Description Usage Arguments Value

Interprets the defect rule and defects the true dataset. Note that simulatedefect is generic and can defect any existing dataset according to rule.

1 | ```
simulatedefect(truedata, name, subset, prob, damage)
``` |

`truedata` |
data.frame containing the un-defected data. |

`name` |
character. Specifies variable name to be defected. |

`subset` |
formula. States a condition (e.g. ~x1 > 0.6) which specifies the fraction of observations, that are to be defected. Note, that if 'subset' does not exclusivly use the 'name[d]' variable, this implies that the independence assumption of MICE is not met (on purpose). |

`prob` |
numeric value. Specifies the binomial probability for each observation in 'subset' to be defected. |

`damage` |
By users defintion, it specifies what type and how the data is to be defected. 'damage' = NA generates missing data. A value between [0, 1] implies right censoring (e.g. 'damage' = 1/3), [1,...] left censoring. The value is used to multiply the true value of 'name' in order to defect the data. The generalization for fixed interval factors is 'damage' = list(1/3, 4/3), where the values specifiy the factor for the lower and the upper bound respectively. More realistic examples can be generated with vector valued 'damage': If 'damage' = c(0.1, 1) is a vector of length 2, it specifies the min and max value of a uniform distribution, from which a factor is randomly drawn for each observation with which the true data is multiplied. The generalization for random interval factors is 'damage' = list(c(0.2, 1), c(1,3)), where the first vector specifies the unif interval for factors affecting the lower bound and the second affecting the upper bound. NOTE: if a list is provided, both members must either vectors or single values. |

list. List elements are the defected dataframe, an indicator vector specifying which observation was defected and an atomic description of censoring type, which the user implicitly defined by damage.

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