View source: R/stablelearner.R

dgp_twoclass | R Documentation |

Data-generating function to generate artificial data sets of a classification
problem with two response classes, denoted as `"A"`

and `"B"`

.

dgp_twoclass(n = 100, p = 4, noise = 16, rho = 0, b0 = 0, b = rep(1, p), fx = identity)

`n` |
integer. Number of observations. The default is 100. |

`p` |
integer. Number of signal predictors. The default is 4. |

`noise` |
integer. Number of noise predictors. The default is 16. |

`rho` |
numeric value between -1 and 1 specifying the correlation
between the signal predictors. The correlation is given by |

`b0` |
numeric value. Baseline probability for class |

`b` |
numeric value. Slope parameter for the predictors on the logit scale. The default is 1 for all predictors. |

`fx` |
a function that is used to transform the predictors. The default
is |

A `data.frame`

including a column denoted as `class`

that is
a factor with two levels `"A"`

and `"B"`

. All other columns
represent the predictor variables (signal predictors followed by noise
predictors) and are named by `"x1"`

, `"x2"`

, etc..

`stability`

dgp_twoclass(n = 200, p = 6, noise = 4)

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