0729 2.61 1.93e-2 #> 8 Merc 240D 24.4 3.19 20 22.2 0.730 2.17 0.0791 2.61 2.18e-2 #> 9 Merc 230 22.8 3.15 22.9 25.1 1.41 -2.32 0.295 2.59 1.59e-1 #> 10 Merc 280 19.2 3.44 18.3 19.4 0.491 -0.185 0.0358 2.64 6.55e-5 #> # ... with 22 more rows, and 1 more variable: .std.resid <dbl>
augment(mod, mtcars)
#> # A tibble: 32 x 19 #> .rownames mpg cyl disp hp drat wt qsec vs am gear carb #> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 Mazda RX… 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 7… 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet S… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 Duster 3… 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # ... with 22 more rows, and 7 more variables: .fitted <dbl>, .se.fit <dbl>, #> # .resid <dbl>, .hat <dbl>, .sigma <dbl>, .cooksd <dbl>, .std.resid <dbl>
# predict on new data newdata <- mtcars %>% head(6) %>% mutate(wt = wt + 1) augment(mod, newdata = newdata)
#> # A tibble: 6 x 13 #> mpg cyl disp hp drat wt qsec vs am gear carb .fitted #> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 3.62 16.5 0 1 4 4 16.8 #> 2 21 6 160 110 3.9 3.88 17.0 0 1 4 4 16.0 #> 3 22.8 4 108 93 3.85 3.32 18.6 1 1 4 1 20.3 #> 4 21.4 6 258 110 3.08 4.22 19.4 1 0 3 1 16.5 #> 5 18.7 8 360 175 3.15 4.44 17.0 0 0 3 2 13.1 #> 6 18.1 6 225 105 2.76 4.46 20.2 1 0 3 1 16.0 #> # ... with 1 more variable: .se.fit <dbl>
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)
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
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()
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
# column-wise models a <- matrix(rnorm(20), nrow = 10) b <- a + rnorm(length(a)) result <- lm(b ~ a) tidy(result)
#> # A tibble: 6 x 6 #> response term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Y1 (Intercept) 0.0799 0.293 0.273 0.793 #> 2 Y1 a1 1.61 0.329 4.89 0.00176 #> 3 Y1 a2 0.264 0.292 0.905 0.395 #> 4 Y2 (Intercept) 0.707 0.390 1.82 0.112 #> 5 Y2 a1 0.700 0.438 1.60 0.154 #> 6 Y2 a2 0.0863 0.388 0.222 0.830