Distance to centroid classifier function

Given an \(n x m\) matrix of centroids, where \(m\) are the prototypic centroids with \(n\) features, classify new samples according to the distance to the centroids.

classify(data, centroid, method = "pearson")

Arguments

data	a data.frame of dimensions \(n x p\) with the samples to classify, were \(n\) are the same set of features as in the centroids
centroid	a data.frame of dimensions \(n x m\), where each column is a prototypic centroid to classify the samples
method	Character string indicating which method to use to calculate distance to centroid. Options are "pearson" (default), "kendall", or "spearman"

Value

Returns a numeric vector of length \(p\) with the class assigned to each sample according to the shortest distance to centroid

Examples

if (FALSE) {
rna_luad<-use_rna_luad()

#The expression of the toy datasets are already scaled
prm <- rna_luad$TCGA$expression_matrix

#We change the rownames to be gene Symbol insted of Gene Id.
rownames(prm)<- rna_luad$TCGA$feature_data$gene

#Wilkerson's centroids
centroids<- rna_luad$WilkCentroids

#Extract features from both data.frames
inBoth<- Reduce(intersect, list(rownames(prm),rownames(centroids)))

#Classify samples
Wilk.Class<- classify(prm[inBoth,],centroids[inBoth,])
table(Wilk.Class)
}