eval_imputation_performance {Melissa} | R Documentation

## Evaluate imputation performance

### Description

`eval_imputation_performance` is a wrapper function for computing imputation/clustering performance in terms of different metrics, such as AUC and precision recall curves.

### Usage

    eval_imputation_performance(obj, imputation_obj)

### Arguments

`obj` | Output of Melissa inference object.
`imputation_obj` | List containing two vectors of equal length, corresponding to true methylation states and predicted/imputed methylation states.

### Value

The 'melissa' object, with an additional slot named 'imputation', containing the AUC, F-measure, True Positive Rate (TPR) and False Positive Rate (FPR), and Precision Recall (PR) curves.

### Author(s)

C.A.Kapourani C.A.Kapourani@ed.ac.uk

### See Also

`create_melissa_data_obj`, `melissa`, `filter_regions`, `eval_imputation_performance`, `eval_cluster_performance`

### Examples

    # First take a subset of cells to efficiency
    # Extract synthetic data
    dt <- melissa_synth_dt

    # Partition to train and test set
    dt <- partition_dataset(dt)

    # Create basis object from BPRMeth package
    basis_obj <- BPRMeth::create_rbf_object(M = 3)

    # Run Melissa
    melissa_obj <- melissa(X = dt$met, K = 2, basis = basis_obj, vb_max_iter = 10,
      vb_init_nstart = 1, is_parallel = FALSE, is_verbose = FALSE)

    imputation_obj <- impute_met_state(obj = melissa_obj, test = dt$met_test)

    melissa_obj <- eval_imputation_performance(obj = melissa_obj,
                                               imputation_obj = imputation_obj)

    cat("AUC: ", melissa_obj$imputation$auc)

[Package Melissa version 1.2.0 Index]