cluster_classify {GSgalgoR} | R Documentation

## Distance to centroid classifier function

### Description

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.

### Usage

    cluster_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

    # load example dataset
    require(iC10TrainingData)
    require(pamr)

    data(train.Exp)
    data(IntClustMemb)
    TrainData <- list(x = train.Exp, y = IntClustMemb)

    # Create prototypic centroids
    pam <- pamr.train(TrainData)
    centroids <- pam$centroids

    Class <- cluster_classify(train.Exp, centroids)
    table(Class, IntClustMemb)

[Package GSgalgoR version 1.2.1 Index]