Package: CARRoT 3.0.2
CARRoT: Predicting Categorical and Continuous Outcomes Using One in Ten Rule
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) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.
Authors:
CARRoT_3.0.2.tar.gz
CARRoT_3.0.2.zip(r-4.5)CARRoT_3.0.2.zip(r-4.4)CARRoT_3.0.2.zip(r-4.3)
CARRoT_3.0.2.tgz(r-4.4-any)CARRoT_3.0.2.tgz(r-4.3-any)
CARRoT_3.0.2.tar.gz(r-4.5-noble)CARRoT_3.0.2.tar.gz(r-4.4-noble)
CARRoT_3.0.2.tgz(r-4.4-emscripten)CARRoT_3.0.2.tgz(r-4.3-emscripten)
CARRoT.pdf |CARRoT.html✨
CARRoT/json (API)
| # Install 'CARRoT' in R: |
| install.packages('CARRoT', repos = c('https://albazarova.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 11 months agofrom:50629234d1. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 30 2024 |
| R-4.5-win | OK | Aug 30 2024 |
| R-4.5-linux | OK | Aug 30 2024 |
| R-4.4-win | OK | Aug 30 2024 |
| R-4.4-mac | OK | Aug 30 2024 |
| R-4.3-win | OK | Aug 30 2024 |
| R-4.3-mac | OK | Aug 30 2024 |
Exports:AUCav_outcombcompute_max_lengthcompute_max_weightcompute_weightscross_valcubfind_intfind_subget_indicesget_predictionsget_predictions_linget_probabilitiesmake_numericmake_numeric_setsquadrregr_indsum_weights_sub
Dependencies:codetoolsdoParallelforeachiteratorsnnetrbibutilsRdpack
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Area Under the Curve | AUC |
| Averaging out the predictive power | av_out |
| Combining in a list | comb |
| Maximum number of the regressions | compute_max_length |
| Maximum feasible weight of the predictors | compute_max_weight |
| Weights of predictors | compute_weights |
| Cross-validation run | cross_val |
| Three-way interactions and squares | cub |
| Finding the interacting terms based on the index | find_int |
| Finds certain subsets of predictors | find_sub |
| Best regression | get_indices |
| Predictions for multinomial regression | get_predictions |
| Predictions for linear regression | get_predictions_lin |
| Probabilities for multinomial regression | get_probabilities |
| Turning a non-numeric variable into a numeric one | make_numeric |
| Transforming the set of predictors into a numeric set | make_numeric_sets |
| Pairwise interactions and squares | quadr |
| Indices of the best regressions | regr_ind |
| Cumulative weights of the predictors' subsets | sum_weights_sub |
