check_augment_data_specification: Check that augment behavior is consistent for dataframes and...
In modeltests: Testing Infrastructure for Broom Model Generics

View source: R/check_augment_data_specification.R

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.

1	check_augment_data_specification(aug, model, data, add_missing, test_newdata)

`aug`	An augment method. For example, `augment.betareg`.
`model`	A fit model object to call the augment method on.
`data`	A data frame or tibble to use when testing `aug`.
`add_missing`	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.
`test_newdata`	Logical indicating whether the `newdata` argument behavior should be tested instead of the `data` argument behavior.
`strict`	Logical indicating whether the strict version of tests should be used. Defaults to `TRUE`.