Description Usage Arguments Value Author(s) Examples

Creates a decision table of correlated features.

1 2 3 |

`nFeatures` |
A numeric vector of features proportions. The default is c(10,5,3,2,2). |

`rf` |
A numeric vector of correlations within feature set. |

`rd` |
A numeric vector of correlations between each feature and decision. |

`nObjects` |
A numeric value of objects number. The default is 120. |

`nOutcome` |
A numeric value of number of decision classes. The default is 2. |

`distribution` |
A character value of the name of the distribution. For discrete data choose betwen "uniform" and "binomial". For non-discrete data choose between "uniform" or "normal". The default is "uniform". |

`unbalanced` |
Logical. Set TRUE to generate unbalanced data. Default is FALSE. |

`pUnbalancedClass` |
A numeric value of number of unbalanced proportion for the first class. The default is 0.8. |

`discrete` |
Logical. Set TRUE to generate discrete data. Default is FALSE. |

`levels` |
A numeric value of discretization levels. The default is 4. |

`labels` |
A character vector of discretization labels for levels of discretization. |

`binProb` |
A numeric value of probability for binomial distribution. The default is 0.5. |

`seed` |
A numeric value of seed. The default is 1. |

`output` |
A data frame with a decision table. |

Mateusz Garbulowski

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
library(R.ROSETTA)
### continuous data ###
## weak correlation
df1 <- synData(nFeatures=c(5,5,3,2,2), rf=c(0.2,0.3,0.2,0.4,0.4), rd=c(0.2,0.3,0.4,0.3,0.4))
out1 <- rosetta(df1)
out1$quality ## accuracy = 60%
## medium correlation
df2 <- synData(nFeatures=c(5,5,3,2,2), rf=c(0.2,0.3,0.2,0.4,0.4), rd=c(0.4,0.4,0.6,0.6,0.7))
out2 <- rosetta(df2)
out2$quality ## accuracy = 75%
## strong correlation
df3 <- synData(nFeatures=c(5,5,3,2,2), rf=c(0.2,0.3,0.2,0.4,0.4), rd=c(0.5,0.7,0.7,0.8,0.8))
out3 <- rosetta(df3)
out3$quality ## accuracy = 90%
### discrete data ###
dfd <- synData(nFeatures=c(5,5,3,2,2), rf=c(0.2,0.3,0.2,0.4,0.4),
rd=c(0.2,0.3,0.4,0.5,0.6), discrete = T, levels = 3, labels = c("low", "medium", "high"))
outd <- rosetta(dfd, discrete = T)
outd$quality ## accuracy = 85%
``` |

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