cvAUC (version 1.1.0)

ci.cvAUC: Confidence Intervals for Cross-validated Area Under the ROC Curve (AUC) Estimates

Description

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

Usage

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

Arguments

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 predictions.

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 predictions and labels, or a list of length V (for V-fold cross-validation) of vectors of indexes for the observations contained in each fold. The folds argument must only be specified if the predictions and labels arguments are vectors.

confidence

A number between 0 and 1 that represents confidence level.

Value

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.

Details

See the documentation for the prediction function in the ROCR package for details on the predictions, labels and label.ordering arguments.

References

LeDell, Erin; Petersen, Maya L.; and van der Laan, Mark J., ``Computationally Efficient Confidence Intervals for Cross-validated Area Under the ROC Curve Estimates.'' (December 2012). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 304. http://biostats.bepress.com/ucbbiostat/paper304

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.

See Also

prediction, performance, cvAUC, ci.pooled.cvAUC

Examples

# NOT RUN {
# 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
# }