boot.adjroc {adjROC} | R Documentation |

## boot.adjroc

### Description

computes bootstrap adjusted sensitivity, bootstrap adjusted specificity, or bootstrap crossing point between sensitivity and specificity for different thresholds

### Usage

```
boot.adjroc(
score,
class,
n = 100,
method = "emp",
sensitivity = NULL,
specificity = NULL
)
```

### Arguments

`score` |
A numeric array of diagnostic score i.e. the estimated probability of each diagnosis |

`class` |
A numeric array of equal length of |

`n` |
number of bootstrap samples. |

`method` |
Specifies the method for estimating the ROC curve. Three methods are supported, which are |

`sensitivity` |
numeric. Specify the threshold of sensitivity. |

`specificity` |
numeric. Specify the threshold of specificity. |

### Value

list including mean and CI of bootstrap value (sensitivity, specificity, or the crossing point) and the bootstrap data.

### Examples

```
# random classification and probability score
score <- runif(10000, min=0, max=1)
class <- sample(x = c(1,0), 10000, replace=TRUE)
# calculate adjusted sensitivity, when specificity threshold is 0.90:
adjroc(score = score, class = class, specificity = 0.9, plot = TRUE)
# calculate adjusted specificity, when sensitivity threshold equals 0.9
boot.adjroc(score = score, class = class, n = 100, sensitivity = 0.9)
# calculate the bootstrap meeting point between sensitivity and specificity
boot.adjroc(score = score, class = class, n = 100)
```

[Package

*adjROC*version 0.3 Index]