perturb_dataset {CIMICE} | R Documentation

## Perturbate a boolean matrix

### Description

Given a boolean matrix, randomly add False Positives (FP), False Negatives (FN) and Missing data following user defined rates. In the final matrix, missing data is represented by the value 3.

### Usage

    perturb_dataset(dataset, FP_rate = 0, FN_rate = 0, MIS_rate = 0)

### Arguments

`dataset` | a matrix/sparse matrix
`FP_rate` | False Positive rate
`FN_rate` | False Negative rate
`MIS_rate` | Missing Data rate

### Details

Note that CIMICE does not support dataset with missing data natively, so using MIS_rate != 0 will then require some pre-processing.

### Value

the new, perturbed, matrix

### Examples

    require(dplyr)

    example_dataset() %>%
      make_generator_stub() %>%
      set_generator_edges(
        list(
          "D", "A, D", 1 ,
          "A", "A, D", 1 ,
          "A, D", "A, C, D", 1 ,
          "A, D", "A, B, D", 1 ,
          "Clonal", "D", 1 ,
          "Clonal", "A", 1 ,
          "D", "D", 1 ,
          "A", "A", 1 ,
          "A, D", "A, D", 1 ,
          "A, C, D", "A, C, D", 1 ,
          "A, B, D", "A, B, D", 1 ,
          "Clonal", "Clonal", 1
      )) %>%
      finalize_generator %>%
      simulate_generator(3, 10) %>%
      perturb_dataset(FP_rate = 0.01, FN_rate = 0.1, MIS_rate = 0.12)

[Package CIMICE version 1.2.1 Index]