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Compute the area under the ROC curve

Source: R/PsFunctions.R
computePsAuc.Rd

Compute the area under the ROC curve of the propensity score.

Usage

computePsAuc(data, confidenceIntervals = FALSE, maxRows = 1e+05)

Arguments

data

A data frame with at least the two columns described below

confidenceIntervals

Compute 95 percent confidence intervals (computationally expensive for large data sets)

maxRows

Maximum number of rows to use. If the number of rows is larger, a random sample will be taken. This can increase speed, with minor cost to precision. Set to 0 to use all data.

Value

A tibble holding the AUC and its 95 percent confidence interval

Details

The data frame should have a least the following two columns:

  • treatment (integer): Column indicating whether the person is in the target (1) or comparator (0) group.

  • propensityScore (numeric): Propensity score.

Examples

treatment <- rep(0:1, each = 100)
propensityScore <- c(rnorm(100, mean = 0.4, sd = 0.25), rnorm(100, mean = 0.6, sd = 0.25))
data <- data.frame(treatment = treatment, propensityScore = propensityScore)
data <- data[data$propensityScore > 0 & data$propensityScore < 1, ]
computePsAuc(data)
#> [1] 0.6716202