Predicting Categorical and Continuous Outcomes Using One in Ten Rule

Documentation for package ‘CARRoT’ version 3.0.2

AUC	Area Under the Curve
av_out	Averaging out the predictive power
comb	Combining in a list
compute_max_length	Maximum number of the regressions
compute_max_weight	Maximum feasible weight of the predictors
compute_weights	Weights of predictors
cross_val	Cross-validation run
cub	Three-way interactions and squares
find_int	Finding the interacting terms based on the index
find_sub	Finds certain subsets of predictors
get_indices	Best regression
get_predictions	Predictions for multinomial regression
get_predictions_lin	Predictions for linear regression
get_probabilities	Probabilities for multinomial regression
make_numeric	Turning a non-numeric variable into a numeric one
make_numeric_sets	Transforming the set of predictors into a numeric set
quadr	Pairwise interactions and squares
regr_ind	Indices of the best regressions
sum_weights_sub	Cumulative weights of the predictors' subsets