clanc.intcv: Classification to Nearest Centroids Classifier

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

Classification to Nearest Centroids Classifier

Description

Build a Classification to Nearest Centroids classifier on the objective data.

Usage

clanc.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 ClaNC
model	a ClaNC classifier, resulted from cv.fit

References

Alan R. Dabney, Author Notes.(2005) ClaNC: point-and-click software for classifying microarrays to nearest centroids, https://academic.oup.com/bioinformatics/article/22/1/122/219377

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]

clanc.int <- clanc.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.