roclab (version 0.1.4)

auc.kroclearn: Compute AUC for a fitted kernel model

Description

Estimate the AUC (Area Under the ROC Curve) for a fitted kernel model on new data.

Usage

# S3 method for kroclearn
auc(object, newdata, y, ...)

Value

A numeric scalar giving the estimated AUC.

Arguments

object

A fitted model object of class "kroclearn" (kernel model).

newdata

A matrix or data.frame of test predictors. Must have the same structure as the training data (categorical variables are dummy-aligned automatically).

y

Response vector of test labels ({-1, 1} or convertible).

...

Not used.

Examples

set.seed(123)

n_train <- 100
r_train <- sqrt(runif(n_train, 0.05, 1))
theta_train <- runif(n_train, 0, 2*pi)
X_train <- cbind(r_train * cos(theta_train), r_train * sin(theta_train))
y_train <- ifelse(r_train < 0.5, 1, -1)

n_test <- 10
r_test <- sqrt(runif(n_test, 0.05, 1))
theta_test <- runif(n_test, 0, 2*pi)
X_test <- cbind(r_test * cos(theta_test), r_test * sin(theta_test))
y_test <- ifelse(r_test < 0.5, 1, -1)

fit <- kroclearn(X_train, y_train, lambda = 0.1,
  kernel = "radial", approx=TRUE)

auc(fit, X_test, y_test)