Call this function to perform tests. If a tests fails, an informative error will be thrown. Otherwise silent.
Uses augment_data_helper() to create copies of the same dataset as
a tibble, data frame and dataframe with rownames. When add_missing = TRUE these
datasets have missing values along the diagonal, and one row of entirely missing
values. Once the datasets have been generated, tests that:
augment(fit, data = generated_dataset) passes check_tibble() for each
generated dataset.
Output of augment(fit, data = generated_dataset) is the same for all three
generated datasets, except the data frame with rownames should also generate
a .rownames column that the tibble and nameless data frame do not.
Additional tests when test_newdata = TRUE:
head(aug(model, newdata = data)) equals aug(head(model, newdata = data)).
This commutativity check catches issues where the output of predict changes
for the same data point depending on the rest of the dataset.
check_augment_data_specification(aug, model, data, add_missing, test_newdata)An invisible NULL. This function should be called for side effects, not return values.
An augment method. For example, augment.betareg.
A fit model object to call the augment method on.
A data frame or tibble to use when testing aug.
Logical indicating whether or not missing data should be
introduced into the datasets generated with augment_data_helper(). This
missing data is only used to test the newdata argument, not the data
argument.
Logical indicating whether the newdata argument behavior
should be tested instead of the data argument behavior.