Predicting Categorical and Continuous Outcomes Using One in Ten Rule

[Up] [Top]

Documentation for package ‘CARRoT’ version 2.5.1

Help Pages

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
regr_whole Best regressions
sum_weights_sub Cumulative weights of the predictors' subsets