K-Nearest Neighbors Classifier

Build a K-Nearest Neighbors classifier using internal cross validation to choose the turning parameter, with a 5-fold cross validation as default.

Usage

knn.intcv(kfold = 5, X, y, seed)

Arguments

kfold

number of folds. By default, kfold = 5.

X

dataset to be trained. This dataset must have rows as probes and columns as samples.

y

a vector of sample group of each sample for the dataset to be trained. It must have an equal length to the number of samples in X.

seed

an integer used to initialize a pseudorandom number generator.

Value

a list of 4 elements:

mc

an internal misclassification error rate

time

the processing time of performing internal validation with kNN

model

a kNN classifier

References

https://topepo.github.io/caret/

Examples

set.seed(101)
biological.effect <- estimate.biological.effect(uhdata = uhdata.pl)
ctrl.genes <- unique(rownames(uhdata.pl))[grep("NC", unique(rownames(uhdata.pl)))]
biological.effect.nc <- biological.effect[!rownames(biological.effect)
  %in% ctrl.genes, ]
group.id <- substr(colnames(biological.effect.nc), 7, 7)

biological.effect.train.ind <- colnames(biological.effect.nc)[c(sample(which(
  group.id == "E"), size = 64),
  sample(which(group.id == "V"), size = 64))]
biological.effect.nc.tr <- biological.effect.nc[, biological.effect.train.ind]

knn.int <- knn.intcv(X = biological.effect.nc.tr,
                     y = substr(colnames(biological.effect.nc.tr), 7, 7),
                     kfold = 5, seed = 1)
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 4.1.3
#> Loading required package: lattice