Package index • MantaID

All functions

Example: ID example dataset.

mi(): A wrapper function that executes MantaID workflow.

mi_balance_data(): Data balance. Most classes adopt random undersampling, while a few classes adopt smote method to oversample to obtain relatively balanced data;

mi_clean_data(): Reshape data and delete meaningless rows.

mi_data_attributes: ID-related datasets in biomart.

mi_data_procID: Processed ID data.

mi_data_rawID: ID dataset for testing.

mi_filter_feat(): Performing feature selection in a automatic way based on correlation and feature importance.

mi_get_ID(): Get ID data from the Biomart database using attributes.

mi_get_ID_attr(): Get ID attributes from the Biomart database.

mi_get_confusion(): Compute the confusion matrix for the predicted result.

mi_get_miss(): Observe the distribution of the false response of the test set.

mi_get_padlen(): Get max length of ID data.

mi_plot_cor(): Plot correlation heatmap.

mi_plot_heatmap(): Plot heatmap for result confusion matrix.

mi_predict_new(): Predict new data with a trained learner.

mi_run_bmr(): Compare classification models with small samples.

mi_split_col(): Cut the string of ID column character by character and divide it into multiple columns.

mi_split_str(): Split the string into individual characters and complete the character vector to the maximum length.

mi_to_numer(): Convert data to numeric, and for the ID column convert with fixed levels.

mi_train_BP(): Train a three layers neural network model.

mi_train_rg(): Random Forest Model Training.

mi_train_rp(): Classification tree model training.

mi_train_xgb(): Xgboost model training

mi_tune_rg(): Tune the Random Forest model by hyperband.

mi_tune_rp(): Tune the Decision Tree model by hyperband.

mi_tune_xgb(): Tune the Xgboost model by hyperband.

mi_unify_mod(): Predict with four models and unify results by the sub-model's specificity score to the four possible classes.