K-Nearest Neighbours imputation — impute_knn • diceR

The non-missing cases indicate the training set, and missing cases indicate the test set.

Usage

impute_knn(x, data, seed = 123456)

Arguments

x: clustering object
data: data matrix
seed: random seed for knn imputation reproducibility

Value

An object with (potentially not all) missing values imputed with K-Nearest Neighbours.

Note

We consider 5 nearest neighbours and the minimum vote for definite decision is 3.

See also

Other imputation functions: impute_missing()

Author

Aline Talhouk

Examples

data(hgsc)
dat <- hgsc[1:100, 1:50]
x <- consensus_cluster(dat, nk = 4, reps = 4, algorithms = c("km", "hc",
"diana"), progress = FALSE)
x <- apply(x, 2:4, impute_knn, data = dat, seed = 1)