Package 'modeltests'

Determine acceptable names for augment output

Description

Given a data frame (or tibble), and a model object, makes a character vector of acceptable columns names for augment output. This includes:

• Any column names of the passed dataset

• Any syntactically correct column names generated by calling stats::model.frame() on the object in question.

Usage

acceptable_augment_colnames(object, passed_data)

Arguments

object	A model object.
passed_data	The dataset used to create the model object.

Value

A vector of colnames that are acceptable in augment output.

---

Allowed argument names in tidiers

Description

Allowed argument names in tidiers

Usage

argument_glossary

Format

A tibble with 3 variables:

method

One of "glance", "augment" or "tidy".

argument

Character name of allowed argument name.

description

Character description of argument use.

Examples

argument_glossary

---

Check that tidying methods use allowed argument names

Description

Call this function to perform tests. If a tests fails, an informative error will be thrown. Otherwise silent.

Tests when strict = FALSE:

• None

Tests when strict = TRUE:

• tidy_method has a conf.int argument if it has a conf.level argument.

• tidy_method has a conf.level argument if it has a conf.int argument.

• conf.int defaults to FALSE when present.

• conf.level defaults to '0.95“ when present.

• exponentiate defaults to FALSE when present.

• All arguments to tidy_method are listed in the argument_glossary.

Usage

check_arguments(tidy_method, strict = TRUE)

Arguments

tidy_method	A tidying method. For example: glance.Arima.
strict	Logical indicating whether the strict version of tests should be used. Defaults to TRUE.

Value

An invisible NULL. This function should be called for side effects, not return values.

See Also

testthat, testthat::expect_true()

---

Check an augment method

Description

Call this function to perform tests. If a tests fails, an informative error will be thrown. Otherwise silent.

Test when strict = FALSE:

• aug(model, data = data) passes check_tibble()

• aug(model, newdata = newdata) passes check_tibble()

Additional tests when strict = TRUE:

• aug(model, data = data) passes check_augment_data_specification().

• aug(model, newdata = newdata) passes check_augment_data_specification().

• aug(model, newdata = newdata) passes check_augment_data_specification() with add_missing = TRUE.

• If aug has a newdata argument, the newdata argument takes precedence over a data argument, i.e. calls check_augment_newdata_precedence().

• aug either gives an informative error or produces a reasonable tibble, i.e. calls check_augment_no_data().

Note that it doesn't make sense to test that aug(model, data = data) passes check_augment_data_specification() with add_missing = TRUE. This is because the user is already guaranteeing that data is the original dataset used to create model.

Usage

check_augment_function(aug, model, data = NULL, newdata = NULL, strict = TRUE)

Arguments

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.
newdata	A dataset to use to check the newdata behavior, ideally distinct for the dataset used to check the data behavior.
strict	Logical indicating whether the strict version of tests should be used. Defaults to TRUE.

Value

An invisible NULL. This function should be called for side effects, not return values.

---

Check that newdata argument has higher precedence than data argument

Description

Call this function to perform tests. If a tests fails, an informative error will be thrown. Otherwise silent.

Usage

check_augment_newdata_precedence(aug, model, data, strict = TRUE)

Arguments

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.
strict	Logical indicating whether the strict version of tests should be used. Defaults to TRUE.

Value

An invisible NULL. This function should be called for side effects, not return values.

---

Check an augment method when no data or newdata is passed

Description

Call this function to perform tests. If a tests fails, an informative error will be thrown. Otherwise silent.

Test when strict = FALSE:

• None

Additional tests when strict = TRUE:

• aug(model) either returns an informative error or produces output that passes check_tibble().

• If the output passes check_tibble, will issue warning when:

  • Augmented data is missing rows from original data.

  • Augmented data is missing columns from original data.

  • Original data has rownames but ugmented data is missing .rownames column.

Usage

check_augment_no_data(aug, model, passed_data, strict = TRUE)

Arguments

aug	An augment method. For example, augment.betareg.
model	A fit model object to call the augment method on.
passed_data	The dataset that model was original fit on that aug should try to reconstruct when neither data nor newdata is specified.
strict	Logical indicating whether the strict version of tests should be used. Defaults to TRUE.

Value

An invisible NULL. This function should be called for side effects, not return values.

---

Check that tibble has expected dimensions.

Description

Check that tibble has expected dimensions.

Usage

check_dims(data, expected_rows = NULL, expected_cols = NULL)

Arguments

data	A tibble or data frame.
expected_rows	Expected number of rows of tibble.
expected_cols	Expected number of columns of tibble.

Examples

check_dims(iris, expected_rows = 150)

---

Check the output of a glance method

Description

Call this function to perform tests. If a tests fails, an informative error will be thrown. Otherwise silent.

Tests when strict = FALSE:

• Each item passed to ... passes check_tibble()

• Each item passed to ... has exactly 1 row.

Additional tests when strict = TRUE:

• Column names and order agree across all elements of ....

Usage

check_glance_outputs(..., strict = TRUE)

Arguments

...	Outputs returned from calls to (the same) glance method.
strict	Logical indicating whether the strict version of tests should be used. Defaults to TRUE.

Value

An invisible NULL. This function should be called for side effects, not return values.

See Also

check_tibble()

---

Check the output of a tidying method

Description

Call this function to perform tests. If a tests fails, an informative error will be thrown. Otherwise silent.

Tests when strict = FALSE:

• output is a tibble.

Additional tests when strict = TRUE:

• columns are listed in the column_glossary.

Usage

check_tibble(output, method, columns = colnames(output), strict = TRUE)

Arguments

output	Object returned from tidy(), augment() or glance().
method	One of "tidy", "augment" or "glance". Determines which set of column name checks are applied.
columns	The names of the columns in the output data frame. Defaults to the column names of output. Useful when checking augment() when you only want to check the new columns in the data frame, as opposed to all columns.
strict	Logical indicating whether the strict version of tests should be used. Defaults to TRUE.

Details

Do not call directly. Helper function used by check_tidy_output(), check_glance_outputs() and check_augment_function().

Value

An invisible NULL. This function should be called for side effects, not return values.

---

Check the output of a tidy method

Description

Call this function to perform tests. If a tests fails, an informative error will be thrown. Otherwise silent.

A thin wrapper around check_tibble().

Usage

check_tidy_output(td, strict = TRUE)

Arguments

td	Output from a tidy method.
strict	Logical indicating whether the strict version of tests should be used. Defaults to TRUE.

Value

An invisible NULL. This function should be called for side effects, not return values.

---

Allowed column names in tidied tibbles

Description

Allowed column names in tidied tibbles

Usage

column_glossary

Format

A tibble with 4 variables:

method

One of "glance", "augment" or "tidy".

column

Character name of allowed output column.

description

Character description of expected column contents.

Examples

column_glossary

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Check whether or not a data-frame-like object has rownames

Description

Check whether or not a data-frame-like object has rownames

Usage

has_rownames(df)

Arguments

df	A data frame

Value

Logical indicating if df has rownames. If df is a tibble, returns FALSE. If df is a data.frame, return FALSE if the rownames are simply row numbers. If the rownames are anything other than the return row numbers, returns TRUE.

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