CVknn {bcROCsurface}R Documentation

Cross-validation for K nearest-neighbor regression


This function calculates the estimated cross-validation prediction error for K nearest-neighbor regression and returns a suitable choice for K.


CVknn(X, Dvec, V, K.list = NULL, type = "eucli", plot = FALSE)



a numeric design matrix, which used in rhoKNN to estimate probabilities of the disease status.


a n * 3 binary matrix with three columns, corresponding to the three classes of the disease status. In row i, 1 in column j indicates that the i-th subject belongs to class j, with j = 1, 2, 3. A row of NA values indicates a non-verified subject.


a binary vector containing the verification status (1 verified, 0 not verified).


a list of candidate values for K. If NULL(the default), the set {1, 2, ..., n.ver} is employed, where, n.ver is the number of verified subjects.


a type of distance, see rhoKNN for more details. Default "eucli".


if TRUE, a plot of cross-validation prediction error is produced.


Data are divided into two groups, the first contains the data corresponding to V = 1, whereas the second contains the data corresponding to V = 0. In the first group, the discrepancy between the true disease status and the KNN estimates of the probabilities of the disease status is computed by varying K from 1 to the number of verification subjects, see To Duc et al. (2016). The optimal value of K is the value that corresponds to the smallest value of the discrepancy.


A suitable choice for K is returned.


To Duc, K., Chiogna, M., Adimari, G. (2016): Nonparametric Estimation of ROC Surfaces Under Verification Bias. Submitted.


XX <- cbind(EOC$CA125, EOC$CA153, EOC$Age)
Dna <- preDATA(EOC$D, EOC$CA125) <- Dna$Dvec
CVknn(XX,, EOC$V, type = "mahala", plot = TRUE)

[Package bcROCsurface version 1.0-4 Index]