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CARRoT (version 3.0.2)

Predicting Categorical and Continuous Outcomes Using One in Ten Rule

Description

Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) , Rhemtulla et al. (2012) , Riley et al. (2018) , Riley et al. (2019) .

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Version

Install

install.packages('CARRoT')

Version

3.0.2

License

GPL-2

Maintainer

Last Published

October 13th, 2023

Functions in CARRoT (3.0.2)

regr_ind

Indices of the best regressions
comb

Combining in a list
compute_weights

Weights of predictors
compute_max_weight

Maximum feasible weight of the predictors
AUC

Area Under the Curve
av_out

Averaging out the predictive power
find_sub

Finds certain subsets of predictors
find_int

Finding the interacting terms based on the index
compute_max_length

Maximum number of the regressions
quadr

Pairwise interactions and squares
cub

Three-way interactions and squares
get_predictions_lin

Predictions for linear regression
cross_val

Cross-validation run
get_probabilities

Probabilities for multinomial regression
make_numeric

Turning a non-numeric variable into a numeric one
sum_weights_sub

Cumulative weights of the predictors' subsets
make_numeric_sets

Transforming the set of predictors into a numeric set
get_indices

Best regression
get_predictions

Predictions for multinomial regression