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 htest
augment(x, ...)

Arguments

x

An htest objected, such as those created by stats::cor.test(), stats::t.test(), stats::wilcox.test(), stats::chisq.test(), etc.

...

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

See stats::chisq.test() for more details on how residuals are computed.

See also

Value

A tibble::tibble() with exactly one row and columns:

.observed

Observed count.

.prop

Proportion of the total.

.row.prop

Row proportion (2 dimensions table only).

.col.prop

Column proportion (2 dimensions table only).

.expected

Expected count under the null hypothesis.

.residuals

Pearson residual.

.stdres

Standardized residual.

Examples

tt <- t.test(rnorm(10)) tidy(tt)
#> # A tibble: 1 x 8 #> estimate statistic p.value parameter conf.low conf.high method alternative #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> #> 1 -0.347 -1.35 0.209 9 -0.927 0.233 One Samp… two.sided
glance(tt) # same output for all htests
#> # A tibble: 1 x 8 #> estimate statistic p.value parameter conf.low conf.high method alternative #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> #> 1 -0.347 -1.35 0.209 9 -0.927 0.233 One Samp… two.sided
tt <- t.test(mpg ~ am, data = mtcars) tidy(tt)
#> # A tibble: 1 x 10 #> estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 -7.24 17.1 24.4 -3.77 0.00137 18.3 -11.3 -3.21 #> # ... with 2 more variables: method <chr>, alternative <chr>
wt <- wilcox.test(mpg ~ am, data = mtcars, conf.int = TRUE, exact = FALSE) tidy(wt)
#> # A tibble: 1 x 7 #> estimate statistic p.value conf.low conf.high method alternative #> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> #> 1 -6.80 42 0.00187 -11.7 -2.90 Wilcoxon rank sum … two.sided
ct <- cor.test(mtcars$wt, mtcars$mpg) tidy(ct)
#> # A tibble: 1 x 8 #> estimate statistic p.value parameter conf.low conf.high method alternative #> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <chr> <chr> #> 1 -0.868 -9.56 1.29e-10 30 -0.934 -0.744 Pearson… two.sided
chit <- chisq.test(xtabs(Freq ~ Sex + Class, data = as.data.frame(Titanic))) tidy(chit)
#> # A tibble: 1 x 4 #> statistic p.value parameter method #> <dbl> <dbl> <int> <chr> #> 1 350. 1.56e-75 3 Pearson's Chi-squared test
augment(chit)
#> # A tibble: 8 x 9 #> Sex Class .observed .prop .row.prop .col.prop .expected .residuals .stdres #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Male 1st 180 0.0818 0.104 0.554 256. -4.73 -11.1 #> 2 Female 1st 145 0.0659 0.309 0.446 69.4 9.07 11.1 #> 3 Male 2nd 179 0.0813 0.103 0.628 224. -3.02 -6.99 #> 4 Female 2nd 106 0.0482 0.226 0.372 60.9 5.79 6.99 #> 5 Male 3rd 510 0.232 0.295 0.722 555. -1.92 -5.04 #> 6 Female 3rd 196 0.0891 0.417 0.278 151. 3.68 5.04 #> 7 Male Crew 862 0.392 0.498 0.974 696. 6.29 17.6 #> 8 Female Crew 23 0.0104 0.0489 0.0260 189. -12.1 -17.6