auc {BuyseTest} | R Documentation |

## Estimation of the Area Under the ROC Curve (EXPERIMENTAL)

### Description

Estimation of the Area Under the ROC curve, possibly after cross validation, to assess the discriminant ability of a biomarker regarding a disease status.

### Usage

```
auc(
labels,
predictions,
fold = NULL,
observation = NULL,
direction = ">",
add.halfNeutral = TRUE,
null = 0.5,
conf.level = 0.95,
transformation = TRUE,
order.Hprojection = 2,
pooling = "mean"
)
```

### Arguments

`labels` |
[integer/character vector] the disease status (should only take two different values). |

`predictions` |
[numeric vector] A vector with the same length as |

`fold` |
[character/integer vector] If using cross validation, the index of the fold.
Should have the same length as |

`observation` |
[integer vector] If using cross validation, the index of the corresponding observation in the original dataset. Necessary to compute the standard error when using cross validation. |

`direction` |
[character] |

`add.halfNeutral` |
[logical] should half of the neutral score be added to the favorable and unfavorable scores? Useful to match the usual definition of the AUC in presence of ties. |

`null` |
[numeric, 0-1] the value against which the AUC should be compared when computing the p-value. |

`conf.level` |
[numeric, 0-1] the confidence level of the confidence intervals. |

`transformation` |
[logical] should a log-log transformation be used when computing the confidence intervals and the p-value. |

`order.Hprojection` |
[1,2] the order of the H-projection used to linear the statistic when computing the standard error.
2 is involves more calculations but is more accurate in small samples. Only active when the |

`pooling` |
[character] method used to compute the global AUC from the fold-specific AUC: either an empirical average |

### Details

The iid decomposition of the AUC is based on a first order decomposition. So its squared value will not exactly match the square of the standard error estimated with a second order H-projection.

### Value

An S3 object of class `BuyseTestAUC`

that inherits from data.frame.
The last line of the object contains the global AUC value with its standard error.

### References

Erin LeDell, Maya Petersen, and Mark van der Laan (2015). **Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates**. *Electron J Stat.* 9(1):1583–1607.

### Examples

```
library(data.table)
n <- 200
set.seed(10)
X <- rnorm(n)
dt <- data.table(Y = as.factor(rbinom(n, size = 1, prob = 1/(1+exp(1/2-X)))),
X = X,
fold = unlist(lapply(1:10,function(iL){rep(iL,n/10)})))
## compute auc
auc(labels = dt$Y, predictions = dt$X, direction = ">")
## compute auc after 10-fold cross-validation
auc(labels = dt$Y, prediction = dt$X, fold = dt$fold, observation = 1:NROW(dt))
```

*BuyseTest*version 3.0.4 Index]