knn.intcv: K-Nearest Neighbors Classifier

In yilinwu123/precision1: PaiREd miCrorna sImulation on Study desIgn for mOlecular classificatioN

K-Nearest Neighbors Classifier

Description

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)

yilinwu123/precision1 documentation built on June 28, 2022, 2:53 a.m.