ci.cvAUC {cvAUC} | R Documentation |

This function calculates influence curve based confidence intervals for cross-validated area under the ROC curve (AUC) estimates.

```
ci.cvAUC(predictions, labels, label.ordering = NULL, folds = NULL, confidence = 0.95)
```

`predictions` |
A vector, matrix, list, or data frame containing the predictions. |

`labels` |
A vector, matrix, list, or data frame containing the true class labels. Must have the same dimensions as |

`label.ordering` |
The default ordering of the classes can be changed by supplying a vector containing the negative and the positive class label (negative label first, positive label second). |

`folds` |
If specified, this must be a vector of fold ids equal in length to |

`confidence` |
A number between 0 and 1 that represents confidence level. |

See the documentation for the `prediction`

function in the ROCR package for details on the `predictions`

, `labels`

and `label.ordering`

arguments.

A list containing the following named elements:

`cvAUC` |
Cross-validated area under the curve estimate. |

`se` |
Standard error. |

`ci` |
A vector of length two containing the upper and lower bounds for the confidence interval. |

`confidence` |
A number between 0 and 1 representing the confidence. |

Erin LeDell oss@ledell.org

Maya Petersen mayaliv@berkeley.edu

Mark van der Laan laan@berkeley.edu

LeDell, Erin; Petersen, Maya; van der Laan, Mark. Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electron. J. Statist. 9 (2015), no. 1, 1583–1607. doi:10.1214/15-EJS1035. http://projecteuclid.org/euclid.ejs/1437742107.

M. J. van der Laan and S. Rose. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Series in Statistics. Springer, first edition, 2011.

Tobias Sing, Oliver Sander, Niko Beerenwinkel, and Thomas Lengauer. ROCR: Visualizing classifier performance in R. Bioinformatics, 21(20):3940-3941, 2005.

`prediction`

, `performance`

,
`cvAUC`

, `ci.pooled.cvAUC`

```
# This i.i.d. data example does the following:
# Load a data set with a binary outcome. For the i.i.d. case we use a simulated data set of
# 500 observations, included with the package, of graduate admissions data.
#
# Divide the indices randomly into 10 folds, stratifying by outcome. Stratification is not
# necessary, but is commonly performed in order to create validation folds with similar
# distributions. Store this information in a list called folds.
#
# Define a function to fit a model on the training data and to generate predicted values
# for the observations in the validation fold, for a single iteration of the cross-validation
# procedure. We use a logistic regression fit.
#
# Apply this function across all folds to generate predicted values for each validation fold.
# The concatenated version of these predicted values is stored in vector called predictions.
# The outcome vector, Y, is the labels argument.
iid_example <- function(data, V = 10){
.cvFolds <- function(Y, V){ #Create CV folds (stratify by outcome)
Y0 <- split(sample(which(Y==0)), rep(1:V, length = length(which(Y==0))))
Y1 <- split(sample(which(Y==1)), rep(1:V, length = length(which(Y==1))))
folds <- vector("list", length=V)
for (v in seq(V)) {folds[[v]] <- c(Y0[[v]], Y1[[v]])}
return(folds)
}
.doFit <- function(v, folds, data){ #Train/test glm for each fold
fit <- glm(Y~., data = data[-folds[[v]],], family = binomial)
pred <- predict(fit, newdata = data[folds[[v]],], type = "response")
return(pred)
}
folds <- .cvFolds(Y = data$Y, V = V) #Create folds
predictions <- unlist(sapply(seq(V), .doFit, folds = folds, data = data)) #CV train/predict
predictions[unlist(folds)] <- predictions #Re-order pred values
# Get CV AUC and confidence interval
out <- ci.cvAUC(predictions = predictions, labels = data$Y,
folds = folds, confidence = 0.95)
return(out)
}
# Load data
library(cvAUC)
data(admissions)
# Get performance
set.seed(1)
out <- iid_example(data = admissions, V = 10)
# The output is given as follows:
# > out
# $cvAUC
# [1] 0.9046473
#
# $se
# [1] 0.01620238
#
# $ci
# [1] 0.8728913 0.9364034
#
# $confidence
# [1] 0.95
```

[Package *cvAUC* version 1.1.4 Index]