knn.intcv | R Documentation |
Build a K-Nearest Neighbors classifier using internal cross validation to choose the turning parameter, with a 5-fold cross validation as default.
knn.intcv(kfold = 5, X, y, seed)
kfold |
number of folds. By default, |
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 |
seed |
an integer used to initialize a pseudorandom number generator. |
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 |
https://topepo.github.io/caret/
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)
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