Classification and Regression Tests

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Documentation for package ‘crtests’ version 0.2.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

crtests-package	crtests: A package for creating and executing classification and regression tests
apply_levels	Converts the column factor levels in 'df' to those in 'df_reference'
argument_match_test	Test function with non-matching arguments
capitalize_first	Capitalize the first letter of a word
classification_model	Generic function for creating a classification model
classification_model.boosting	Generic function for creating a classification model
classification_model.default	Generic function for creating a classification model
classification_model.rpart	Generic function for creating a classification model
createtest	Create a classification or regression test case
create_and_run_test	Create test and run it
crtests	crtests: A package for creating and executing classification and regression tests
drop_na	Remove NAs according to a strategy
evaluate	Evaluate the performance of a prediction.
evaluate_problem	Generic function for evaluation of test results
evaluate_problem.classification	Generic function for evaluation of test results
evaluate_problem.regression	Generic function for evaluation of test results
evaluation	Create an evaluation object
extract_formula	Extract a formula from a test
factor_length	Determine the length of the factors in a data.frame
group_levels	Group infrequent levels in 'data', either a factor or a data.frame
group_levels.data.frame	Group infrequent levels in 'data', either a factor or a data.frame
group_levels.default	Group infrequent factor levels
group_levels.factor	Group infrequent levels in 'data', either a factor or a data.frame
group_levels.list	Group infrequent levels in 'data', either a factor or a data.frame
is_complete_row	Determine if the rows in a data.frame have NAs
make_predictions	Make predictions using a model Generic function for testing a model by making predictions
make_predictions.boosting	Make predictions using a model Generic function for testing a model by making predictions
make_predictions.default	Make predictions using a model Generic function for testing a model by making predictions
make_predictions.gbm	Make predictions using a model Generic function for testing a model by making predictions
make_predictions.rpart	Make predictions using a model Generic function for testing a model by making predictions
method_prepare	Method-specific data preparation
method_prepare.default	Method-specific data preparation
method_prepare.randomForest	Method-specific data preparation
missing_argument_test	Utility functions
multisample	Make multiple samples of data
multisample.cross_fold	Make multiple samples of data
multisample.random	Make multiple samples of data
multitest	Create and run multiple instances of a test
multitest_evaluation	Create an evaluation of multiple tests
na_count	Count the number of NAs in an object
na_count.data.frame	Count the number of NAs in an object
na_count.default	Count the number of NAs in an object
prepare	Prepare the data for the specified test.
prepare.default	Prepare the data for the specified test.
prepare_data	Prepare data for training or testing.
print.evaluation	Print an 'evaluation' object
print.multitest_evaluation	Print a multitest_evaluation
print.multitest_evaluation.summary	Print a multitest_evaluation.summary object
random_string	Generate a random string
regression_model	Fit a regression model Generic function for fitting a regression model
regression_model.default	Fit a regression model Generic function for fitting a regression model
remove_names	Set any names of x to ""
remove_names.matrix	Set any names of x to ""
replace_names	Replace strings in the names of an object
replace_names.data.frame	Replace strings in the names of an object
replace_names.default	Replace strings in the names of an object
replace_names.matrix	Replace strings in the names of an object
runtest	Run a classification or regression test
runtest.default	Run a classification or regression test
summary.evaluation	Summary of an evaluation
summary.multitest_evaluation	Make a summary of multiple test evaluations
train_model	Train a classification or regression model
train_model.classification	Train a classification or regression model
train_model.regression	Train a classification or regression model
util	Utility functions