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 betareg
augment(x, data = model.frame(x), newdata = NULL,
  type.predict, type.residuals, ...)

Arguments

x

A betareg object produced by a call to betareg::betareg().

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

Character indicating type of prediction to use. Passed to the type argument of the stats::predict() generic. Allowed arguments vary with model class, so be sure to read the predict.my_class documentation.

type.residuals

Character indicating type of residuals to use. Passed to the type argument of stats::residuals() generic. Allowed arguments vary with model class, so be sure to read the residuals.my_class documentation.

...

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

For additional details on Cook's distance, see stats::cooks.distance().

See also

Value

A tibble::tibble() with columns:

.cooksd

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

.fitted

Fitted / predicted value.

.resid

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

Examples

library(betareg) data("GasolineYield", package = "betareg") mod <- betareg(yield ~ batch + temp, data = GasolineYield) mod
#> #> Call: #> betareg(formula = yield ~ batch + temp, data = GasolineYield) #> #> Coefficients (mean model with logit link): #> (Intercept) batch1 batch2 batch3 batch4 batch5 #> -6.15957 1.72773 1.32260 1.57231 1.05971 1.13375 #> batch6 batch7 batch8 batch9 temp #> 1.04016 0.54369 0.49590 0.38579 0.01097 #> #> Phi coefficients (precision model with identity link): #> (phi) #> 440.3 #>
tidy(mod)
#> # A tibble: 12 x 6 #> component term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Intercept) -6.16 0.182 -33.8 3.44e-250 #> 2 mean batch1 1.73 0.101 17.1 2.59e- 65 #> 3 mean batch2 1.32 0.118 11.2 3.34e- 29 #> 4 mean batch3 1.57 0.116 13.5 8.81e- 42 #> 5 mean batch4 1.06 0.102 10.4 4.06e- 25 #> 6 mean batch5 1.13 0.104 11.0 6.52e- 28 #> 7 mean batch6 1.04 0.106 9.81 1.03e- 22 #> 8 mean batch7 0.544 0.109 4.98 6.29e- 7 #> 9 mean batch8 0.496 0.109 4.55 5.30e- 6 #> 10 mean batch9 0.386 0.119 3.25 1.14e- 3 #> 11 mean temp 0.0110 0.000413 26.6 1.26e-155 #> 12 precision (phi) 440. 110. 4.00 6.29e- 5
tidy(mod, conf.int = TRUE)
#> # A tibble: 12 x 8 #> component term estimate std.error statistic p.value conf.low conf.high #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Inter… -6.16 1.82e-1 -33.8 3.44e-250 -6.52e+0 -5.80 #> 2 mean batch1 1.73 1.01e-1 17.1 2.59e- 65 1.53e+0 1.93 #> 3 mean batch2 1.32 1.18e-1 11.2 3.34e- 29 1.09e+0 1.55 #> 4 mean batch3 1.57 1.16e-1 13.5 8.81e- 42 1.34e+0 1.80 #> 5 mean batch4 1.06 1.02e-1 10.4 4.06e- 25 8.59e-1 1.26 #> 6 mean batch5 1.13 1.04e-1 11.0 6.52e- 28 9.31e-1 1.34 #> 7 mean batch6 1.04 1.06e-1 9.81 1.03e- 22 8.32e-1 1.25 #> 8 mean batch7 0.544 1.09e-1 4.98 6.29e- 7 3.30e-1 0.758 #> 9 mean batch8 0.496 1.09e-1 4.55 5.30e- 6 2.82e-1 0.709 #> 10 mean batch9 0.386 1.19e-1 3.25 1.14e- 3 1.53e-1 0.618 #> 11 mean temp 0.0110 4.13e-4 26.6 1.26e-155 1.02e-2 0.0118 #> 12 precision (phi) 440. 1.10e+2 4.00 6.29e- 5 2.25e+2 656.
tidy(mod, conf.int = TRUE, conf.level = .99)
#> # A tibble: 12 x 8 #> component term estimate std.error statistic p.value conf.low conf.high #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Inter… -6.16 1.82e-1 -33.8 3.44e-250 -6.63e+0 -5.69 #> 2 mean batch1 1.73 1.01e-1 17.1 2.59e- 65 1.47e+0 1.99 #> 3 mean batch2 1.32 1.18e-1 11.2 3.34e- 29 1.02e+0 1.63 #> 4 mean batch3 1.57 1.16e-1 13.5 8.81e- 42 1.27e+0 1.87 #> 5 mean batch4 1.06 1.02e-1 10.4 4.06e- 25 7.96e-1 1.32 #> 6 mean batch5 1.13 1.04e-1 11.0 6.52e- 28 8.67e-1 1.40 #> 7 mean batch6 1.04 1.06e-1 9.81 1.03e- 22 7.67e-1 1.31 #> 8 mean batch7 0.544 1.09e-1 4.98 6.29e- 7 2.63e-1 0.825 #> 9 mean batch8 0.496 1.09e-1 4.55 5.30e- 6 2.15e-1 0.776 #> 10 mean batch9 0.386 1.19e-1 3.25 1.14e- 3 8.03e-2 0.691 #> 11 mean temp 0.0110 4.13e-4 26.6 1.26e-155 9.90e-3 0.0120 #> 12 precision (phi) 440. 1.10e+2 4.00 6.29e- 5 1.57e+2 724.
augment(mod)
#> # A tibble: 32 x 6 #> yield batch temp .fitted .resid .cooksd #> * <dbl> <fct> <dbl> <dbl> <dbl> <dbl> #> 1 0.122 1 205 0.101 1.59 0.0791 #> 2 0.223 1 275 0.195 1.66 0.0917 #> 3 0.347 1 345 0.343 0.211 0.00155 #> 4 0.457 1 407 0.508 -2.88 0.606 #> 5 0.08 2 218 0.0797 0.109 0.0000168 #> 6 0.131 2 273 0.137 -0.365 0.00731 #> 7 0.266 2 347 0.263 0.260 0.00523 #> 8 0.074 3 212 0.0943 -1.77 0.0805 #> 9 0.182 3 272 0.167 1.02 0.0441 #> 10 0.304 3 340 0.298 0.446 0.0170 #> # ... with 22 more rows
glance(mod)
#> # A tibble: 1 x 6 #> pseudo.r.squared df.null logLik AIC BIC df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 0.962 30 84.8 -146. -128. 20