machine_learning: Calculate the distance of points from the center of a cluster

In quickcode: Quick and Essential 'R' Tricks for Better Scripts

Calculate the distance of points from the center of a cluster

Description

This function operates on multivariate data and calculates the distance of points from the centroid of one or more clusters.

Usage

pairDist(data, round)

Arguments

data	data frame object or a matrix/array object
round	round result to decimal place

Value

a named vector consisting of a row number and a pair-distance value

Function utility

Used to generate the computations needed to model pair-distance measures in three dimensions

More information about this function

The pairDist function is used to quantify how far each data point (row) is from the overall mean across all columns. It’s commonly used in multivariate statistics, machine learning, and data analysis to assess the variability or similarity of data points relative to their mean. More specifically, the function is used in outlier detection and cluster analysis to evaluate the dispersion of data. Used in conjunction with other calculations, pairDist output can also be used to model data in three dimensions.

References

the current function was adapted from one of the examples in the svgViewR package,
https://cran.r-project.org/web/packages/svgViewR/svgViewR.pdf

Examples

data = attenu[,1:2]

# example 1: basic example using data.frame
pairDist(data)

# example 2: basic example using as.matrix
pairDist(as.matrix(data))

# example 3: round results to 2 decimal points
pairDist(data, 2)

# example 4
data = matrix(
c(1, 5, NA, 4, 5, 6, NA, 8, 9, NA, 12, 23, 6, 0, 3, 3, 8, 15),
ncol = 3,
byrow = TRUE)
x = pairDist(data, 3)
data = data.frame(data)
data = cbind(data, x)

quickcode documentation built on April 11, 2025, 5:49 p.m.