Man pages for emil
Evaluation of Modeling without Information Leakage
| as.modeling_procedure | Coerce to modeling procedure |
| dichotomize | Dichotomize time-to-event data |
| emil | Introduction to the emil package |
| error_fun | Performance estimation functions |
| evaluate | Evaluate a modeling procedure |
| extension | Extending the emil framework with user-defined methods |
| factor_to_logical | Convert factors to logicals |
| fill | Replace values with something else |
| fit | Fit a model |
| fit_caret | Fit a model using the 'caret' package |
| fit_cforest | Fit conditional inference forest |
| fit_coxph | Fit Cox proportional hazards model |
| fit_glmnet | Fit elastic net, LASSO or ridge regression model |
| fit_lda | Fit linear discriminant |
| fit_lm | Fit a linear model fitted with ordinary least squares |
| fit_naive_bayes | Fit a naive Bayes classifier |
| fit_pamr | Fit nearest shrunken centroids model. |
| fit_qda | Fit quadratic discriminant. |
| fit_randomForest | Fit random forest. |
| fit_rpart | Fit a decision tree |
| fit_svm | Fit a support vector machine |
| get_color | Get color palettes |
| get_importance | Feature (variable) importance of a fitted model |
| get_performance | Extract prediction performance |
| get_prediction | Extract predictions from modeling results |
| get_response | Extract the response from a data set |
| get_tuning | Extract parameter tuning statistics |
| image.resample | Visualize resampling scheme |
| importance_glmnet | Feature importance extractor for elastic net models |
| importance_pamr | Feature importance of nearest shrunken centroids. |
| importance_randomForest | Feature importance of random forest. |
| impute | Regular imputation |
| indent | Increase indentation |
| index_fit | Convert a fold to row indexes of fittdng or test set |
| is_blank | Wrapper for several methods to test if a variable is empty |
| is_constant | Check if an object contains more than one unique value |
| is_multi_procedure | Detect if modeling results contains multiple procedures |
| learning_curve | Learning curve analysis |
| list_method | List all available methods |
| log_message | Print a timestamped and indented log message |
| mode | Get the most common value |
| modeling_procedure | Setup a modeling procedure |
| na_index | Support function for identifying missing values |
| name_procedure | Get names for modeling procedures |
| neg_gmpa | Negative geometric mean of class specific predictive accuracy |
| nice_axis | Plots an axis the way an axis should be plotted. |
| nice_box | Plots a box around a plot |
| nice_require | Load a package and offer to install if missing |
| notify_once | Print a warning message if not printed earlier |
| pipe | Pipe operator |
| plot.learning_curve | Plot results from learning curve analysis |
| plot_Surv | Plot Surv vector [DEPRECATED] |
| predict_caret | Predict using a 'caret' method |
| predict_cforest | Predict with conditional inference forest |
| predict_coxph | Predict using Cox proportional hazards model |
| predict_glmnet | Predict using generalized linear model with elastic net... |
| predict_lda | Prediction using already trained prediction model |
| predict_lm | Prediction using linear model |
| predict.model | Predict the response of unknown observations |
| predict_naive_bayes | Predict using naive Bayes model |
| predict_pamr | Prediction using nearest shrunken centroids. |
| predict_qda | Prediction using already trained classifier. |
| predict_randomForest | Prediction using random forest. |
| predict_rpart | Predict using a fitted decision tree |
| predict_svm | Predict using support vector machine |
| pre_factor_to_logical | Convert factors to logical columns |
| pre_impute | Basic imputation |
| pre_impute_df | Impute a data frame |
| pre_impute_knn | Nearest neighbors imputation |
| pre_log_message | Print log message during pre-processing |
| pre_pamr | PAMR adapted dataset pre-processing |
| pre_process | Data preprocessing |
| print.preprocessed_data | Print method for pre-processed data |
| pvalue | Extraction of p-value from a statistical test |
| pvalue.coxph | Extract p-value from a Cox proportional hazards model |
| pvalue.crr | Extracts p-value from a competing risk model |
| pvalue.cuminc | Extract p-value from a cumulative incidence estimation |
| pvalue.survdiff | Extracts p-value from a logrank test |
| resample | Resampling schemes |
| roc_curve | Calculate ROC curves |
| select | 'emil' and 'dplyr' integration |
| subresample | Generate resampling subschemes |
| subtree | Extract a subset of a tree of nested lists |
| trivial_error_rate | Calculate the trivial error rate |
| tune | Tune parameters of modeling procedures |
| validate_data | Validate a pre-processed data set |
| vlines | Add vertical or horizontal lines to a plot |
| weighted_error_rate | Weighted error rate |
