View source: R/cross-validation-KNN.R
cv_knn | R Documentation |
This function calculates the estimated cross-validation prediction error for K nearest-neighbor regression and returns a suitable choice for K.
cv_knn(x_mat, dise_vec, veri_stat, k_list = NULL, type = "eucli", plot = FALSE)
x_mat |
a numeric design matrix, which used in |
dise_vec |
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 |
veri_stat |
a binary vector containing the verification status (1 verified, 0 not verified). |
k_list |
a list of candidate values for K. If |
type |
a type of distance, see |
plot |
if |
Data are divided into two groups, the first contains the data corresponding to veri_stat = 1, whereas the second contains the data corresponding to veri_stat = 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. (2020). 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. and Adimari, G. (2020) Nonparametric estimation of ROC surfaces in presence of verification bias. REVSTAT-Statistical Journal. 18, 5, 697–720.
data(EOC)
x_mat <- cbind(EOC$CA125, EOC$CA153, EOC$Age)
dise_na <- pre_data(EOC$D, EOC$CA125)
dise_vec_na <- dise_na$dise_vec
cv_knn(x_mat, dise_vec_na, EOC$V, type = "mahala", plot = TRUE)
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