kROC: Kernel Estimation for ROC Curves

In sROC: Nonparametric Smooth ROC Curves for Continuous Data

Description

To compute the nonparametric kernel estimate of receiver operating characteristic (ROC) Curves for continuous data.

Usage

kROC(x, y, bw.x="pi_ucv", bw.y="pi_ucv", adjust=1, kernel=c("normal", "epanechnikov"), xgrid, 
	ngrid=256, from, to, cut=3, na.rm = FALSE, ...)

Arguments

x	numeric vector.
y	numeric vector.
bw.x	the smoothing bandwidth of x to be used. bw can also be a character string giving a rule to choose the bandwidth. See bw.CDF and bw.CDF.pi. The default used the Altman and Leger's plug-in approach with an unbiased cross-validation pilot bandwidth.
bw.y	the smoothing bandwidth of y to be used.
adjust	the parameter for adjusting the bandwidth. The bandwidth used for the estimate is actually adjust*bw. By default, adjust=1.
kernel	a character string giving the smoothing kernel to be used. This must be either “normal” or “epanechnikov”. By default, the normal kernel is used.
xgrid	the user-defined data points at which the CDF is to be evaluated. If missing, the CDF will be evaluated at the equally spaced points defined within the function.
ngrid	the number of equally spaced points at which the density is to be estimated.
from	the left-most points of the grid at which the density is to be estimated.
to	the right-most points of the grid at which the density is to be estimated
cut	by default, the values of from and to are cut bandwidths beyond the extremes of the data.
na.rm	logical; if TRUE, missing values are removed from x. If FALSE any missing values cause an error.
...	further arguments for methods.

Details

estimate the nonparametric kernel estimate of receiver operating characteristic (ROC) Curves for continuous data

Value

An object of class “ROC”.

FPR	the false positive rate.
TPR	the true positive rate.
bw.x, bw.y	the bandwidths used.
nx, ny	the sample sizes after elimination of missing values.
call	the call which produced the result.
x.data.name, y.data.name	the deparsed names of the x argument.
x.has.na, y.has.na	logical; if TRUE, there are missing values in the original data.

The print method reports summary values on the x and Fhat components.

Author(s)

X.F. Wang [e-mail]

References

Lloyd, C.J. (1998). Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems. Journal of the American Statistical Association, 93(444): 1356-1364.

Zhou, X.H. and Harezlak, J. (2002). Comparison of bandwidth selection methods for kernel smoothing of ROC curves. Statistics in Medicine, 21, 2045-2055.

Zou, K.H., Hall, W.J., and Shapiro, D.E. (1997). Smooth non-parametric receiver operating characteristic (ROC) curves for continuous diagnostic tests. Statistics in medicine, 16(19): 2143-56.

See Also

bw.CDF, bw.CDF.pi.

Examples

## --------------------
set.seed(100)
n <- 200
x <- rgamma(n,2,1)
y <- rnorm(n)

xy.ROC <- kROC(x,y, bw.x="pi_sj",bw.y="pi_sj")
xy.ROC

plot(xy.ROC)

sROC documentation built on May 1, 2019, 10:24 p.m.