R: Kernel Estimation for ROC Curves

kROC {sROC} | R Documentation

## Kernel Estimation for ROC Curves

### 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 wangx6@ccf.org

### 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)

[Package sROC version 0.1-2 Index]