cvAUC (version 1.1.4)

ci.pooled.cvAUC: Confidence Intervals for Cross-validated Area Under the ROC Curve (AUC) Estimates for Pooled Repeated Measures Data

Description

This function calculates influence curve based confidence intervals for cross-validated area under the curve (AUC) estimates, for a pooled repeated measures data set.

Usage

ci.pooled.cvAUC(predictions, labels, label.ordering = NULL, 
  folds = NULL, ids, 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.

ids

A vector, matrix, list, or data frame containing cluster or entity ids. All observations from the same entity (i.e. patient) that have been pooled must have the same id. Must have the same dimensions as 'predictions'.

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.

In pooled repeated measures data, the clusters (not the individual observations) are the independent units. Each observation has a corresponding binary outcome. This data structure arises often in clinical studies where each patient is measured, and an outcome is recorded, at various time points. Then the observations from all patients are pooled together. See the Examples section below for more information.

References

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.

See Also

prediction, performance, cvAUC, ci.cvAUC

Examples

# NOT RUN {
# This example is similar to the ci.cvAUC example, with the excpection that
# this is a pooled repeated measures data set.  The example uses simulated
# data that contains multiple time point observations for 500 patients, 
# each observation having a binary outcome.
# 
# The cross-validation folds are stratified by ids that have at least one 
# positive outcome.  All observations belonging to one patient are
# contained within the save CV fold.


pooled_example <- function(data, ids, V = 10){

  .cvFolds <- function(Y, V, ids){
    #Stratify by outcome & id
    classes <- tapply(1:length(Y), INDEX = Y, FUN = split, 1)
    ids.Y1 <- unique(ids[classes$`1`])  #ids that contain an observation with Y==1
    ids.noY1 <- setdiff(unique(ids), ids.Y1)  #ids that have no Y==1 obvervations
    ids.Y1.split <- split(sample(length(ids.Y1)), rep(1:V, length = length(ids.Y1)))
    ids.noY1.split <- split(sample(length(ids.noY1)), rep(1:V, length = length(ids.noY1)))  
    folds <- vector("list", V)
    for (v in seq(V)){
      idx.Y1 <- which(ids %in% ids.Y1[ids.Y1.split[[v]]])
      idx.noY1 <- which(ids %in% ids.noY1[ids.noY1.split[[v]]])
      folds[[v]] <- c(idx.Y1, idx.noY1)
    }
    return(folds)
  }
  .doFit <- function(v, folds, data){  #Train/test glm for each fold
    fit <- glm(Y~., data = data[-folds[[v]],], family = binomial)
    pred <- predict(fit, newdata = data[folds[[v]],], type = "response")
    return(pred)
  }
  folds <- .cvFolds(Y = data$Y, ids = ids, V = V)  #Create folds
  predictions <- unlist(sapply(seq(V), .doFit, folds = folds, data = data))  #CV train/predict
  predictions[unlist(folds)] <- predictions  #Re-order fold indices
  out <- ci.pooled.cvAUC(predictions = predictions, labels = data$Y, 
                         folds = folds, ids = ids, confidence = 0.95)
  return(out)
}


# Load data
library(cvAUC)
data(adherence)


# Get performance
set.seed(1)
out <- pooled_example(data = subset(adherence, select=-c(id)), 
                      ids = adherence$id, V = 10)


# The output is given as follows:
# > out
# $cvAUC
# [1] 0.8648046
#
# $se
# [1] 0.01551888
#
# $ci
# [1] 0.8343882 0.8952211
#
# $confidence
# [1] 0.95


# }