partition_dataset {Melissa} | R Documentation

## Partition synthetic dataset to training and test set

### Description

Partition synthetic dataset to training and test set

### Usage

    partition_dataset(dt_obj, data_train_prcg = 0.5,
      region_train_prcg = 0.95, cpg_train_prcg = 0.5, is_synth = FALSE)

### Arguments

`dt_obj` | Melissa data object
`data_train_prcg` | Percentage of genomic regions that will be fully used for training, i.e. across the whole region we will have no CpGs missing.
`region_train_prcg` | Fraction of genomic regions to keep for training set, i.e. some genomic regions will have no coverage at all during training.
`cpg_train_prcg` | Fraction of CpGs in each genomic region to keep for training set.
`is_synth` | Logical, whether we have synthetic data or not.

### Value

The Melissa object with the following changes. The 'met' element will now contain only the 'training' data. An additional element called 'met_test' will store the data that will be used during testing to evaluate the imputation performance. These data will not be seen from Melissa during inference.

### Author(s)

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

### See Also

`create_melissa_data_obj`, `melissa`, `filter_regions`

### Examples

    # Partition the synthetic data from Melissa package
    dt <- partition_dataset(melissa_encode_dt)

[Package Melissa version 1.2.0 Index]