cvWtTuning {ClinicalUtilityRecal} | R Documentation |

## Cross-validation for Selecting Weight Tuning Parameter

### Description

Calibration weights require specification of tuning parameter `delta`

or `lambda`

. This function uses K-fold cross-validation to select tuning parameter used for calibration weights, with standardized net benfeit (sNB) as objective function. Either one of `delta`

or `lambda`

must be specificed. The sequence of tuning parameters can be obtained from the `RAWgrid`

function.

### Usage

```
cvWtTuning(p,y,r,rl,ru,kFold=5,cvParm,tuneSeq,cv.seed=1111)
```

### Arguments

`y` |
Vector of binary outcomes, with 1 indicating event (cases) and 0 indicating no event (controls) |

`p` |
Vector of risk score values |

`r` |
Clinically relevant risk threshold |

`rl` |
Lower bound of clinically relevant region |

`ru` |
Upper bound of clinically relevant region |

`kFold` |
Number of folds for cross-validation |

`cvParm` |
Parameter to be selected via cross-validation. Can be either |

`tuneSeq` |
Sequence of values of tuning parameters to perform cross-validation over |

`cv.seed` |
Intial seed set for random splitting of data into K folds |

### Value

`cv.res` |
Matrix containing sequence of tuning parameters and corresponding cross-validation sNB |

`cv.param` |
Value of tuning parameter selected via cross validation |

`cv.full` |
Matrix of cross-validation results for all folds |

### Note

Note this function does not split data into training and validaion set, but performs the K-fold cross-validation procedure on all data included. We advise that a separate, validation subset should be split from the data used in this function.

### Author(s)

Anu Mishra

### References

Mishra, A. (2019). Methods for Risk Markers that Incorporate Clinical Utility (Doctoral dissertation). (Available Upon Request)

### See Also

`calWt`

,
`RAWgrid`

,
`nb`

,
`cvRepWtTuning`

*ClinicalUtilityRecal*version 0.1.0 Index]