Classification to Nearest Centroids Classifier — clanc.intcv • PRECISION.array

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)
#> CV:12345