Description Usage Arguments Details Author(s) Examples

View source: R/ipOversampling.R

This method corrects for the sample selection bias by the plain replication of each observation in the sample according to its IP weight, i.e. in a stratified random sample one replicates an observation of stratum h by the factor w_h.

1 | ```
ipOversampling(data, weights, normalize = FALSE)
``` |

`data` |
a data frame containing the observations rowwise, along with their corresponding categorical strata feature(s). |

`weights` |
a numerical vector whose length must coincide with the number of the rows of data. The i-th value contains the inverse-probability e.g. determines how often the i-th observation of data shall be replicated. |

`normalize` |
If weight vector should be normalized, i.e. the smallest entry of the vector will be set to 1. |

If the numeric vector contains numbers which are not natural but real, they will be rounded.

Norbert Krautenbacher, Kevin Strauss, Maximilian Mandl, Christiane Fuchs

1 2 3 4 5 6 7 8 9 | ```
library(smotefamily)
library(sambia)
data.example <- sample_generator(100,ratio = 0.80)
result <- gsub('n','0',data.example[,'result'])
result <- gsub('p','1',result)
data.example[,'result'] <- as.numeric(result)
weights <- data.example[,'result']
weights <- ifelse(weights==1,1,4)
sample <- sambia::ipOversampling(data.example,weights)
``` |

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