Description Usage Arguments Details Value Author(s) References Examples

This function adapts random forests to work (albeit clumsily and inefficiently) with clustered categorical outcome data. For example, there may be multiple observations on individuals (clusters). Predictions are made fof the OOB (out of bag) clusters

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`formula` |
Model formula |

`id` |
numeric, identifies clusters |

`data` |
data frame that supplies the data |

`nfold` |
numeric, number of folds |

`ntree` |
numeric, number of trees (number of bootstrap samples) |

`progress` |
Print information on progress of calculations |

`printit` |
Print summary information on accuracy |

`seed` |
Set seed, if required, so that results are exactly reproducible |

Bootstrap samples are taken of observations in the in-bag clusters. Predictions are made for all observations in the OOB clusters.

`class` |
Predicted values from cross-validation |

`OOBaccuracy` |
Cross-validation estimate of accuracy |

`confusion` |
Confusion matrix |

John Maindonald

https://maths-people.anu.edu.au/~johnm/nzsr/taws.html

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```
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360 370 380 390 400 410 420 430 440 450 460 470 480 490 500
OOB accuracy = 0.56 \n
```

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