auc.nonpara.kernel {auRoc} | R Documentation |
Obtain the point estimate and the confidence interval of the AUC using kernel methods.
auc.nonpara.kernel(x, y, conf.level=0.95, integration=c("FALSE","TRUE"), bw=c("nrd0", "sj"), nint=512, method=c("mw", "jackknife", "bootstrapP", "bootstrapBCa"), nboot)
x |
a vector of observations from class P. |
y |
a vector of observations from class N. |
conf.level |
confidence level of the interval. The default is 0.95. |
integration |
a logical value. If its value is |
bw |
method used for bandwidth selection. |
nint |
the number of equally spaced points at which the density is to be estimated. The default if 512. |
method |
a method used to construct the CI. |
nboot |
number of bootstrap iterations. |
The AUC essentially depends on the CDFs of two classes N and P. We could use kernel smoothing methods to obtain the CDFs. The methods implemented in this function construct the CI based on two different strategies: the first uses kernel smoothing to estimate the PDFs and then the CDFs; and the second starts from the estimate of the CDFs directly. Gaussian kernel is used.
Point estimate and lower and upper bounds of the CI of the AUC.
The observations from class P tend to have larger values then that from class N.
Dai Feng
Dai Feng, Giuliana Cortese, and Richard Baumgartner (2015) A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size. Statistical Methods in Medical Research DOI: 10.1177/0962280215602040
Simon Sheather and Michael Jones (1991) A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society. Series B (Methodological) 53 683-690
#Example 1 data(petBrainGlioma) y <- subset(petBrainGlioma, grade==1, select="FDG", drop=TRUE) x <- subset(petBrainGlioma, grade==2, select="FDG", drop=TRUE) auc.nonpara.kernel(x, y) ## Not run: #Example 2 data(petBrainGlioma) y <- subset(petBrainGlioma, grade==1, select="ACE", drop=TRUE) x <- subset(petBrainGlioma, grade==2, select="ACE", drop=TRUE) auc.nonpara.kernel(x, y, integration="TRUE", bw="sj", method="bootstrapBCa", nboot=999) ## End(Not run)